Open Access Original
Article

DOI: 10.7759/cureus.57860

Increased Age-Adjusted Cancer Mortality Afterthe Third mRNA-Lipid Nanoparticle Vaccine Dose
During the COVID-19 Pandemic in Japan

Review began 10/19/2023
Review ended 04/01/2024

Miki Gibo 1, Seiji Kojima 2, Akinori Fujisawa 3, Takayuki Kikuchi 4, Masanori Fukushima 4

Published 04/08/2024© Copyright 2024
Gibo et al. This is an open access article
distributed under the terms of the Creative
Commons Attribution License CC-BY 4.0.,

1. Primary Health Care, Matsubara Clinic, Kochi, JPN 2. Pediatrics, Nagoya Pediatric Cancer Fund, Nagoya, JPN 3.
Cardiovascular Medicine, Honbetsu Cardiovascular Medicine Clinic, Honbetsu, JPN 4. Translational Research & Health
Data Science, Learning Health Society Institute, Nagoya, JPN

which permits unrestricted use, distribution,and reproduction in any medium, provided
the original author and source are credited.

Corresponding author: Miki Gibo, gibo.578@gmail.com

Abstract
During the COVID-19 pandemic, excess deaths including cancer have become a concern in Japan, which has
a rapidly aging population. Thus, this study aimed to evaluate how age-adjusted mortality rates (AMRs) for
different types of cancer in Japan changed during the COVID-19 pandemic (2020-2022). Official statistics
from Japan were used to compare observed annual and monthly AMRs with predicted rates based on prepandemic (2010-2019) figures using logistic regression analysis. No significant excess mortality wasobserved during the first year of the pandemic (2020). However, some excess cancer mortalities wereobserved in 2021 after mass vaccination with the first and second vaccine doses, and significant excess
mortalities were observed for all cancers and some specific types of cancer (including ovarian cancer,
leukemia, prostate cancer, lip/oral/pharyngeal cancer, pancreatic cancer, and breast cancer) after massvaccination with the third dose in 2022. AMRs for the four cancers with the most deaths (lung, colorectal,
stomach, and liver) showed a decreasing trend until the first year of the pandemic in 2020, but the rate ofdecrease slowed in 2021 and 2022. This study discusses possible explanations for these increases in ageadjusted cancer mortality rates.

Categories: Preventive Medicine, Epidemiology/Public Health, Oncology
Keywords: breast cancer, prostatic carcinoma, pancreas tumors, oral cancers, leukemia , ovarian cancers, excess
mortality, covid-19, sars-cov-2 mrna vaccine, age-adjusted mortality rate

Introduction
The COVID-19 pandemic began in December 2019 in Wuhan, China, and was first detected in Japan in
January 2020. In response, a range of healthcare and socio-economic restrictions were implemented to curb
the spread of the disease. Since February 2021, the mRNA-lipid nanoparticle (mRNA-LNP) vaccine has been
available for emergency use and is recommended for all individuals aged six months and older, especiallythose at high risk. As of March 2023, 80% of the Japanese population had received their first and second
doses, 68% had received their third dose, and 45% had received their fourth dose [1]. Despite these national
measures, 33.8 million people had been infected, and 74,500 deaths had been attributed to COVID-19 inJapan by the end of April 2023. Additionally, excess deaths from causes other than COVID-19 have beenreported in various countries [2-6], including deaths from cancer [7-10], and Japan is no exception [11,12].
Cancer is the leading cause of death in Japan, accounting for one-fourth of all deaths. Therefore, it isessential to understand the effects of the pandemic on mortality rates of cancer from 2020 to 2022. Age
adjustment is necessary for accurate evaluation, especially in diseases such as cancer that tend to occur
in elderly adults. Japan has several characteristics that make it ideal for analyzing the impact of thepandemic on cancer mortality rates, including its large population of 123 million, availability of official
statistics, and the high 80% accuracy rate of death certificates according to autopsy studies [13].

Materials And Methods
Statistical data
The data used in this analysis are all publicly available national data. Death figures were obtained from theVital Statistics [14], which include monthly and annual deaths by cause, sex, and age (five-year age
groups). Cancer deaths are divided into 20 subclassifications. Only Japanese individuals living in Japan are
included. Population estimates by age group required for the age-adjusted analysis were also obtained fromthe national data [15]. The number of people with confirmed COVID-19 infection was obtained from the
Ministry of Health, Labour and Welfare website [16]. Vaccination rates by age group were obtained from thewebsites of the Prime Minister's Office and the Ministry of Health, Labor and Welfare [1,17].

Age-specific mortality rate (ASMR)
Annual crude numbers of deaths were grouped by 10-year age groups for the 0-39 age range, which had
fewer deaths, and in five-year age groups for the 40-89 age range, which had more deaths. However, due to

How to cite this article
Gibo M, Kojima S, Fujisawa A, et al. (April 08, 2024) Increased Age-Adjusted Cancer Mortality After the Third mRNA-Lipid Nanoparticle Vaccine
Dose During the COVID-19 Pandemic in Japan. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

the small sample size, ages 90 and older were combined into one group.
ASMR (per 100,000 people)
d i=crude number of deaths in that age group, p i=number in that age group in the observed population

Age adjustment by direct standardization
Because ASMRs are too detailed to provide an overview of mortality for all cancers, we used the ageadjusted mortality rate (AMR) from direct standardization as a summarized indicator. For comparisons ofmortality rates over time, as in our study, all data are from the Japanese population as a whole, withcomparably large numbers and age composition. The specific death rates per age group are known, so direct
standardization is appropriate [18]. The Ministry of Health, Labour and Welfare in Japan reportedly uses
direct standardization with smoothed standard population data from 2015 (125.32 million) [19], and thesame approach was used in this study. The formulas for the calculation are as follows:
age-adjusted number of deaths
age-adjusted mortality rate(AMR) (per 100,000 people
i=age group, di=number of deaths in that age group, pi=number in that age group in the observed
population, psi=number in that age group in the standard population.
Age adjustment for sex-specific cancers was performed using the “sex-specific smoothed standard
population dataset 1” [20]. In leap years, the deaths had occurred in 366 days, so the age-adjusted number ofannual deaths and AMR were multiplied by 365/366 to correct. The age-adjusted number and rate of deathsin February in leap years were also corrected for monthly analysis.

Excess mortality during the COVID-19 pandemic
Excess mortality in this study was defined as follows:
Excess number of deaths
Do=observed number of deaths, Dp=predicted number of deaths in the corresponding year or month
Excess mortality (%)
Ro=observed rate, Rp=predicted rate in the corresponding year or month (rate is ASMR or AMR)
The predicted rates based on the 2010-2019 period before the COVID-19 pandemic were calculatedusing logistic regression analysis [5]. The predicted AMRs for each month were also calculated using datafrom the corresponding month in 2010-2019. R (version 4.3.1; R Development Core Team, Vienna, Austria)
was used for statistical analysis.
The confidence intervals (CIs) and prediction intervals (PIs) around the predicted rates were calculated bylogit-transformed values using the following formulas and then transformed inversely.
residual variance (σ 2)standard error (SE i) for logit-transformed confidence interval (l-CI)

standard error (SE i) for logit-transformed prediction interval (l-PI)
logit-transformed CI (l-CI) or PI (l-PI) = Vpi ± t n-p-1(probability)
Voi =logit-transformed ASMR or AMR, Vp i=logit-transformed predicted ASMR or AMR, n =number ofobservations (here, it is 10; from 2010 to 2019), p =number of explanatory variables (here, it is 1), x i =current
year (here, it is one of 2010,2011,,2022),, tn-p-1 (probability) =t value at the degree of

freedom (n-p-1), and the probability of interest

Results

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

2 of 18

Mortality from all causes and all cancers
Table 1 shows the numbers of crude, age-adjusted, and excess deaths for all causes, all cancers, and eachcancer type with the excess mortality rates during the pandemic in 2020, 2021, and 2022. Each cancer type islisted in decreasing order of number of deaths in 2022. The annual numbers of age-adjusted deaths in 2020,
2021, and 2022 during the pandemic were 1,206,126, 1,244,976, and 1,320,768 from all causes, and 345,248,
345,625, and 344,114 from all cancers, respectively. In 2020, the first year of the pandemic, there
was significant deficit mortality for all causes (< 99% lower PI) and no excess mortality for all cancers.
However, in 2021, there was significant excess mortality of 2.1% (>99% upper PI) for all causes and 1.1%(>95% upper PI) for all cancers. In 2022, the excesses increased to 9.6% (>99% upper PI) for all causes and2.1% (>99% upper PI) for all cancers. In 2022, the number of excess deaths was 115,799 (95%CI: 106,018,
125,501) for all causes and 7,162 (95%CI: 4,786, 9,522) for all cancers. Among the 20 subclassifications, thefive cancers with the most deaths (lung, colorectal, stomach, pancreatic, and liver cancer) accounted for 61%
of deaths from all cancers. The ranking of the number of deaths for each cancer type was nearly
unchanged from 2020 to 2022.

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

3 of 18

Crude number of deaths

Excess number of deaths

Excess mortality (age-(age-adjusted)adjusted)

Age-adjusted number of deaths

Cause of death and ICD-10 codes
2020

2021

2022

2020

2021

2022

2020

2021

2022

1,372,648

1,439,809

1,568,961

1,206,126

1,244,976

1,320,768-28,126

25,453

115,799

2020

2021

2022

2.1%⁑

9.6%⁑

All-cause of deaths

2.3%‡

Malignant neoplasms C00-C97

378,356

381,497385,787345,248345,625

344,114-1,379

3,870

7,162

-0.4%

1.1%*

2.1%⁑

Malignant neoplasm of trachea, bronchus and lung C33–C34

75,581

76,212

76,66468,721

68,832

68,292

-352

672

1,033

-0.5%

1.0%

1.5%

Malignant neoplasm of colon, sigmoid, and rectum C18-C20

51,78452,41653,08847,30347,49847,338

-859

-380

-259

-1.8%

-0.8%

-0.5%

Malignant neoplasm of stomach C16

42,31841,62440,71138,38837,45835,940

-199

366

286

-0.5%

1.0%

0.8%

Malignant neoplasm of pancreas C25

37,674

38,57839,46834,59035,24935,593

296

651

688

0.9%*

1.9%⁑

2.0%⁑

Other remaining malignant neoplasm in C00–C97

28,59228,93429,64626,02426,06526,273

-115

149

579

-0.4%

0.6%

2.3%

Malignant neoplasm of liver and intrahepatic bile ducts C22

24,83924,102

23,621

22,561

21,70820,960

-42

161

421

-0.2%

0.7%

2.0%

17,772

18,172

17,75815,81015,99015,303

-255

333

43

-1.6%

2.1%

0.3%

-

-

14,65014,803

15,911

14,08914,185

15,109

-558

-631

122
3.8%†

4.3%†

Malignant neoplasm of gallbladder and other parts of biliary tract
C23–C24

Malignant neoplasm of breast C50

0.8%

Malignant lymphoma C81–C86

13,995

13,997

14,23012,591

12,507

12,437

239

64

-98

1.9%

0.5%

-0.8%

Malignant neoplasm of prostate C61

12,75813,216

13,44010,775

10,981

10,835

131

547

604

1.2%

5.3%*

5.9%*

Malignant neoplasm of esophagus C15

10,978

10,958

10,918

10,298

10,248

10,105

-381

-226

-170

-3.6%

-2.2%

-1.7%

Leukemia C91–C95

8,983

9,120

9,758

8,280

8,397

8,868

-16

143

656

-0.2%

1.7%

8.0%*

Malignant neoplasm of bladder C67

9,166

9,443

9,597

8,060

8,196

8,114

-181

-68

-171

-2.2%

-0.8%

-2.1%

Malignant neoplasm of lip, oral cavity and pharynx C00–C14

7,826

8,000

8,429

7,257

7,364

7,636

-46

92

395

-0.6%

1.3%

5.5%*

Malignant neoplasm of uterus C53–C55

6,806

6,818

7,156

6,568

6,589

6,877

-73

-86

168

-1.1%

-1.3%

2.5%

Malignant neoplasm of ovary C56

4,875

5,081

5,182

4,732

4,928

4,989

114

347

442

2.5%

7.6%⁑

9.7%⁑

4,295

4,351

4,391

3,857

3,888

3,850

-136

-45

-22

-3.4%

-1.1%

-0.6%

2,847

3,054

3,106

2,729

2,944

2,966

-165

-46

-120

-5.7%

-1.5%

-3.9%

Malignancy of skin C43–C44

1,707

1,718

1,806

1,532

1,512

1,546

8

1

47

0.6%

0.1%

3.2%

Malignant neoplasm of larynx C32

781

795

798

714

721

707

-62

-27

-15

-8.0%

-3.6%

-2.1%

Subclassification

Other malignant neoplasms of lymphoid, hematopoietic,etc. C88–
C90, C96

Malignant neoplasm of central nervous system C70–C72, C75.1–
C75.3

TABLE 1: Observed crude, age-adjusted, and excess deaths for all causes, all cancers, and eachcancer type with excess mortality rates during the pandemic in 2020, 2021, and 2022.
Each cancer type is listed in decreasing order of the number of deaths in 2022.
Excess mortality = (observed AMR - predicted AMR) / predicted AMR * 100 (%). (Predicted AMRs based on the pre-pandemic years 2010-2019 were
calculated using logistic regression.)
Symbol ⁑ for >99%, * >95% upper, ‡<99%, †<95% lower prediction interval (PI)

Figure 1 shows the annual AMRs over time and the excess mortality rates in each month during the COVID19 pandemic (2020-2022) of all cancers. AMRs had been decreasing until 2020 (AMR 275.5/100,000
population), but this decline stopped after 2021, exceeding the 95% upper PI in 2021 (AMR
275.8/100,000) and the 99% upper PI in 2022 (AMR 274.6/100,000). As shown on the right side, the monthly
excess mortality (%) exceeded the 99% upper PI for the first time in August 2021, coinciding with the peak of
the first and second mass vaccinations and became elevated again from May 2022, two months later the2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

4 of 18

peak of the third mass vaccination, once again exceeding the 99% upper PIs for four months until December.

FIGURE 1: Age-adjusted mortality rates (AMRs) over time and excess
mortality in each month: all cancers.
(Left side) Observed age-adjusted mortality rates (AMRs) (per 100,000 population) are represented by a blue linewith marks, the predicted trend by logistic regression analysis by a dashed line, and the 95% prediction intervals(PIs) by dotted lines. Markers are highlighted in yellow for years with figures exceeding 95% of the upper PI, andpink for years with figures exceeding 99% of the upper PI. The vertical line indicates the arrival of COVID-19 inJapan. There was a decreasing trend until 2020, but the decline stopped after 2021, with figures exceeding the
95% upper PI in 2021 and the 99% upper PI (the line was not shown) in 2022.
(Right side) The horizontal axis indicates each month during the pandemic in 2020, 2021, and 2022, while thevertical axis on the left side indicates the excess mortality (%), calculated as (observed AMR − predicted AMR in
the corresponding month) / predicted AMR in the corresponding month*100. The predicted AMRs based on the
2010–2019 period preceding the COVID-19 pandemic were estimated by logistic regression analysis.
The ⁑ symbol signifies >99% upper PI, and † <95% lower PI. The vertical axis on the right side indicates thenumber of domestic vaccinations and deaths attributed to COVID-19.
Monthly excess mortalities exceeded the 99% upper PI for the first time in August 2021, coinciding with the peakof the first and second mass vaccinations, and once again exceeded the 99% upper PI for four months from May
2022, two months after the peak of the third mass vaccination.

Age-specific mortality for all cancers
Prior to the pandemic (2010-2019), crude age-specific mortality rates for all types ofcancer had decreasing trends in all age groups, except for the 90+ age group (data not shown). In 2020,
deficit mortality was observed in most age groups, except the 75-79 age group. However, this graduallyshifted to excess mortality in 2021 and escalated in 2022 in almost all age groups, except for the 65-69 and85+ groups. In the 75-79 age group, excess mortality was 3.9% (95%CI: 2.6, 5.3) in 2020, 7.9% (6.4, 9.5) in
2021, and 9.5% (7.8, 11.4) in 2022, each exceeding the 99% upper PIs. In the 80-84 age group, excess
mortality was 2.9% (1.4, 4.5) in 2022, exceeding the 95% upper PI. No statistical significance was detected in
the younger age groups, which had few deaths. The chart below shows that the number of deaths from allcancers was highest in the 80-84 age group (Figure 2). According to Table 2, more than 90% of people
aged 70 and older have received a third vaccine dose [1,17]. The Ministry of Health, Labour and Welfare
reported that more than 99.9% of formulations administered were mRNA-LNPs, with BNT162b2 accounting
for 78.1% and mRNA-1273 accounting for 21.8% [17], up to third doses with monovalent vaccine.

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

5 of 18

FIGURE 2: Excess mortality during the pandemic in 2020, 2021, and2022 (upper) and crude cancer deaths in 2022 (lower) in each age group.
Excess age-specific mortality = (observed ASMR − predicted ASMR) / predicted ASMR * 100 (%). Predicted
ASMRs based on the 2010-2019 period preceding the COVID-19 pandemic were calculated using logistic
regression. The ⁑ symbol signifies >99% upper PI, * >95% upper PI, and †<95% lower PI. 2020, deficit mortality
was observed in most age groups except for the 75–79 age group. However, mortality increased gradually in 2021
and more remarkably in 2022 in almost all age groups except for the 65–69 and 85+ age groups. In the 75–79 age
group, excess mortality was 3.9% (95%CI: 2.6, 5.3) in 2020, 7.9% (95%CI: 6.4, 9.5) in 2021, and 9.5% (95%CI:
7.8, 11.4) in 2022, exceeding the 99% upper PIs. In the 80–84 age group, excess mortality was 2.9% (1.4, 4.5) in
2022, exceeding the 95% upper PI. No statistical significance was detected in the younger age groups, which had
few deaths. The chart below shows that the number of deaths from all cancers was highest in the 80–84 age
group.

Age group (year old)

6 month-4 ※

5-11

12-19

20-29

30-39

40-49

50-59

60-69

70-79

80-89

90-

First and second doses

3.1%

16.2%

67.4%

80.2%

80.5%

82.9%

90.7%

91.5%

93.7%

97.7%

99.3%

Third dose

0.9%

8.1%

42.8%

56.1%

58.8%

65.0%

79.0%

86.5%

91.1%

95.1%

96.0%

TABLE 2: Vaccination rate by age group as of March 2023.
More than 90% of people aged 70 and older have received a third vaccine dose.

Mortality by cancer typeFigure 3 illustrates excess mortality for each type of cancer. In 2020, only pancreatic cancer slightly
exceeded the 95% upper PI. However, statistically significant excess mortality was observed for three out of
20 types of cancers in 2021, and five out of 20 in 2022. The types were ovarian cancer, leukemia, prostatecancer, lip/oral/pharyngeal cancer, and pancreatic cancer, in descending order in 2022. AMRs exceeded thepredicted value by 7.6% (95%CI: 5.6, 9.5) in 2021 and 9.7% (7.5, 12.0) in 2022 for ovarian cancer, 1.7% (-2.1,
5.7) and 8.0% (3.4, 12.8) for leukemia, 5.3% (2.7, 7.9), 5.9% (3.0, 8.9) for prostate cancer, 1.3% (-1.4,
4.1) and 5.5% (2.3, 8.7) for lip/oral/pharyngeal cancer, and 1.9% (0.4, 3.4) and 2.0% (0.3, 3.7) for pancreaticcancer. Breast cancer had significant deficit mortality in 2020 and 2021, which shifted to excess in 2022,
though without statistical significance.

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

6 of 18

FIGURE 3: Excess mortality for each cancer type during the pandemic
in 2020, 2021, and 2022.
Excess mortality = (observed AMR − predicted AMR) / predicted AMR * 100 (%).
Predicted AMRs based on the 2010–2019 period preceding the pandemic were calculated using logistic
regression. The ⁑ symbol signifies >99% upper PI, * >95% upper PI, and †<95% lower PI.
As shown on the far left, excess mortality from all cancers was observed in 2021 and 2022, each over the
95% or 99% upper PI. Of the 20 types of cancers, five showed significant excess mortality: these were ovariancancer, leukemia, prostate, lip/oral/pharyngeal, and pancreatic cancers, in decreasing order of 2022 figures.
Breast cancer had significant deficit mortality in 2020 and 2021, which then shifted to excess mortality, though
without statistical significance.

AMRs for the four cancers with the most deaths (lung, colorectal, stomach, and liver cancer) showeddecreasing trends until the first year of the pandemic in 2020, but the rate of decrease slowed in 2021 and2022. Still, AMR stayed within the 95% PIs (Figure 4).

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

7 of 18

FIGURE 4: Trends in age-adjusted mortality rates over time for leading
cancers (lung, colorectal, stomach, and liver).
Lung, colorectal, stomach, and liver cancers all showed similar decreasing trends in age-adjusted mortality rates(AMRs). However, this decline slowed, and AMRs gradually exceeded the predicted values in 2021 and 2022 for
all, except for colorectal cancer. Pancreatic cancer, the fourth leading cause of death, is described later.

Trends for cancer types with excess mortalities in 2021 and 2022
Five types of cancer, namely, ovarian cancer, leukemia, prostate, lip/oral/pharyngeal, and pancreatic cancer,
had AMRs exceeding the predicted values in 2021 and/or 2022. Figure 5 illustrates the trends by year andmonth for these types of cancer. Four of them showed a gradual downward trend, while pancreatic cancer
showed an upward trend over time. All five types of cancer showed an increase in 2021 compared to 2020,
with ovarian, prostate, and pancreatic cancers exceeding 95% upper PIs in 2021, and all five cancer types
exceeding 95% upper PIs in 2022. Monthly excess mortalities gradually rose for these cancers in 2021-2022
compared to 2020.

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

8 of 18

FIGURE 5: Age-adjusted mortality rates (AMRs) over time and excess
mortality in each month for cancers with excess mortalities in 2021 and2022.
See Figure 1 for an explanation of the graphs' axes, units, and other elements.
(A) Ovarian cancer
There was a gradual downward trend from 2010, a slight increase from 2020, and a substantial increase from2021 to 2022, with AMRs exceeding the 99% upper PIs in 2021 and 2022. Monthly excess mortalities gradually
increased through 2021 and 2022.
(B) Leukemia
Annual AMRs of leukemia slowly declined or plateaued starting in 2010, but significantly increased in 2022,
exceeding the 95% upper PI. Monthly AMRs exceeded the 99% upper PI in January and the 95% upper PI in

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

9 of 18

December 2022.
(C) Prostate cancer
Annual AMRs were on a gradual downward trend starting in 2010, but increased starting in 2021, exceeding the
95% upper PIs in 2021 and 2022. Monthly excess mortalities gradually increased through 2021 and 2022,
exceeding the 95% upper PI in December 2022.
(D) Lip/oral/pharyngeal cancer
Annual AMRs over time were on a gradual downward trend, but increased in 2022, exceeding the 95% upper PI.
Monthly excess mortalities increased more clearly after the third mass vaccination for COVID-19 in 2022,
exceeding the 95% upper PIs in January and December 2022.
(E) Pancreatic cancer
AMRs in 2015 were excluded from the analysis because it was an apparent outlier for unknown reasons.
Annual AMRs of pancreatic cancer increased from 2010, began to exceed the 95% upper PI in 2020, and thendeviated further and increased between 2021 and 2022, exceeding the 99% upper PIs. Monthly excess
mortalities were seen from 2020, exceeding the 95% upper PI in December 2020, and increased even further,exceeding the 99% upper PIs in December 2021 and 2022.

Cancer type with monthly excess mortalities: breast cancer
Of the remaining 15 cancer types for which annual AMRs did not exceed the 95% PIs, the only cancer typefor which monthly AMRs deviated from the 95% PIs multiple times in a year was breast cancer in women.
Annual AMRs for breast cancer in women were under the 95% lower PIs in 2020 and 2021, and they increased
in 2022 but remained within the 95% PI (Figure 6, left side). Monthly statistically significant excess mortality
appeared several months after the peak of the third mass vaccination, exceeding the 95% upper PIs twice in2022 (right side).

FIGURE 6: Age-adjusted mortality rates (AMRs) over time and excess
mortality in each month during the pandemic: breast cancer.
See Figure 1 for an explanation of the graphs’ axes, units, and other elements.
Annual AMRs for breast cancer in women were under the 95% lower PIs in 2020 and 2021, and increased in
2022, but without statistical significance (left side). Monthly excess mortalities appeared several months
later, after the third mass vaccination, exceeding the 95% upper PIs twice in 2022 (right side).

Discussion
Scherb et al. estimated the crude excess mortality for all causes during the COVID-19 pandemic in Japan in
2020, 2021, and 2022 to be -2.84% (95%CI: -4.46, -1.25), 0.80% (-0.83, 2.40), and 8.37% (6.74, 9.97),
respectively, using linear logistic trend predictions based on 2005 to 2019 [5]. In our study, we estimatedage-adjusted excess mortality by logistic regression with predictions from 2010 to 2019 and calculated 2.3% (-2.7, -1.9), 2.1% (1.6, 2.6), and 9.6% (9.0, 10.2), respectively. These results seem consistent. For all
cancers, we estimated the excess mortalities to be -0.4% (-0.9, 0.1), 1.1% (0.5, 1.8), and 2.1% (1.4, 2.8),
respectively, indicating no excess in 2020 and statistically significant increases in 2021 and especially in
2022.

Findings in 2020, during the first year of the pandemic
Mortality from all cancers did not increase in 2020. The only statistically significant deviations from thepredictions in 2020 were a 3.9% excess in all cancer deaths among the 75-79 age group, a very slight (0.9%)2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

10 of 18

excess in pancreatic cancer deaths, and a 3.8% deficit in breast cancer deaths (a 4.3% deficit in breast cancer
deaths was also observed in 2021). In 2020, highly virulent strains of SARS-CoV-2 entered Japan, but there
were relatively few deaths attributed to COVID-19 in Japan [21]. Declarations of pandemic emergency
were issued three times before September 2021, requesting social distancing and securing hospitalization for
COVID-19 patients. Modeling studies were conducted in some countries to estimate the impact of thepandemic on cancer mortality [7,8]. In fact, during the first wave of COVID-19 in Belgium in March andApril 2020, cancer deaths increased by 10% and 33%, respectively, compared to the number of deaths
predicted from 2013 to 2018 [9]. In Madurai, a south Indian city, deaths attributed to cancer increased 109%
during the first weeks of lockdown [10]. In Brazil, during the first wave in March through May 2020, thenumbers of biopsies, colonoscopies, mammograms, and oncological surgeries decreased by 29%, 57%, 55%,
and 9%, respectively, compared to pre-pandemic figures. As a result, the number of hospitalizations forcancer decreased by 21%, whereas the mortality rate of hospitalized patients with cancer increased by
14% [22]. In Japan, the number of screenings for gastric, lung, colorectal, breast, and uterine cancers in thecommunity decreased by 24.4% (at work, only 0.9%) in 2020 and appeared to have returned to original trends
in 2021 [23]. The number of significant surgeries for gastrointestinal cancers decreased by 6.2% in 2020 and
5.1% in 2021 compared to those in 2018 and 2019 [23]. The decline of such cancer care could explain theexcess cancer mortality among the 75-79 age group and the slight increase in pancreatic cancer
deaths observed in 2020 in our study. The reasons for the decrease in breast cancer deaths in 2020-2021 are
unknown.

Findings in 2021 and 2022, the second and third years of the pandemic
The statistical findings on cancer deaths in 2021-2022 can be summarized as follows.
All cancer deaths: A statistically significant excess emerged in 2021 and increased further in 2022. In
addition, significant excess monthly mortality was observed after August 2021, whereas mass vaccination of
the general population began around April 2021. There were excess trends in cancer deaths across most age
groups. However, these trends were only statistically significant for age groups with the highest cancer
mortality: the 75-79 age group in 2021 and the 75-84 age group in 2022. More than 90% of people over 70
years of age have received the third vaccine dose.
Cancer type: Deaths from the most common types of cancer (lung, colorectal, stomach, and liver cancer)
showed downward trends in the pre-pandemic period, and the rates of decrease slowed during the pandemic
but remained within the 95% PIs. On the other hand, six of the 20 types of cancer (ovarian cancer, leukemia,
prostate, lip/oral/pharyngeal, pancreatic, and breast cancer) had statistically significant excess mortalities
in 2021, which increased further in 2022. Reduced cancer screening and healthcare due to the lockdown
might increase deaths for any type of cancer. Still, the significant increases in mortalities for six specificcancer types were unlikely to be explained by a shortage of healthcare services. As for the incidences during
this period, the crude numbers of all patients registered at core cancer care centers showed increasing trends
for prostate cancer in men and breast cancer in women in 2021, especially in 2022 [24]. However, these are
not age-adjusted and cannot be judged as increases.

Influence of multiple mRNA-LNP vaccine doses
Based on a report on domestic cancer care [23] and the absence of an emergency declaration from October
2021 onwards, the restrictions on access to cancer screening or treatment seem to have been much relieved
after late 2021. Mass vaccination with the first and second doses started in the spring of 2021, and thevaccination rate soon peaked in the summer of 2021 at 80% of the population. The vaccination rate for thethird dose peaked in the spring of 2022, at 68%. Now, Japan is even conducting mass vaccination with aseventh dose, making it the country with the highest vaccination rates.
Researchers have reported that the SARS-CoV-2 mRNA-LNP vaccine may pose the risk of development andprogression of cancer [25-28]. In addition, several case reports have described cancer developing or
worsening after vaccination and discussed possible causal links between cancer and mRNA-LNP vaccination[29-34].
Based on the molecular weight of BNT162b2 mRNA (Pfizer-BioNTech), the mRNA content per dose isestimated at 13 trillion molecules and 40 trillion molecules in mRNA-1273 (Moderna) [35,36]. The total
number of cells in humans is estimated to be 37.2 trillion [37], making the number of mRNA-LNPs very high,ranging from one-third to the equivalent of the total cell number. After inoculation, the mRNA-LNPs aredelivered to various organs, especially the liver, spleen, adrenal gland, ovary, and bone marrow [38]. In one
study, vaccine mRNA was detected in the lymph nodes of persons vaccinated with hybridization of a SARSCoV-2 mRNA vaccine-specific probe 7 to 60 days after the second mRNA-1273 or BNT162b2 dose [39].
Modified mRNA with N1-methyl-pseudouridine could translate a large amount of SARS-CoV-2 spike protein(S-protein) [40]. S-protein emerged on the surface of exosomes in the blood of the vaccinated [41].
Fragments of vaccine-specific recombinant S-protein were found in blood specimens of 50% of vaccine
recipients and were still detected three to six months later [42].
On the other hand, in the case of SARS-CoV-2 infection, which is basically a respiratory infection, viral S-

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

11 of 18

protein was only detected in serum for up to 10-20 days, even in patients with acute severe disease [4345]. The attenuated Omicron strains emerged in Japan in early 2022 and have been prevalent at various
points since then. As shown in the graphs in Figures 1, 5, and 6, the monthly number of vaccinatedindividuals was many times greater than that of newly confirmed cases of infection, and the cumulative
number of vaccinated individuals (380 million) was 13 times that of newly confirmed cases of infection (30million) until the end of 2022.
A study of more than 50,000 employees at a medical institution in the United States observed the incidenceof the Omicron variant epidemic based on the number of vaccine doses received (0, 1, 2, 3, and 4 or moredoses) over a period of 26 weeks and showed that the number of vaccines received was positively correlatedwith the cumulative incidence rate of COVID-19 [46]. Susceptibility to COVID-19 infection after multiple
vaccinations may be enhanced by antibody-dependent enhancement [47], immune imprinting [39,48], andimmunosuppression [25-27]. This can result in a risk of exposure to viral S-protein in addition to vaccine Sprotein for the multiple-vaccinated. These data suggest a significant impact on vaccine recipients, including
the large number of mRNA-LNPs that are injected, their rapid and widespread distribution particularly into
specific organs, the amount of S-protein produced, its long persistence in the body, and increased
susceptibility to infection. Next, we consider each factor that may contribute to the involvement of themRNA-LNP SARS-CoV-2 vaccine in increasing mortality from all cancers and some specific cancer types.

Thrombogenic effects of spike protein and LNP
Because cancer often leads to the activation of coagulation via various mechanisms, one of the major causesof mortality in patients with cancer is cancer-associated thrombosis (CAT) [49-51], manifesting as
disseminated intravascular coagulation (DIC) in its most extreme form [52]. Therefore, it is reasonable toassume that the additional thrombus-forming tendency noted with the mRNA-LNP vaccine could
be extremely dangerous. The viral and vaccine S-protein of SARS-CoV-2, especially Omicronlineages, having a solid electro-positive potential, could attach to electro-negative glycoconjugates on thesurfaces of red blood cells (RBCs), other blood cells, and endothelial cells [53]. The S-protein of SARS-CoV-2
alone has been reported to bind to angiotensin-converting enzyme 2 (ACE2) and activate angiotensin II
receptor type 1 (AT1) signal, which promotes interleukin-6 (IL-6) trans-signaling [54], induces vascular wall
thickening via activation of the protein kinases [55], impairs mitochondrial function [56], and generates
reactive oxygen species (ROS) [57]. A recent study revealed that certain segments of the S-protein can induce
the formation of amyloid, a fibrous protein that is insoluble in water. This protein plays a significant role inblood coagulation and fibrinolytic disorders [58]. Anti-spike protein antibodies bind to the S-proteins thatemerge on cell surfaces, which triggers autoimmune inflammatory reactions [59-63]. In addition, theinjection of LNPs into mice has been reported to cause strong inflammation [64]. All these findings suggestthat the COVID-19 mRNA-LNP vaccine poses a risk of thrombosis in individuals with cancer and might
explain the excess mortalities after mass vaccination.

Suppression of cancer immunosurveillanceSome studies have shown that type I interferon (INF) responses, which play an essential role in cancer
immunosurveillance, are suppressed after SARS-CoV-2 mRNA-LNP vaccination [65,66]. A large number of
exosomes containing microRNA (miRNA)-148a and miRNA-590 are released from cells where large amountsof S-protein were translated, and each miRNA suppresses the ubiquitin-specific peptidase 33 (USP33)interferon regulatory factor (IRF9) axis in microglia, which internalize these exosomes [67]. In a review,
Seneff et al. explained that this suppresses the function of type I IFN and BRCA2, which are
critical factors against cancer cells [26]. Programmed death-ligand1 (PD-L1)/programmed cell death 1 (PD1) expression in the tumor microenvironment suppresses cancer immunosurveillance profoundly [68]. One
study showed that S-protein exposure increases surface expression of PD-L1 on a wide range of immune cell
types and tumor cells, and PD-1 on T cells, which suppresses the activity of CD4+ and CD8+ T cells against
cancer cells [69]. Another study found that non-cancer-specific IgG4 inhibits the antibody effector functions
mediated by cancer-specific IgG1, as evidenced by the dramatic acceleration of growth observed in
implanted colorectal and breast tumors and skin papillomas caused by carcinogens following local
administration of non-cancer-specific IgG4 [70]. According to a meta-analysis in all cancers, pancreaticcancer, and lymphoma, standardized incidence ratios (SIRs) of patients with IgG4-related disease (IgG4-RD)
to the general population were 2.57 (95%CI: 1.72, 3.84), 4.07 (1.04, 15.92), and 69.17 (3.91, 1,223.04),
respectively [71]. In another study, anti-spike IgG4 levels rose in the serum of SARS-CoV-2 mRNA vaccine
recipients after the second dose and increased further after the third dose [72]. A review on IgG4 discussed
how long-term exposure to large amounts of specific antigens, such as those found in SARS-CoV-2 mRNA
vaccines, may cause uncontrolled growth of cancer cells through a class switch from IgG1 or IgG3 to IgG4[73]. Another study showed that IL-10 release with non-specific stimulation in fresh whole blood ofrecipients of the second dose of BNT162b2 or mRNA-1273 increased within two weeks [74]. These findings
might explain excess mortality for all cancers, especially excess deaths for pancreatic cancer and breast
cancer in our study.
The SARS-CoV-2 vaccine has been shown to cause immunosuppression and lead to the reactivation of latentviruses such as varicella-zoster virus (VZV, human herpesvirus 3; HHV3) or human herpesvirus 8 (HHV8) insome cases [75,76]. HHV8 is considered oncogenic and can cause Kaposi's sarcoma. Oropharyngeal cancer is

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

12 of 18

reported to be caused by the Ebstein-Barr virus (EBV, HHV4) or human papillomavirus (HPV) [77], which also
may be reactivated by possible immunosuppression resulting from vaccination. These phenomena could also
help explain the excess deaths from lip/oral/pharyngeal cancer in 2022 when mass vaccination with thirdand later doses was underway.

Cancer development by SARS-CoV-2 mRNA vaccine
In our study, the AMRs of ovarian cancer, leukemia, prostate, lip/oral/pharyngeal, pancreatic, and breast
cancers increased significantly beyond the predicted rates, especially in 2022. All of these cancers are known
as estrogen and estrogen receptor alpha (ERα)-sensitive cancers [78-83]. Recent research by Solis et al. on
the binding ability of S-protein of SARS-CoV-2 against over 9,000 human proteins has shown that S-protein
specifically binds to ERα and upregulates the transcriptional activity of ERα. The addition of estradiol (E2)to human breast cancer cells causes proliferation of the cancer cells, whereas the addition of raloxifene, aselective ERα modulator, inhibits proliferation. Breast cancer cells grow when S is added instead of E2, andthe addition of raloxifene inhibits their growth. Solis et al. also mentioned that the finding of SERα cytosolic colocalization may lead to a potentiation of membrane-bound ERα signaling [84]. Membranebound ERα has been implicated in many pathways, including the activation of c-Myc, which promotes thecell cycle and impacts cancer development [85].
ERα-mediated transcription may induce endogenous DNA double-strand breaks (DSBs) in ER-sensitivecancers [86]. Studies have shown that transcriptionally activated ERα induces DSBs by topoisomerase II andthe recently known R-loop/G-quadruplex structures formation, significantly increasing the need for BRCA1
for their repair in breast cancer cells [87-89]. One study showed nuclear translocation of mRNA and S proteinwith the nuclear localization signal [90], and an in silico bioinformatic analysis showed interactions between
the S2 subunit of S-protein and BRCA1, BRCA2, and P53 [91], possibly resulting in their sequestration anddysfunction. The possible co-occurrence of high BRCA1 demand to repair DNA damage caused by activatedtranscription via ERα bound with S-protein along with dysfunction of BRCA1 sequestrated by Sprotein raises concerns about increased cancer risk in ERα-sensitive cells in mRNA-LNP SARS-CoV-2
vaccine recipients.
As mentioned above, there is also great concern about the risk of dysfunction in the crucial cancer
suppressor genes, brca2 and P53, as well as BRCA1, through mechanisms involving downregulation of
IRF9 through interference by specific miRNA in exosomes [26] and the possible sequestration by the S2
subunit of S-protein in the vaccine [91]. Impaired BRCA1 activity is associated with higher risk of breast,uterine, and ovarian cancer in women and prostate cancer in men, as well as moderately higher riskof pancreatic cancer in both men and women [92]. BRCA2-associated cancers include breast and ovariancancer in women, prostate and breast cancer in men, and acute myeloid leukemia in children [26]. Thesefindings are highly consistent with our results.
Additional factors that may contribute to the development of cancer are being investigated. Since
endogenous ROS causes oxidative DNA damage [93], excessive oxidative stress resulting from thedownregulation of ACE2 by S-protein [57] may contribute to cancer development. One study showed that the
downregulation of the Mas receptor promotes the metastasis of epithelial ovarian cancer [94]. ACE2 receptor,
bound by S-protein after mRNA-LNP vaccination, may directly cause downregulation and subsequentdysfunction of the Mas receptor, possibly leading to an increased risk of metastasis in vaccinated womenwith ovarian cancer. The observation that injected LNPs accumulate particularly in the ovaries and bonemarrow [38] could better explain our findings of excess mortalities from ovarian cancer and leukemia in
2022. According to an analysis of scientific literature about sex hormone receptors in head and necksquamous cell carcinoma (HNSCC), ERα plays various roles in the biopathology of HNSCC, particularly
oropharyngeal cancer. These include promoting DNA hypermutation, facilitating HPV integration, andcooperating with epithelial growth factor receptor (EGFR) [82]. This may explain the increased mortality of
lip/oral/pharyngeal cancer in our study.
A recent study showed that SARS-CoV-2 RNA could be reverse-transcribed to DNA and integrated into thehuman cell genome in vitro [95]. Another study reported that transfected mRNA in the human cells exposed
to BNT162b2 leads to unsilencing of the endogenous retrotransposon long interspersed element-1 (LINE-1)
and reverse transcription of vaccine mRNA sequences to DNA in the nucleus [96]. Accumulation of vaccine
mRNA and reverse-transcribed DNA molecules in the cytoplasm could be expected to induce chronic
autoinflammation, autoimmunity, DNA damage, and cancer risk in susceptible individuals [97].
The U.S. Food and Drug Administration (FDA) states in its guidance for the production of viral vaccines for
infectious disease, "There are several potential mechanisms by which residual DNA could be oncogenic,
including the integration and expression of encoded oncogenes or insertional mutagenesis following DNA
integration" [98]. The FDA's guidelines are essential for Japan because Japan's special emergency use
authorization depended on FDA approval during the COVID-19 pandemic [99]. Recently, some researchers
have reported that several lots of Pfizer-BioNTech and Moderna vaccines contain a certain amount of
double-stranded DNA fragments from residual plasmid vectors [100,101]. Some of them mentioned that theamount of residual DNA exceeds the regulatory limits for residual DNA set by the FDA. Given these reports
and the FDA's regulatory statement, further investigation is required to determine whether the observed

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

13 of 18

excess cancer deaths following mass vaccination were linked to reported residual DNA in the vaccine.

Limitations
This study was conducted using descriptive statistics from official sources and has not been clinicallyvalidated. Further analytical statistics study by vaccination status is needed.

Conclusions
Statistically significant increases in age-adjusted mortality rates of all cancer and some specific types ofcancer, namely, ovarian cancer, leukemia, prostate, lip/oral/pharyngeal, pancreatic, and breast cancers, wereobserved in 2022 after two-thirds of the Japanese population had received the third or later dose of SARSCoV-2 mRNA-LNP vaccine. These particularly marked increases in mortality rates of these ERα-sensitivecancers may be attributable to several mechanisms of the mRNA-LNP vaccination rather than COVID-19
infection itself or reduced cancer care due to the lockdown. The significance of this possibility warrants
further studies. This article was previously posted to the Zenodo repository server on September 18, 2023.

Additional Information
Author Contributions
All authors have reviewed the final version to be published and agreed to be accountable for all aspects of thework.
Concept and design: Miki Gibo, Seiji Kojima, Masanori Fukushima
Acquisition, analysis, or interpretation of data: Miki Gibo, Seiji Kojima, Akinori Fujisawa, Takayuki
Kikuchi, Masanori Fukushima
Drafting of the manuscript: Miki Gibo, Seiji Kojima
Critical review of the manuscript for important intellectual content: Miki Gibo, Seiji Kojima, Akinori
Fujisawa, Takayuki Kikuchi, Masanori Fukushima
Supervision: Seiji Kojima, Masanori Fukushima

Disclosures
Human subjects: All authors have confirmed that this study did not involve human participants or tissue.
Animal subjects: All authors have confirmed that this study did not involve animal subjects or tissue.
Conflicts of interest: In compliance with the ICMJE uniform disclosure form, all authors declare thefollowing: Payment/services info: All authors have declared that no financial support was received fromany organization for the submitted work. Financial relationships: All authors have declared that they haveno financial relationships at present or within the previous three years with any organizations that might
have an interest in the submitted work. Other relationships: All authors have declared that there are noother relationships or activities that could appear to have influenced the submitted work.

Acknowledgements
We sincerely thank Professor Satoshi Teramukai at Kyoto Prefectural University for his essential advice on
statistics, Professor Emeritus Yasufumi Murakami at Tokyo University of Science for his excellent advice on
the biology of cancer, and Ms. Yuriko Hirai at MCL Corporation for her help with the manuscript. Dataavailability: All data analyzed in this study can be obtained from the official websites.

References
1.
2.

3.

4.

5.
6.

COVID-19 vaccines. (2023). Accessed: February 29, 2024:
https://japan.kantei.go.jp/ongoingtopics/vaccine.html.
Msemburi W, Karlinsky A, Knutson V, Aleshin-Guendel S, Chatterji S, Wakefield J: The WHO estimates ofexcess mortality associated with the COVID-19 pandemic. Nature. 2023, 613:130-7. 10.1038/s41586-02205522-2
COVID-19 Excess Mortality Collaborators: Estimating excess mortality due to the COVID-19 pandemic: asystematic analysis of COVID-19-related mortality, 2020-21. Lancet. 2022, 399:1513-36. 10.1016/S01406736(21)02796-3
Msemburi W, Karlinsky A, Knutson V, Aleshin-Guendel S, Chatterji S, Wakefield J: Excess deaths associatedwith covid-19 pandemic in 2020: age and sex disaggregated time series analysis in 29 high income
countries. Nature. 2023, 613:130-7. 10.1136/bmj.n1137
Scherb H, Hayashi K: Annual all-cause mortality rate in Germany and Japan (2005 to 2022) with focus on the
COVID-19 pandemic: hypotheses and trend analyses. Med Clin Sci. 2023, 5:16-22. 10.33425/2690-5191.1077
Kuhbandner C, Reitzner M: Estimation of excess mortality in Germany during 2020-2022 . Cureus. 2023,
15:e39371. 10.7759/cureus.39371

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

14 of 18

7.

8.

9.

10.
11.

12.
13.

14.

15.
16.
17.
18.

19.
20.
21.

22.

23.
24.
25.
26.

27.

28.
29.

30.

31.

32.

33.

34.

35.

Maringe C, Spicer J, Morris M, et al.: The impact of the COVID-19 pandemic on cancer deaths due to delays
in diagnosis in England, UK: a national, population-based, modelling study. Lancet Oncol. 2020, 21:102334. 10.1016/S1470-2045(20)30388-0
Alagoz O, Lowry KP, Kurian AW, et al.: Impact of the COVID-19 pandemic on breast cancer mortality in the
US: estimates from collaborative simulation modeling. J Natl Cancer Inst. 2021, 113:1484-94.
10.1093/jnci/djab097
Silversmit G, Verdoodt F, Van Damme N, De Schutter H, Van Eycken L: Excess mortality in a nationwide
cohort of cancer patients during the initial phase of the COVID-19 pandemic in Belgium. Cancer Epidemiol
Biomarkers Prev. 2021, 30:1615-9. 10.1158/1055-9965.EPI-21-0230
Lewnard JA, B CM, Kang G, Laxminarayan R: Attributed causes of excess mortality during the COVID-19
pandemic in a south Indian city. Nat Commun. 2023, 14:3563. 10.1038/s41467-023-39322-7
Tanaka H, Togawa K, Katanoda K: Impact of the COVID-19 pandemic on mortality trends in Japan: areversal in 2021? A descriptive analysis of national mortality data, 1995-2021. BMJ Open. 2023, 13:e071785.
10.1136/bmjopen-2023-071785
Excess and exiguous deaths dashboard in Japan . (2023). Accessed: March 28, 2024: https://exdeathsjapan.org/en/.
Mieno MN, Tanaka N, Arai T, Kawahara T, Kuchiba A, Ishikawa S, Sawabe M: Accuracy of death certificatesand assessment of factors for misclassification of underlying cause of death. J Epidemiol. 2016, 26:191-8.
10.2188/jea.JE201500[The Ministry of Health, Labour and Welfare: e-Stat, The Vital Statistics] . (2023). Accessed: March 28, 2024:
https://www.e-stat.go.jp/stat-search/files?
page=1&layout=datalist&toukei=00450011&tstat=000001028897&cycle=1&tclass1=....
[The Statistics Bureau of the Ministry of Internal Affairs and Communications: the summary of population
estimation results]. (2023). Accessed: March 28, 2024: https://www.stat.go.jp/data/jinsui/2.html#monthly.
[Visualizing the data, information on COVID-19 infections] . (2023). Accessed: March 28, 2024:
https://covid19.mhlw.go.jp/extensions/public/en/index.html.
[Status of reports of suspected adverse reactions from medical institutions under the immunization law] .
(2023). Accessed: March 28, 2024: https://www.mhlw.go.jp/content/10601000/001068689.pdf.
Finding and using health statistics . (2023). Accessed: March 10, 2024:
https://www.nlm.nih.gov/oet/ed/stats/02600.html#:~:text=Age%2Dadjusted%20rates%20were%20calculated,size%20of%20that%....
[The standard population for age-adjusted mortality rates] . (2020). Accessed: March 28, 2024:
https://www.mhlw.go.jp/toukei/saikin/hw/jinkou/kakutei20/dl/14_nencho.pdf.
[Sex-specific smoothed standard population dataset] . (2019). Accessed: March 28, 2024:
https://www.mhlw.go.jp/content/10700000/000557741.pdf.
Cumulative confirmed COVID-19 deaths per million people . (2020). Accessed: March 11, 2024:
https://ourworldindata.org/explorers/coronavirus-data-explorer?
tab=table&zoomToSelection=true&time=2020-12-31..latest....
Fonseca GA, Normando PG, Loureiro LV, Rodrigues RE, Oliveira VA, Melo MD, Santana IA: Reduction in thenumber of procedures and hospitalizations and increase in cancer mortality during the COVID-19 pandemicin Brazil. JCO Glob Oncol. 2021, 7:4-9. 10.1200/GO.20.00471[Impact of new coronavirus infection on cancer screening and treatment (evaluation in FY2021)] . (2023).
Accessed: March 28, 2024: https://www.mhlw.go.jp/content/10901000/001046961.pdf.
[In-Hospital Cancer Registry: 2022 National Aggregate]. (2024). Accessed: February 28, 2024:
https://ganjoho.jp/public/qa_links/report/hosp_c/pdf/2022_report.pdf.
Seneff S, Nigh G: Worse than the disease? Reviewing some possible unintended consequences of the mRNA
vaccines against COVID-19. Int J Vaccine, theory, Pract Res. 2021, 2:38-79. 10.56098/ijvtpr.v2i1.23
Seneff S, Nigh G, Kyriakopoulos AM, McCullough PA: Innate immune suppression by SARS-CoV-2 mRNA
vaccinations: the role of G-quadruplexes, exosomes, and MicroRNAs. Food Chem Toxicol. 2022, 164:113008.
10.1016/j.fct.2022.113008
Parry PI, Lefringhausen A, Turni C, Neil CJ, Cosford R, Hudson NJ, Gillespie J: ‘Spikeopathy’: COVID-19
spike protein is pathogenic, from both virus and vaccine mRNA. Biomedicines. 2023, 11:2287.
10.3390/biomedicines11082287
Valdes Angues R, Perea Bustos Y: SARS-CoV-2 vaccination and the multi-hit hypothesis of oncogenesis .
Cureus. 2023, 15:e50703. 10.7759/cureus.50703
Goldman S, Bron D, Tousseyn T, et al.: Rapid progression of angioimmunoblastic T cell lymphoma following
BNT162b2 mRNA vaccine booster shot: a case report. Front Med (Lausanne). 2021, 8:798095.
10.3389/fmed.2021.798095
Sekizawa A, Hashimoto K, Kobayashi S, et al.: Rapid progression of marginal zone B-cell lymphoma after
COVID-19 vaccination (BNT162b2): a case report. Front Med (Lausanne). 2022, 9:963393.
10.3389/fmed.2022.963393
Zamfir MA, Moraru L, Dobrea C, et al.: Hematologic malignancies diagnosed in the context of the mRNA
COVID-19 vaccination campaign: a report of two cases. Medicina (Kaunas). 2022, 58:874.
10.3390/medicina58070874
Cavanna L, Grassi SO, Ruffini L, et al.: Non-Hodgkin lymphoma developed shortly after mRNA COVID-19
vaccination: report of a case and review of the literature. Medicina (Kaunas). 2023, 59:157.
10.3390/medicina59010157
Ang SY, Huang YF, Chang CT: pH-positive B-cell acute lymphoblastic leukemia occurring after receipt ofbivalent SARS-CoV-2 mRNA vaccine booster: a case report. Medicina (Kaunas). 2023, 59:627.
10.3390/medicina59030627
Kyriakopoulos AM, Nigh G, McCullough PA, Olivier MD, Seneff S: Bell's palsy or an aggressive infiltratingbasaloid carcinoma post-mRNA vaccination for COVID-19? A case report and review of the literature. EXCLI
J. 2023, 22:992-1011. 10.17179/excli2023-6145
Number of mRNA molecules in Pfizer Biontec COVID-19 vaccine . (2021). Accessed: March 28, 2024:

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

15 of 18

36.

37.
38.
39.

40.

41.

42.

43.

44.
45.

46.
47.
48.
49.
50.

51.
52.
53.

54.
55.
56.
57.
58.
59.
60.
61.
62.

63.

64.

65.

https://ameblo.jp/toonomikado/entry-12667109507.html.
McKernan K, Kyriakopoulos A, McCullough PA: Differences in vaccine and SARS-CoV-2 replication derived
mRNA: implications for cell biology and future disease [PREPRINT]. OSF Preprints. 2021,
10.31219/osf.io/bcsa6
Bianconi E, Piovesan A, Facchin F, et al.: An estimation of the number of cells in the human body . Ann Hum
Biol. 2013, 40:463-71. 10.3109/03014460.2013.807878
SARS-CoV-2 mRNA vaccine (BNT162, PF-07302048) . (2021). Accessed: March 28, 2024:
https://www.pmda.go.jp/drugs/2021/P20210212001/672212000_30300AMX00231_I100_2.pdf.
Röltgen K, Nielsen SC, Silva O, et al.: Immune imprinting, breadth of variant recognition, and germinal
center response in human SARS-CoV-2 infection and vaccination. Cell. 2022, 185:1025-40.e14.
10.1016/j.cell.2022.01.018
Kim SC, Sekhon SS, Shin WR, Ahn G, Cho BK, Ahn JY, Kim YH: Modifications of mRNA vaccine structuralelements for improving mRNA stability and translation efficiency. Mol Cell Toxicol. 2022, 18:1-8.
10.1007/s13273-021-00171-4
Bansal S, Perincheri S, Fleming T, Poulson C, Tiffany B, Bremner RM, Mohanakumar T: Cutting edge:
Circulating exosomes with Covid spike protein are induced by BNT162b2 (Pfizer-BioNTech) vaccination
prior to development of antibodies: a novel mechanism for immune activation by mRNA vaccines. J
Immunol. 2021, 207:2405-10. 10.4049/jimmunol.2100637
Brogna C, Cristoni S, Marino G, et al.: Detection of recombinant Spike protein in the blood of individuals
vaccinated against SARS-CoV-2: possible molecular mechanisms. Proteomics Clin Appl. 2023, 17:e2300048.
10.1002/prca.202300048
Andersson MI, Arancibia-Carcamo CV, Auckland K, et al.: SARS-CoV-2 RNA detected in blood products frompatients with COVID-19 is not associated with infectious virus. Wellcome Open Res. 2020, 5:181.
10.12688/wellcomeopenres.16002.2
Wölfel R, Corman VM, Guggemos W, et al.: Virological assessment of hospitalized patients with COVID2019. Nature. 2020, 581:465-9. 10.1038/s41586-020-2196-x
Ogata AF, Maley AM, Wu C, et al.: Ultra-sensitive serial profiling of SARS-CoV-2 antigens and antibodies inplasma to understand disease progression in COVID-19 patients with severe disease. Clin Chem. 2020,
66:1562-72. 10.1093/clinchem/hvaa213
Shrestha NK, Burke PC, Nowacki AS, Simon JF, Hagen A, Gordon SM: Effectiveness of the coronavirus
disease 2019 bivalent vaccine. Open Forum Infect Dis. 2023, 10:ofad209. 10.1093/ofid/ofad209
Liu Y, Soh WT, Kishikawa JI, et al.: An infectivity-enhancing site on the SARS-CoV-2 spike protein targetedby antibodies. Cell. 2021, 184:3452-66.e18. 10.1016/j.cell.2021.05.032
Vatti A, Monsalve DM, Pacheco Y, Chang C, Anaya JM, Gershwin ME: Original antigenic sin: acomprehensive review. J Autoimmun. 2017, 83:12-21. 10.1016/j.jaut.2017.04.008
Streiff MB, Abutalib SA, Farge D, Murphy M, Connors JM, Piazza G: Update on guidelines for the
management of cancer-associated thrombosis. Oncologist. 2021, 26:e24-40. 10.1002/onco.13596
Walker AJ, Card TR, West J, Crooks C, Grainge MJ: Incidence of venous thromboembolism in patients withcancer - a cohort study using linked United Kingdom databases. Eur J Cancer. 2013, 49:1404-13.
10.1016/j.ejca.2012.10.021
Madoiwa S: [Reviews: cancer-associated thrombosis: crosstalk between cancer and blood coagulationfibrinolysis system]. Nihon Kessen Shiketsu Gakkai Shi. 2023, 34:556-65. 10.2491/jjsth.34.556
Levi M: Disseminated intravascular coagulation in cancer: an update . Semin Thromb Hemost. 2019, 45:3427. 10.1055/s-0039-1687890
Boschi C, Scheim DE, Bancod A, et al.: SARS-CoV-2 spike protein induces hemagglutination: implications
for COVID-19 morbidities and therapeutics and for vaccine adverse effects. Int J Mol Sci. 2022, 23:15480.
10.3390/ijms232415480
Patra T, Meyer K, Geerling L, et al.: SARS-CoV-2 spike protein promotes IL-6 trans-signaling by activationof angiotensin II receptor signaling in epithelial cells. PLoS Pathog. 2020, 16: 10.1371/journal.ppat.1009128
Suzuki YJ, Nikolaienko SI, Dibrova VA, et al.: SARS-CoV-2 spike protein-mediated cell signaling in lungvascular cells. Vascul Pharmacol. 2021, 137:106823. 10.1016/j.vph.2020.106823
Lei Y, Zhang J, Schiavon CR, et al.: SARS-CoV-2 spike protein impairs endothelial function via
downregulation of ACE 2. Circ Res. 2021, 128:1323-6. 10.1161/CIRCRESAHA.121.318902
Eguchi S, Kawai T, Scalia R, Rizzo V: Understanding angiotensin II type 1 receptor signaling in vascularpathophysiology. Hypertension. 2018, 71:804-10. 10.1161/HYPERTENSIONAHA.118.10266
Nyström S, Hammarström P: Amyloidogenesis of SARS-CoV-2 spike protein . J Am Chem Soc. 2022,
144:8945-50. 10.1021/jacs.2c03925
Zin Tun GS, Gleeson D, Al-Joudeh A, Dube A: Immune-mediated hepatitis with the Moderna vaccine, nolonger a coincidence but confirmed. J Hepatol. 2022, 76:747-9. 10.1016/j.jhep.2021.09.031
Boettler T, Csernalabics B, Salié H, et al.: SARS-CoV-2 vaccination can elicit a CD8 T-cell dominant
hepatitis. J Hepatol. 2022, 77:653-9. 10.1016/j.jhep.2022.03.040
Lyons-Weiler J: Pathogenic priming likely contributes to serious and critical illness and mortality in
COVID-19 via autoimmunity. J Transl Autoimmun. 2020, 3:100051. 10.1016/j.jtauto.2020.100051
Vojdani A, Vojdani E, Kharrazian D: Reaction of human monoclonal antibodies to SARS-CoV-2 proteins
with tissue antigens: implications for autoimmune diseases. Front Immunol. 2020, 11:617089.
10.3389/fimmu.2020.617089
Polykretis P, Donzelli A, Lindsay JC, et al.: Autoimmune inflammatory reactions triggered by the COVID-19
genetic vaccines in terminally differentiated tissues. Autoimmunity. 2023, 56:2259123.
10.1080/08916934.2023.2259123
Ndeupen S, Qin Z, Jacobsen S, Bouteau A, Estanbouli H, Igyártó BZ: The mRNA-LNP platform's lipid
nanoparticle component used in preclinical vaccine studies is highly inflammatory. iScience. 2021,
24:103479. 10.1016/j.isci.2021.103479
Ivanova EN, Shwetar J, Devlin JC, et al.: mRNA COVID-19 vaccine elicits potent adaptive immune response
without the acute inflammation of SARS-CoV-2 infection. iScience. 2023, 26:108572.

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

16 of 18

66.
67.
68.
69.

70.
71.
72.
73.

74.

75.

76.

77.

78.
79.

80.
81.

82.

83.
84.
85.
86.
87.

88.
89.
90.
91.
92.

93.
94.

95.

10.1016/j.isci.2023.108572
Föhse K, Geckin B, Zoodsma M, et al.: The impact of BNT162b2 mRNA vaccine on adaptive and innate
immune responses. Clin Immunol. 2023, 255:109762. 10.1016/j.clim.2023.109762
Mishra R, Banerjea AC: SARS-CoV-2 spike targets USP33-IRF9 axis via exosomal miR-148a to activate
human microglia. Front Immunol. 2021, 12:656700. 10.3389/fimmu.2021.656700
Lipson EJ, Lilo MT, Ogurtsova A, et al.: Basal cell carcinoma: PD-L1/PD-1 checkpoint expression and tumor
regression after PD-1 blockade. J Immunother Cancer. 2017, 5:23. 10.1186/s40425-017-0228-3
Loacker L, Kimpel J, Bánki Z, Schmidt CQ, Griesmacher A, Anliker M: Increased PD-L1 surface expression on
peripheral blood granulocytes and monocytes after vaccination with SARS-CoV2 mRNA or vector vaccine.
Clin Chem Lab Med. 2023, 61:e17-9. 10.1515/cclm-2022-0787
Wang H, Xu Q, Zhao C, et al.: An immune evasion mechanism with IgG4 playing an essential role in cancer
and implication for immunotherapy. J Immunother Cancer. 2020, 8:e000661. 10.1136/jitc-2020-000661
Yu T, Wu Y, Liu J, Zhuang Y, Jin X, Wang L: The risk of malignancy in patients with IgG4-related disease: asystematic review and meta-analysis. Arthritis Res Ther. 2022, 24:14. 10.1186/s13075-021-02652-2
Irrgang P, Gerling J, Kocher K, et al.: Class switch toward noninflammatory, spike-specific IgG4 antibodies
after repeated SARS-CoV-2 mRNA vaccination. Sci Immunol. 2023, 8:10.1126/sciimmunol.ade2798
Uversky VN, Redwan EM, Makis W, Rubio-Casillas A: IgG4 antibodies induced by repeated vaccination may
generate immune tolerance to the SARS-CoV-2 spike protein. Vaccines (Basel). 2023, 11:991.
10.3390/vaccines11050991
Gil-Manso S, Carbonell D, López-Fernández L, et al.: Induction of high levels of specific humoral andcellular responses to SARS-CoV-2 after the administration of COVID-19 mRNA vaccines requires several
days. Front Immunol. 2021, 12:726960. 10.3389/fimmu.2021.726960
Katsikas Triantafyllidis K, Giannos P, Mian IT, Kyrtsonis G, Kechagias KS: Varicella zoster virus reactivation
following COVID-19 vaccination: a systematic review of case reports. Vaccines (Basel). 2021, 9:1013.
10.3390/vaccines9091013
Chen J, Dai L, Barrett L, James J, Plaisance-Bonstaff K, Post SR, Qin Z: SARS-CoV-2 proteins and antiCOVID-19 drugs induce lytic reactivation of an oncogenic virus. Commun Biol. 2021, 4:682.
10.1038/s42003-021-02220-z
Migliaro M, Massuh D, Infante MF, Brahm AM, San Martín MT, Ortuño D: Role of Epstein-Barr virus andhuman papilloma virus in the development of oropharyngeal cancer: a literature review. Int J Dent. 2022,
2022:10.1155/2022/3191569
Langdon SP, Herrington CS, Hollis RL, Gourley C: Estrogen signaling and its potential as a target for therapy
in ovarian cancer. Cancers (Basel). 2020, 12:1647. 10.3390/cancers12061647
van Dijk AD: The functional role of estrogen receptor alpha (ERα) in AML; a new potential therapeutictarget for the treatment of inv (16) and MLL-rearranged acute myeloid leukemia. University of Groningen,
Groningen, Netherlands; 2020.
Xue J, Yao Y, Yao Q, et al.: Important roles of estrogen receptor alpha in tumor progression and antiestrogen therapy of pancreatic ductal adenocarcinoma. Life Sci. 2020, 260:118302. 10.1016/j.lfs.2020.118302
Drake V, Bigelow E, Fakhry C, et al.: Biologic and behavioral associations of estrogen receptor alphapositivity in head and neck squamous cell carcinoma. Oral Oncol. 2021, 121:105461.
10.1016/j.oraloncology.2021.105461
DE Oliveira Neto CP, Brito HO, DA Costa RM, Brito LM: Is there a role for sex hormone receptors in headand-neck cancer? Links with HPV infection and prognosis. Anticancer Res. 2021, 41:3707-16.
10.21873/anticanres.15162
Jefferi NE, Shamhari A', Noor Azhar NK, et al.: The role of erα and erβ in castration-resistant prostatecancer and current therapeutic approaches. Biomedicines. 2023, 11: 10.3390/biomedicines11030826
Solis O, Beccari AR, Iaconis D, et al.: The SARS-CoV-2 spike protein binds and modulates estrogen
receptors. Sci Adv. 2022, 8:eadd4150. 10.1126/sciadv.add4150
Schwartz N, Verma A, Bivens CB, Schwartz Z, Boyan BD: Rapid steroid hormone actions via membrane
receptors. Biochim Biophys Acta. 2016, 1863:2289-98. 10.1016/j.bbamcr.2016.06.004
Williamson LM, Lees-Miller SP: Estrogen receptor α-mediated transcription induces cell cycle-dependent
DNA double-strand breaks. Carcinogenesis. 2011, 32:279-85. 10.1093/carcin/bgq255
Sasanuma H, Tsuda M, Morimoto S, et al.: BRCA1 ensures genome integrity by eliminating estrogeninduced pathological topoisomerase II-DNA complexes. Proc Natl Acad Sci U S A. 2018, 115:E10642-51.
10.1073/pnas.1803177115
Kim S, Hwang S: G-quadruplex matters in tissue-specific tumorigenesis by BRCA1 deficiency . Genes (Basel).
2022, 13:391. 10.3390/genes13030391
Stork CT, Bocek M, Crossley MP, et al.: Co-transcriptional R-loops are the main cause of estrogen-induced
DNA damage. Elife. 2016, 5:e17548. 10.7554/eLife.17548
Sattar S, Kabat J, Jerome K, Feldmann F, Bailey K, Mehedi M: Nuclear translocation of spike mRNA andprotein is a novel feature of SARS-CoV-2. Front Microbiol. 2023, 14:1073789. 10.3389/fmicb.2023.1073789
Singh N, Bharara Singh A: S2 subunit of SARS-nCoV-2 interacts with tumor suppressor protein p53 andBRCA: an in silico study. Transl Oncol. 2020, 13:100814. 10.1016/j.tranon.2020.100814
Force-facing our risk of cancer empowered: cancer risks associated with inherited mutations . (2023).
Accessed: March 28, 2024: https://www.facingourrisk.org/info/hereditary-cancer-and-genetictesting/hereditary-cancer-genes-and-risk/genes-by-na....
Ide H, Kotera M: Human DNA glycosylases involved in the repair of oxidatively damaged DNA . Biol Pharm
Bull. 2004, 27:480-5. 10.1248/bpb.27.480
Saito S, Koya Y, Kajiyama H, Yamashita M, Nawa A: Indoxyl sulfate promotes metastatic characteristics ofovarian cancer cells via aryl hydrocarbon receptor-mediated downregulation of the MAS receptor. Lab
Invest. 2023, 103:100025. 10.1016/j.labinv.2022.100025
Zhang L, Richards A, Barrasa MI, Hughes SH, Young RA, Jaenisch R: Reverse-transcribed SARS-CoV-2 RNA
can integrate into the genome of cultured human cells and can be expressed in patient-derived tissues. Proc
Natl Acad Sci U S A. 2021, 118:e2105968118. 10.1073/pnas.2105968118

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

17 of 18

96.

97.
98.

99.
100.
101.

Aldén M, Olofsson Falla F, Yang D, Barghouth M, Luan C, Rasmussen M, De Marinis Y: Intracellular reverse
transcription of Pfizer BioNTech COVID-19 mRNA vaccine BNT162b2 in vitro in human liver cell line. Curr
Issues Mol Biol. 2022, 44:1115-26. 10.3390/cimb44030073
Acevedo-Whitehouse K, Bruno R: Potential health risks of mRNA-based vaccine therapy: a hypothesis . Med
Hypotheses. 2023, 171:111015. 10.1016/j.mehy.2023.111015
FDA: Guidance for industry: characterization and qualification of cell substrates and other biologicalmaterials used in the production of viral vaccines for infectious disease indications. (2010). Accessed: March
7, 2024: https://www.fda.gov/media/78428/download.
[Digital agency: e-gov: Laws and Regulations Search (Special approval) Article 14-3] . (2024). Accessed:
March 28, 2024: https://elaws.e-gov.go.jp/document?lawid=335AC0000000145.
South Carolina Senate hearing - USC Professor Dr. Phillip Buckhaults . (2023). Accessed: March 7, 2024:
https://jessicar.substack.com/p/south-carolina-senate-hearing-usc.
Speicher D, Rose J, Gutschi LM, Wiseman DM, McKernan K: DNA fragments detected in monovalent andbivalent Pfizer/BioNTech and Moderna modRNA COVID-19 vaccines from Ontario, Canada: exploratory
dose response relationship with serious adverse events [PREPRINT]. OST Preprint. 2023,
10.31219/osf.io/mjc97

2024 Gibo et al. Cureus 16(4): e57860. DOI 10.7759/cureus.57860

18 of 18