bluuebunny/crossref_metadata_2025 huggingface dataset:
Supplementary Table S6 from Associations of race and ethnicity with hepatocellular carcinoma, decompensation, and mortality in US Veterans with cirrhosis
Authors: VoPham Trang , Cravero Anne , Feld Lauren D. , Green Pamela , Feng Ziding , Berry Kristin , Kim Nicole J. , Vutien Philip , Mendoza Jason A. , Ioannou George N.
Published: September 2024

Supplementary Table S6. Association between race/ethnicity and risk of all-cause mortality in patients with cirrhosis overall and by cirrhosis etiology: follow-up from cirrhosis diagnosis (2001-2014) to 2020
Supplementary Table S5 from Associations of race and ethnicity with hepatocellular carcinoma, decompensation, and mortality in US Veterans with cirrhosis
Authors: VoPham Trang , Cravero Anne , Feld Lauren D. , Green Pamela , Feng Ziding , Berry Kristin , Kim Nicole J. , Vutien Philip , Mendoza Jason A. , Ioannou George N.
Published: September 2024

Supplementary Table S5. Association between race/ethnicity and risk of cirrhosis decompensation in patients with cirrhosis overall and by cirrhosis etiology: follow-up from cirrhosis diagnosis (2001-2014) to 2020
Supplementary Table S3 from Associations of race and ethnicity with hepatocellular carcinoma, decompensation, and mortality in US Veterans with cirrhosis
Authors: VoPham Trang , Cravero Anne , Feld Lauren D. , Green Pamela , Feng Ziding , Berry Kristin , Kim Nicole J. , Vutien Philip , Mendoza Jason A. , Ioannou George N.
Published: September 2024

Supplementary Table S3. Association between race/ethnicity and risk of HCC in patients with cirrhosis overall and by cirrhosis etiology: follow-up from cirrhosis diagnosis (2001-2014) to 2020
Supplementary Table 5 from Sleep Behaviors, Genetic Predispositions, and Risk of Esophageal Cancer
Authors: Wang Xiaoyan , Tian Ruiyi , Zong Xiaoyu , Jeon Myung Sik , Luo Jingqin , Colditz Graham A. , Wang Jean S. , Tsilidis Konstantinos K. , Ju Yo-El S. , Govindan Ramaswamy , Puri Varun , Cao Yin
Published: September 2024

Supplementary Table 5 shows the association between weighted sleep score and EAC risk
Table S7 from Field synopsis of environmental and genetic risk factors of sporadic early-onset colorectal cancer and advanced adenoma
Authors: Zhang Rongqi , Boakye Daniel , Yang Nan , Zhou Xuan , Zhou Yajing , Jiang Fangyuan , Yu Lili , Wang Lijuan , Sun Jing , Yuan Shuai , Chen Jie , Hamilton Ashleigh C. , Coleman Helen G. , Larsson Susanna C. , Little Julian , Dunlop Malcolm G. , Giovannucci Edward L. , Theodoratou Evropi , Li Xue
Published: September 2024

Supplementary Table 7 shows the association between 55 SNPs and early-onset colorectal cancer from UK biobank.
Table S5 from Field synopsis of environmental and genetic risk factors of sporadic early-onset colorectal cancer and advanced adenoma
Authors: Zhang Rongqi , Boakye Daniel , Yang Nan , Zhou Xuan , Zhou Yajing , Jiang Fangyuan , Yu Lili , Wang Lijuan , Sun Jing , Yuan Shuai , Chen Jie , Hamilton Ashleigh C. , Coleman Helen G. , Larsson Susanna C. , Little Julian , Dunlop Malcolm G. , Giovannucci Edward L. , Theodoratou Evropi , Li Xue
Published: September 2024

Supplementary Table 5 shows the list of genes and variants and their associations with early-onset colorectal cancer.
Table S1 from Field synopsis of environmental and genetic risk factors of sporadic early-onset colorectal cancer and advanced adenoma
Authors: Zhang Rongqi , Boakye Daniel , Yang Nan , Zhou Xuan , Zhou Yajing , Jiang Fangyuan , Yu Lili , Wang Lijuan , Sun Jing , Yuan Shuai , Chen Jie , Hamilton Ashleigh C. , Coleman Helen G. , Larsson Susanna C. , Little Julian , Dunlop Malcolm G. , Giovannucci Edward L. , Theodoratou Evropi , Li Xue
Published: September 2024

Supplementary Table 1 shows basic characteristic of the included studies.
Supplementary Table 5 from Folate intake and ovarian cancer risk among women with endometriosis: a case-control study from the Ovarian Cancer Association Consortium
Authors: Gersekowski Kate , Ibiebele Torukiri I. , Doherty Jennifer A. , Harris Holly R. , Goodman Marc T. , Terry Kathryn L. , Wu Anna H. , Bandera Elisa V. , Qin Bo , Ong Jue-Sheng , Tyrer Jonathan P. , Dixon-Suen Suzanne C. , Modugno Francesmary , Risch Harvey A. , Webb Penelope M.
Published: September 2024

Supplementary Table 5 shows the results from the different Mendelian randomization methods investigating the association between genetically predicted folate and risk of ovarian cancer, by endometriosis status
Supplementary Table 4 from Folate intake and ovarian cancer risk among women with endometriosis: a case-control study from the Ovarian Cancer Association Consortium
Authors: Gersekowski Kate , Ibiebele Torukiri I. , Doherty Jennifer A. , Harris Holly R. , Goodman Marc T. , Terry Kathryn L. , Wu Anna H. , Bandera Elisa V. , Qin Bo , Ong Jue-Sheng , Tyrer Jonathan P. , Dixon-Suen Suzanne C. , Modugno Francesmary , Risch Harvey A. , Webb Penelope M.
Published: September 2024

Supplementary Table 4 shows the association between glycemic index, glycemic load, grain intake and risk of ovarian cancer for women with and without endometriosis.
Supplementary Table 3 from Folate intake and ovarian cancer risk among women with endometriosis: a case-control study from the Ovarian Cancer Association Consortium
Authors: Gersekowski Kate , Ibiebele Torukiri I. , Doherty Jennifer A. , Harris Holly R. , Goodman Marc T. , Terry Kathryn L. , Wu Anna H. , Bandera Elisa V. , Qin Bo , Ong Jue-Sheng , Tyrer Jonathan P. , Dixon-Suen Suzanne C. , Modugno Francesmary , Risch Harvey A. , Webb Penelope M.
Published: September 2024

Supplementary Table 3 shows the associations between folate intake (dietary, supplemental, total) and ovarian cancer by endometriosis status, and by histological subtype (endometrioid/clear cell cancers and high grade serous cancers).
Supplementary Figure 3 from Folate intake and ovarian cancer risk among women with endometriosis: a case-control study from the Ovarian Cancer Association Consortium
Authors: Gersekowski Kate , Ibiebele Torukiri I. , Doherty Jennifer A. , Harris Holly R. , Goodman Marc T. , Terry Kathryn L. , Wu Anna H. , Bandera Elisa V. , Qin Bo , Ong Jue-Sheng , Tyrer Jonathan P. , Dixon-Suen Suzanne C. , Modugno Francesmary , Risch Harvey A. , Webb Penelope M.
Published: September 2024

Supplementary Figure 3 shows scatter plots with the genetic association with folate on the x-axis and
the genetic association with ovarian cancer on the y-axis, for (A) women with and (B) without
endometriosis. The regression line for the inverse variance weighted Mendelian randomization
method is shown.
Table S1 from Associations between etiologic or prognostic tumor tissue markers and neighborhood contextual factors in male health professionals diagnosed with prostate cancer
Authors: Iyer Hari S. , Kensler Kevin H. , Vaselkiv Jane B. , Stopsack Konrad H. , Roscoe Charlotte , Bandera Elisa V. , Qin Bo , Jang Thomas L. , Lotan Tamara L. , James Peter , Hart Jaime E. , Mucci Lorelei A. , Laden Francine , Rebbeck Timothy R.
Published: September 2024

Bivariate Associations Between Tertiles of Neighborhood Contextual Factors at Diagnosis and Somatic Alterations in Prostate Tumor Tissue in US Male Health Professionals with Prostate Cancer, 1986-2009
Supplementary Table S3 from Pre-diagnostic 25-hydroxyvitamin D in association with cancer specific- and all-cause mortality among low income, non-Hispanic Black Americans with colorectal cancer
Authors: Lawler Thomas P. , Khankari Nikhil K. , Shu Xiao-Ou , Steinwandel Mark , Cai Qiuyin , Jurutka Peter W. , Zheng Wei , Warren Andersen Shaneda
Published: September 2024

Supplementary Table S3: Power analyses for Cox-proportional hazards models concerning the associations between serum 25-hydroxyvitamin D and mortality outcomes, with adjustment for mortality risk factors
Figure SF2 from A review of the application of spatial survival methods in cancer research: trends, modelling and visualization techniques
Authors: Bizuayehu Habtamu Mellie , Cameron Jessica K. , Dasgupta Paramita , Baade Peter D.
Published: September 2024

Supplementary Figure S2 shows article’s study period in years
Supplemental Table 7 from Elevated EVL methylation level in the normal colon mucosa is a potential risk biomarker for developing recurrent adenomas
Authors: Yu Ming , Carter Kelly T. , Baker Kelsey K. , Redman Mary W. , Wang Ting , Vickers Kathy , Li Christopher I. , Cohen Stacey A. , Krane Mukta , Ose Jennifer , Gigic Biljana , Figueiredo Jane C. , Toriola Adetunji T. , Siegel Erin M. , Shibata David , Schneider Martin , Ulrich Cornelia M. , Dzubinski Lynda Ann. , Schoen Robert E. , Grady William M.
Published: September 2024

Supplemental Table 7: Summary statistics for methylated EVL measurements in Comparison Set 3
Supplemental Table 4 from Elevated EVL methylation level in the normal colon mucosa is a potential risk biomarker for developing recurrent adenomas
Authors: Yu Ming , Carter Kelly T. , Baker Kelsey K. , Redman Mary W. , Wang Ting , Vickers Kathy , Li Christopher I. , Cohen Stacey A. , Krane Mukta , Ose Jennifer , Gigic Biljana , Figueiredo Jane C. , Toriola Adetunji T. , Siegel Erin M. , Shibata David , Schneider Martin , Ulrich Cornelia M. , Dzubinski Lynda Ann. , Schoen Robert E. , Grady William M.
Published: September 2024

Supplemental Table 4: Summary patient characteristics for Comparison Set 3
Supplemental Figure 2 from Elevated EVL methylation level in the normal colon mucosa is a potential risk biomarker for developing recurrent adenomas
Authors: Yu Ming , Carter Kelly T. , Baker Kelsey K. , Redman Mary W. , Wang Ting , Vickers Kathy , Li Christopher I. , Cohen Stacey A. , Krane Mukta , Ose Jennifer , Gigic Biljana , Figueiredo Jane C. , Toriola Adetunji T. , Siegel Erin M. , Shibata David , Schneider Martin , Ulrich Cornelia M. , Dzubinski Lynda Ann. , Schoen Robert E. , Grady William M.
Published: September 2024

Supplemental Figure 2: Methylation levels of normal colon mucosa in patients with no history of adenoma or cancer ever (group on left) and levels for
patients with a history of adenoma(s) or colorectal cancer at initial or a follow-up colonoscopy (group on right) in Comparison Set 1.
%mEVL=percentage of DNA that is methylated at the EVL locus assayed.
Supplementary Table S6 from Development of a breast cancer risk prediction model incorporating polygenic risk scores and non-genetic risk factors for Korean women
Authors: Choi Jihye , Ha Tae-woong , Choi Hye-Mi , Lee Han-Byoel , Shin Hee-Chul , Chung Woosung , Han Wonshik
Published: September 2024

Mean, SD of the multiple PRS (PRS38_ASN+PRS190_EB) and the integrated model (PRS38_ASN+PRS190_EB+NGRF) at age 80, stratified by percentiles
Supplementary Table S3 from Development of a breast cancer risk prediction model incorporating polygenic risk scores and non-genetic risk factors for Korean women
Authors: Choi Jihye , Ha Tae-woong , Choi Hye-Mi , Lee Han-Byoel , Shin Hee-Chul , Chung Woosung , Han Wonshik
Published: September 2024

Relative risk and population distribution of NGRFs for breast cancer in Korean women
Supplementary Table 3 from Sex-specific associations between body mass index and thyroid cancer incidence among Korean adults
Authors: Kim Kyoung-Nam , Kim Kyungsik , Lee Sangjun , Park Sue K.
Published: September 2024

Supplementary Table 3 shows the associations between body mass index (BMI) and thyroid cancer incidence in the Korean Multi-center Cancer Cohort (KMCC) using the Western BMI cutoff
Supplementary Table 6 from Relationship between ABO blood group alleles and pancreatic cancer is modulated by secretor (FUT2) genotype, but not Lewis antigen (FUT3) genotype
Authors: Kim Jihye , Yuan Chen , Amundadottir Laufey T. , Wolpin Brian M. , Klein Alison P. , Risch Harvey A. , Kraft Peter
Published: September 2024

Table S6 presents joint analysis results of secretor status and Lewis antigen in the relationship with pancreatic cancer risk, stratified by O and non-O blood groups.
Supplementary Table 2 from Relationship between ABO blood group alleles and pancreatic cancer is modulated by secretor (FUT2) genotype, but not Lewis antigen (FUT3) genotype
Authors: Kim Jihye , Yuan Chen , Amundadottir Laufey T. , Wolpin Brian M. , Klein Alison P. , Risch Harvey A. , Kraft Peter
Published: September 2024

Table S2 lists eight SNPs that characterize ABO blood groups, secretor status, and Lewis antigen.
Supplementary Table 1 from Relationship between ABO blood group alleles and pancreatic cancer is modulated by secretor (FUT2) genotype, but not Lewis antigen (FUT3) genotype
Authors: Kim Jihye , Yuan Chen , Amundadottir Laufey T. , Wolpin Brian M. , Klein Alison P. , Risch Harvey A. , Kraft Peter
Published: September 2024

Table S1 presents the haplotype and genotype frequencies of ABO blood alleles, secretor status, and Lewis antigen groups.
Supplementary Table S2 from Urine pH and Risk of Bladder Cancer in Northern New England
Authors: Fischer Alexander H. , Wong Jason YY. , Baris Dalsu , Koutros Stella , Karagas Margaret R. , Schwenn Molly , Johnson Alison , Alguacil Juan , Silverman Debra T. , Rothman Nathaniel
Published: September 2024

Supplementary Table S2 shows urine pH and bladder cancer risk stratified by never-, former-, and current-smokers.
Supplementary Table 1 from Sources of disparities in surveillance mammography performance and risk-guided recommendations for supplemental breast imaging: A simulation study
Authors: Hubbard Rebecca A. , Pujol Toyya A. , Alhajjar Elie , Edoh Kossi , Martin Melissa L.
Published: September 2024

Supplementary Table 1. Distributional assumptions and input parameter values for simulations. Model 1 includes no race and ethnicity variation, Model 2 includes variation in primary cancer and treatment characteristics by race and ethnicity, Model 3 includes variation in SDOH by race and ethnicity, Models 4 and 5 include variation in primary cancer and treatment characteristics as well as SDOH by race and ethnicity.}
Supplementary Figure 3 from Sources of disparities in surveillance mammography performance and risk-guided recommendations for supplemental breast imaging: A simulation study
Authors: Hubbard Rebecca A. , Pujol Toyya A. , Alhajjar Elie , Edoh Kossi , Martin Melissa L.
Published: September 2024

Supplementary Figure 3. Simulation results for surveillance mammography positive predictive value overall and stratified by race and ethnicity in simulated populations of women with a personal history of breast cancer
Supplementary Figure 2 from Sources of disparities in surveillance mammography performance and risk-guided recommendations for supplemental breast imaging: A simulation study
Authors: Hubbard Rebecca A. , Pujol Toyya A. , Alhajjar Elie , Edoh Kossi , Martin Melissa L.
Published: September 2024

Supplementary Figure 2. Simulation results for surveillance mammography cancer detection rates overall and stratified by race and ethnicity in simulated populations of women with a personal history of breast cancer
Supplementary Figure 1 from Sources of disparities in surveillance mammography performance and risk-guided recommendations for supplemental breast imaging: A simulation study
Authors: Hubbard Rebecca A. , Pujol Toyya A. , Alhajjar Elie , Edoh Kossi , Martin Melissa L.
Published: September 2024

Supplementary Figure 1. Simulation results for surveillance mammography false-negative rates overall and stratified by race and ethnicity in simulated populations of women with a personal history of breast cancer
Supplementary Table S9 from Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer: Mendelian randomization
Authors: King Sontoria D. , Veliginti Swathi , Brouwers Martijn C G J. , Ren Zhewen , Zheng Wei , Setiawan Veronica Wendy. , Wilkens Lynne R. , Shu Xiao-Ou , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Canzian Federico , Du Mengmeng , Gallinger Steven J. , Giles Graham G. , Goodman Phyllis J. , Haiman Christopher A. , Kogevinas Manolis , Kooperberg Charles , Le Marchand Loic , Neale Rachel E. , Visvanathan Kala , White Emily , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Berndt Sonja I. , Brais Lauren K. , Brennan Paul , Buring Julie E. , Rabe Kari G. , Bamlet William R. , Chanock Stephen J. , Fuchs Charles S. , Gaziano J. Michael , Giovannucci Edward L. , Hackert Thilo , Hassan Manal M. , Katzke Verena , Kurtz Robert C. , Lee I-Min , Malats Nuria , Murphy Neil , Oberg Ann L. , Orlow Irene , Porta Miquel , Real Francisco X. , Rothman Nathaniel , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Wactawski-Wende Jean , Wang Xiaoliang , Wentzensen Nicolas , Yu Herbert , Zeleniuch-Jacquotte Anne , Yu Kai , Wolpin Brian M. , Duell Eric J. , Li Donghui , Hung Rayjean J. , Perdomo Sandra , McCullough Marjorie L. , Freedman Neal D. , Patel Alpa V. , Peters Ulrike , Riboli Elio , Sund Malin , Tjønneland Anne , Zhong Jun , Van Den Eeden Stephen K. , Kraft Peter , Risch Harvey A. , Amundadottir Laufey T. , Klein Alison P. , Stolzenberg-Solomon Rachael Z. , Antwi Samuel O.
Published: September 2024

Supplementary Table S9 shows descriptive Statistics for PanScan sample
Supplementary Table S8 from Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer: Mendelian randomization
Authors: King Sontoria D. , Veliginti Swathi , Brouwers Martijn C G J. , Ren Zhewen , Zheng Wei , Setiawan Veronica Wendy. , Wilkens Lynne R. , Shu Xiao-Ou , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Canzian Federico , Du Mengmeng , Gallinger Steven J. , Giles Graham G. , Goodman Phyllis J. , Haiman Christopher A. , Kogevinas Manolis , Kooperberg Charles , Le Marchand Loic , Neale Rachel E. , Visvanathan Kala , White Emily , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Berndt Sonja I. , Brais Lauren K. , Brennan Paul , Buring Julie E. , Rabe Kari G. , Bamlet William R. , Chanock Stephen J. , Fuchs Charles S. , Gaziano J. Michael , Giovannucci Edward L. , Hackert Thilo , Hassan Manal M. , Katzke Verena , Kurtz Robert C. , Lee I-Min , Malats Nuria , Murphy Neil , Oberg Ann L. , Orlow Irene , Porta Miquel , Real Francisco X. , Rothman Nathaniel , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Wactawski-Wende Jean , Wang Xiaoliang , Wentzensen Nicolas , Yu Herbert , Zeleniuch-Jacquotte Anne , Yu Kai , Wolpin Brian M. , Duell Eric J. , Li Donghui , Hung Rayjean J. , Perdomo Sandra , McCullough Marjorie L. , Freedman Neal D. , Patel Alpa V. , Peters Ulrike , Riboli Elio , Sund Malin , Tjønneland Anne , Zhong Jun , Van Den Eeden Stephen K. , Kraft Peter , Risch Harvey A. , Amundadottir Laufey T. , Klein Alison P. , Stolzenberg-Solomon Rachael Z. , Antwi Samuel O.
Published: September 2024

Supplementary Table S8 shows results of univariate analysis for 17 imaging and biopsy validated SNPs in the PanC4 sample
Supplementary Table S6 from Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer: Mendelian randomization
Authors: King Sontoria D. , Veliginti Swathi , Brouwers Martijn C G J. , Ren Zhewen , Zheng Wei , Setiawan Veronica Wendy. , Wilkens Lynne R. , Shu Xiao-Ou , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Canzian Federico , Du Mengmeng , Gallinger Steven J. , Giles Graham G. , Goodman Phyllis J. , Haiman Christopher A. , Kogevinas Manolis , Kooperberg Charles , Le Marchand Loic , Neale Rachel E. , Visvanathan Kala , White Emily , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Berndt Sonja I. , Brais Lauren K. , Brennan Paul , Buring Julie E. , Rabe Kari G. , Bamlet William R. , Chanock Stephen J. , Fuchs Charles S. , Gaziano J. Michael , Giovannucci Edward L. , Hackert Thilo , Hassan Manal M. , Katzke Verena , Kurtz Robert C. , Lee I-Min , Malats Nuria , Murphy Neil , Oberg Ann L. , Orlow Irene , Porta Miquel , Real Francisco X. , Rothman Nathaniel , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Wactawski-Wende Jean , Wang Xiaoliang , Wentzensen Nicolas , Yu Herbert , Zeleniuch-Jacquotte Anne , Yu Kai , Wolpin Brian M. , Duell Eric J. , Li Donghui , Hung Rayjean J. , Perdomo Sandra , McCullough Marjorie L. , Freedman Neal D. , Patel Alpa V. , Peters Ulrike , Riboli Elio , Sund Malin , Tjønneland Anne , Zhong Jun , Van Den Eeden Stephen K. , Kraft Peter , Risch Harvey A. , Amundadottir Laufey T. , Klein Alison P. , Stolzenberg-Solomon Rachael Z. , Antwi Samuel O.
Published: September 2024

Supplementary Table S6 shows results of univariate analysis for 22 imaging defined nonalcoholic fatty liver disease SNPs in the PanC4 sample
Supplementary Table S4 from Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer: Mendelian randomization
Authors: King Sontoria D. , Veliginti Swathi , Brouwers Martijn C G J. , Ren Zhewen , Zheng Wei , Setiawan Veronica Wendy. , Wilkens Lynne R. , Shu Xiao-Ou , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Canzian Federico , Du Mengmeng , Gallinger Steven J. , Giles Graham G. , Goodman Phyllis J. , Haiman Christopher A. , Kogevinas Manolis , Kooperberg Charles , Le Marchand Loic , Neale Rachel E. , Visvanathan Kala , White Emily , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Berndt Sonja I. , Brais Lauren K. , Brennan Paul , Buring Julie E. , Rabe Kari G. , Bamlet William R. , Chanock Stephen J. , Fuchs Charles S. , Gaziano J. Michael , Giovannucci Edward L. , Hackert Thilo , Hassan Manal M. , Katzke Verena , Kurtz Robert C. , Lee I-Min , Malats Nuria , Murphy Neil , Oberg Ann L. , Orlow Irene , Porta Miquel , Real Francisco X. , Rothman Nathaniel , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Wactawski-Wende Jean , Wang Xiaoliang , Wentzensen Nicolas , Yu Herbert , Zeleniuch-Jacquotte Anne , Yu Kai , Wolpin Brian M. , Duell Eric J. , Li Donghui , Hung Rayjean J. , Perdomo Sandra , McCullough Marjorie L. , Freedman Neal D. , Patel Alpa V. , Peters Ulrike , Riboli Elio , Sund Malin , Tjønneland Anne , Zhong Jun , Van Den Eeden Stephen K. , Kraft Peter , Risch Harvey A. , Amundadottir Laufey T. , Klein Alison P. , Stolzenberg-Solomon Rachael Z. , Antwi Samuel O.
Published: September 2024

Supplementary Table S4 shows results of univariate analysis for 17 imaging and biopsy validated SNPs in the PanScan sample
Supplementary Figure S1 from Genetic susceptibility to nonalcoholic fatty liver disease and risk for pancreatic cancer: Mendelian randomization
Authors: King Sontoria D. , Veliginti Swathi , Brouwers Martijn C G J. , Ren Zhewen , Zheng Wei , Setiawan Veronica Wendy. , Wilkens Lynne R. , Shu Xiao-Ou , Arslan Alan A. , Beane Freeman Laura E. , Bracci Paige M. , Canzian Federico , Du Mengmeng , Gallinger Steven J. , Giles Graham G. , Goodman Phyllis J. , Haiman Christopher A. , Kogevinas Manolis , Kooperberg Charles , Le Marchand Loic , Neale Rachel E. , Visvanathan Kala , White Emily , Albanes Demetrius , Andreotti Gabriella , Babic Ana , Berndt Sonja I. , Brais Lauren K. , Brennan Paul , Buring Julie E. , Rabe Kari G. , Bamlet William R. , Chanock Stephen J. , Fuchs Charles S. , Gaziano J. Michael , Giovannucci Edward L. , Hackert Thilo , Hassan Manal M. , Katzke Verena , Kurtz Robert C. , Lee I-Min , Malats Nuria , Murphy Neil , Oberg Ann L. , Orlow Irene , Porta Miquel , Real Francisco X. , Rothman Nathaniel , Sesso Howard D. , Silverman Debra T. , Thompson Ian M. , Wactawski-Wende Jean , Wang Xiaoliang , Wentzensen Nicolas , Yu Herbert , Zeleniuch-Jacquotte Anne , Yu Kai , Wolpin Brian M. , Duell Eric J. , Li Donghui , Hung Rayjean J. , Perdomo Sandra , McCullough Marjorie L. , Freedman Neal D. , Patel Alpa V. , Peters Ulrike , Riboli Elio , Sund Malin , Tjønneland Anne , Zhong Jun , Van Den Eeden Stephen K. , Kraft Peter , Risch Harvey A. , Amundadottir Laufey T. , Klein Alison P. , Stolzenberg-Solomon Rachael Z. , Antwi Samuel O.
Published: September 2024

Supplementary Figure S1 shows forest plot for minimally adjusted model for the PanScan sample.
Supplementary Table 3 from Intake of Proton-Pump Inhibitors and Gastric Cancer Within the Stomach cancer Pooling (StoP) Project
Authors: Sassano Michele , Mariani Marco , Pelucchi Claudio , Lunet Nuno , Morais Samantha , Martín Vicente , Moreno Victor , Curado Maria Paula. , Dias-Neto Emmanuel , Leja Marcis , Gašenko Evita , La Vecchia Carlo , Boccia Stefania , Pastorino Roberta
Published: September 2024

Supplementary Table 3: History of exposure to proton-pump inhibitors (PPIs) among cases and controls, according to different exposure definitions.
Supplementary Table 2 from Intake of Proton-Pump Inhibitors and Gastric Cancer Within the Stomach cancer Pooling (StoP) Project
Authors: Sassano Michele , Mariani Marco , Pelucchi Claudio , Lunet Nuno , Morais Samantha , Martín Vicente , Moreno Victor , Curado Maria Paula. , Dias-Neto Emmanuel , Leja Marcis , Gašenko Evita , La Vecchia Carlo , Boccia Stefania , Pastorino Roberta
Published: September 2024

Supplementary Table 2: Questionnaire items used to assess exposure for each study included in the analysis.
Supplementary Table 1 from Intake of Proton-Pump Inhibitors and Gastric Cancer Within the Stomach cancer Pooling (StoP) Project
Authors: Sassano Michele , Mariani Marco , Pelucchi Claudio , Lunet Nuno , Morais Samantha , Martín Vicente , Moreno Victor , Curado Maria Paula. , Dias-Neto Emmanuel , Leja Marcis , Gašenko Evita , La Vecchia Carlo , Boccia Stefania , Pastorino Roberta
Published: September 2024

Supplementary Table 1: Main characteristics of the studies included in the analysis.
Figure S5 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Figure S5 showing the QQ and Manhattan plots of conventional GWAS on the GECCO and CCFR datasets
Figure S4 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Figure S4 shows how DEPTH common risk regions are defined.
Figure S3 from Using DEPendency of association on the number of Top Hits (DEPTH) as a complementary tool to identify novel colorectal cancer susceptibility loci.
Authors: Lai John , Wong Chi Kuen , Schmidt Daniel F. , Kapuscinski Miroslaw K. , Alpen Karen , Maclnnis Robert J. , Buchanan Daniel D. , Win Aung K. , Figueiredo Jane C. , Chan Andrew T. , Harrison Tabitha A. , Hoffmeister Michael , White Emily , Le Marchand Loic , Pai Rish K. , Peters Ulrike , Hopper John L. , Jenkins Mark A. , Makalic Enes
Published: September 2024

Supplementary Figure S3 shows further filtering of SNPs that had greater than 20% difference in odds ratio or P values when comparing odds ratios and P values before and after adjusting for the first four principal components.
Supplementary Table 2 from Hospitalisations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: a Longitudinal Matched Cohort Study.
Authors: Abdalla Tasnim , Walwyn Thomas , White Daniel , Choong Catherine S. , Bulsara Max , Preen David B. , Ohan Jeneva L.
Published: September 2024

Multivariable adjusted hazard ratios for all-cause and cause-specific hospitalisations, major cancer diagnostic group.
Supplementary Figure 1 from Hospitalisations and Cost of Inpatient Care for Physical Diseases in Survivors of Childhood Cancer in Western Australia: a Longitudinal Matched Cohort Study.
Authors: Abdalla Tasnim , Walwyn Thomas , White Daniel , Choong Catherine S. , Bulsara Max , Preen David B. , Ohan Jeneva L.
Published: September 2024

The mean cumulative count of hospitalisations for any physical-health disease by time since index cancer diagnosis, in childhood cancer survivors.
Supplementary Table S1 from HSA Adductomics Reveals Sex Differences in NHL Incidence and Possible Involvement of Microbial Translocation
Authors: Grigoryan Hasmik , Imani Partow , Sacerdote Carlotta , Masala Giovanna , Grioni Sara , Tumino Rosario , Chiodini Paolo , Dudoit Sandrine , Vineis Paolo , Rappaport Stephen M.
Published: September 2024

Supplementary Table S1: Cancer types and morphology codes
Supplementary Methods S1 from HSA Adductomics Reveals Sex Differences in NHL Incidence and Possible Involvement of Microbial Translocation
Authors: Grigoryan Hasmik , Imani Partow , Sacerdote Carlotta , Masala Giovanna , Grioni Sara , Tumino Rosario , Chiodini Paolo , Dudoit Sandrine , Vineis Paolo , Rappaport Stephen M.
Published: September 2024

Supplementary Methods S1: Detailed nLC-HRMSMS methods
Table S5 from Predicted proteome association studies of breast, prostate, ovarian, and endometrial cancers implicate plasma protein regulation in cancer susceptibility
Authors: Gregga Isabelle , Pharoah Paul DP. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: September 2024

Table S5. PhenomeXcan TWAS results for 8 genes that had cancer associations.
Table S4 from Predicted proteome association studies of breast, prostate, ovarian, and endometrial cancers implicate plasma protein regulation in cancer susceptibility
Authors: Gregga Isabelle , Pharoah Paul DP. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: September 2024

Table S4. PWAS meta-FDR<0.05 proteins with endometrial cancer associations.
Table S2 from Predicted proteome association studies of breast, prostate, ovarian, and endometrial cancers implicate plasma protein regulation in cancer susceptibility
Authors: Gregga Isabelle , Pharoah Paul DP. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: September 2024

Table S2. Replication of cancer PWAS results in independent cohort with meta-analysis FDR<0.05.
Table S1 from Predicted proteome association studies of breast, prostate, ovarian, and endometrial cancers implicate plasma protein regulation in cancer susceptibility
Authors: Gregga Isabelle , Pharoah Paul DP. , Gayther Simon A. , Manichaikul Ani , Im Hae Kyung , Kar Siddhartha P. , Schildkraut Joellen M. , Wheeler Heather E.
Published: September 2024

Table S1. Summary statistics of 7 cancer types tested with ARIC plasma proteome models, FDR<0.05, with single- and multi-variant COLOC results.
Supplementary Figure S5 from Characteristics of Cancer Epidemiology Studies that Employ Metabolomics: A Scoping Review
Authors: Yu Catherine T. , Farhat Zeinab , Livinski Alicia A. , Loftfield Erikka , Zanetti Krista A.
Published: September 2024

Supplementary Figure S5 shows scatterplot illustrating 80 cancer-metabolomics primary analyses from 77 studies. Studies that had multiple cancer outcomes were considered separately in the analysis.
Supplementary Figure S1 from Characteristics of Cancer Epidemiology Studies that Employ Metabolomics: A Scoping Review
Authors: Yu Catherine T. , Farhat Zeinab , Livinski Alicia A. , Loftfield Erikka , Zanetti Krista A.
Published: September 2024

Supplementary Figure S1 shows metabolomic epidemiology studies of cancer published from January 1998 to June 2021.
Supplemental Table 2 from Cigarette and alternative tobacco product use among adult cancer survivors enrolled in 9 ECOG-ACRIN clinical trials
Authors: Streck Joanna M. , Lee Ju-Whei , Walter Angela Wangari. , Rosen Rachel L. , Gareen Ilana F. , Kircher Sheetal Mehta. , Herman Benjamin A. , Carlos Ruth C. , Kumar Shaji , Mayer Ingrid A. , Saba Nabil F. , Fenske Timothy S. , Neal Joel W. , Atkins Michael B. , Hodi F Stephen. , Kyriakopoulos Christos E. , Tempany Clare , Shanafelt Tait D. , Wagner Lynne I. , Land Stephanie R. , Park Elyse R. , Ostroff Jamie S.
Published: September 2024

Supplemental Table 2 shows associations of baseline continuous participant characteristics with past 30-day tobacco product use
Supplementary Table 2 from Patterns of evidence-based care for the diagnosis, staging, and first-line treatment of breast cancer by race-ethnicity: a SEER-Medicare study
Authors: Herbach Emma L. , Nash Sarah H. , Lizarraga Ingrid M. , Carnahan Ryan M. , Wang Kai , Ogilvie Amy C. , Curran Michaela , Charlton Mary E.
Published: September 2024

Supplementary Table 2 shows crude and adjusted rate ratios for all applicable outcomes stratified by stage at diagnosis.
Supplementary Figure 1 from Patterns of evidence-based care for the diagnosis, staging, and first-line treatment of breast cancer by race-ethnicity: a SEER-Medicare study
Authors: Herbach Emma L. , Nash Sarah H. , Lizarraga Ingrid M. , Carnahan Ryan M. , Wang Kai , Ogilvie Amy C. , Curran Michaela , Charlton Mary E.
Published: September 2024

Supplementary Figure 1 describes the process for combining multiple data sources to create a race-ethnicity variable using the best available (and most accurate) data.
Supplementary Table S2 from Socioeconomic status at birth and breast tissue composition in adolescence and adulthood
Authors: Kehm Rebecca D. , Lilge Lothar , Walter E Jane , White Melissa , Herbstman Julie B. , Perera Frederica P. , Miller Rachel L. , Terry Mary Beth , Tehranifar Parisa
Published: September 2024

Supplementary Table S2 shows the characteristics of adolescent girls by maternal education at birth in the Columbia Breast Cancer and the Environment Research Program (Columbia-BCERP) Study, n = 165.
Supplementary Table S3 from Racial and ethnic variation in diagnostic mammography performance among women reporting a breast lump
Authors: Nyante Sarah J. , Abraham Linn , Aiello Bowles Erin J. , Lee Christoph I. , Kerlikowske Karla , Miglioretti Diana L. , Sprague Brian L. , Henderson Louise M.
Published: September 2024

Tabulation of the types of false-negative cancers diagnosed
Supplementary Table S1 from Racial and ethnic variation in diagnostic mammography performance among women reporting a breast lump
Authors: Nyante Sarah J. , Abraham Linn , Aiello Bowles Erin J. , Lee Christoph I. , Kerlikowske Karla , Miglioretti Diana L. , Sprague Brian L. , Henderson Louise M.
Published: September 2024

Raw numbers for performance statistic calculations
Supplementary Table 6 from Body Composition, Relative Dose Intensity, and Adverse Events Among Patients with Colon Cancer
Authors: Cheng En , Caan Bette J. , Cawthon Peggy M. , Evans William J. , Hellerstein Marc K. , Shankaran Mahalakshmi , Nyangau Edna , Campbell Kristin L. , Lee Catherine , Binder Alexandra M. , Meyerhardt Jeffrey A. , Schmitz Kathryn H. , Cespedes Feliciano Elizabeth M.
Published: September 2024

Supplementary Table 6. Sex-Specific Adjusted Associations of Body Composition Measurements (Per SD Increase) with Reduced RDI and Relative Changes (%) in the Number of Moderate and Severe Adverse Events
Supplementary Table 2 from Association between outdoor light at night and prostate cancer in the Health Professionals Follow-Up Study
Authors: Chowdhury-Paulino Ilkania M. , Hart Jaime E. , James Peter , Iyer Hari S. , Wilt Grete E. , Booker Benjamin D. , Nethery Rachel C. , Laden Francine , Mucci Lorelei A. , Markt Sarah C.
Published: September 2024

Supplementary Table 2. Hazard ratios and 95% confidence intervals for the association between outdoor light at night (LAN) and lethal prostate cancer incidence in HPFS 1986-2016, stratified by urbanicity (urban: >1,000 people/mi2, nonurban: <1,000 people/mi2), PSA screening intensity, and neighborhood socioeconomic status
Supplementary Table 1 from Association between outdoor light at night and prostate cancer in the Health Professionals Follow-Up Study
Authors: Chowdhury-Paulino Ilkania M. , Hart Jaime E. , James Peter , Iyer Hari S. , Wilt Grete E. , Booker Benjamin D. , Nethery Rachel C. , Laden Francine , Mucci Lorelei A. , Markt Sarah C.
Published: September 2024

Supplementary Table 1. Hazard ratios and 95% confidence intervals for the association between cumulative average outdoor light at night (LAN) and lethal prostate cancer risk in HPFS (1986-2016)
Supplementary Figure 1 from Association between outdoor light at night and prostate cancer in the Health Professionals Follow-Up Study
Authors: Chowdhury-Paulino Ilkania M. , Hart Jaime E. , James Peter , Iyer Hari S. , Wilt Grete E. , Booker Benjamin D. , Nethery Rachel C. , Laden Francine , Mucci Lorelei A. , Markt Sarah C.
Published: September 2024

Supplementary Figure 1. Cubic splines to check for nonlinearity (A) Spline for total prostate cancer; (B) Spline for fatal prostate cancer
Figure S1 from Associations between ambient air pollutants and clonal hematopoiesis of indeterminate potential (CHIP)
Authors: Leiser Claire L. , Whitsel Eric A. , Reiner Alexander , Rich Stephen S. , Rotter Jerome I. , Taylor Kent D. , Tracy Russel P. , Kooperberg Charles , Smith Albert Vernon , Mason JoAnn E. , Mychaleckyj Josyf C. , Bick Alexander G. , Szpiro Adam A. , Kaufman Joel D.
Published: September 2024

Figure S1 shows the sample selection for the WHI and MESA cohorts in TOPMed.
Supplementary Figure S5 from Prediagnostic prescription antibiotics use and survival in colorectal cancer patients: A Swedish national register-based study
Authors: Lu Sai San Moon , Rutegård Martin , Ahmed Maghfoor , Häggström Christel , Gylfe Åsa , Harlid Sophia , Van Guelpen Bethany
Published: September 2024

Supplementary Figure S5: Antibiotics use in relation to overall and cancer-specific survival in stage I-III colorectal cancer patients by
antibiotics classes, typical indications and specific antibiotics drugs.
The hazards ratios (HR) are derived from the Cox proportional hazards models, stratified by tumour sites (proximal
colon/distal colon/rectum/unspecified colorectum categories) and tumour stage (stage I-III), age at diagnosis (</≥
50 years), prediagnostic Charlson comorbidity index (no/any CCI), family history of colorectal cancer (0/1/≥2 first
degree relatives ), and adjusted for: age and calendar year of diagnosis and prediagnostic Charlson comorbidity
index as continuous variables, and sex, place of birth, county of residence, marital status, level of education and
income status as categorical variables, defined as presented in Table 1. Antibiotics use during one year before
colorectal cancer diagnosis was excluded to account for potential use due to undiagnosed but clinically manifest
colorectal cancer. Any use of specific antibiotics class was compared with no use of the specific antibiotics class
(reference category). Antibiotics that primarily or only affect aerobic bacteria were categorized as aerobic
antibiotics. Antibiotics with effect on both anaerobic and aerobic bacteria, and metrodinazoles and/or tinidazoles
(which only affect anaerobic bacteria) were categorized as anti-anaerobic antibiotics. Phenoxymethylpenicillin is
presented as a specific antibiotic drug but was also the sole typical drug for the indication uncomplicated
respiratory infections and some skin and soft tissue infections. Clindamycin is presented as a specific antibiotic
drug but was also the sole typical drug for the indication complicated skin and soft tissue and deep-seated
infections. Amoxicillin includes amoxicillin with beta-lactamase inhibitor. UTI = urinary tract infection, CI =
confidence interval. Detailed antibiotics classification and typical indications are presented in Supplementary
Table S1.
Supplementary Figure S4 from Prediagnostic prescription antibiotics use and survival in colorectal cancer patients: A Swedish national register-based study
Authors: Lu Sai San Moon , Rutegård Martin , Ahmed Maghfoor , Häggström Christel , Gylfe Åsa , Harlid Sophia , Van Guelpen Bethany
Published: September 2024

Supplementary Figure S4: Antibiotics use in relation to survival outcomes in stage I-III colorectal cancer (crude and completecase
multivariable analyses).
(A) Antibiotics use in relation to overall survival. (B) Antibiotics use in relation to cancer-specific survival.
Prediagnostic antibiotics use was categorized as no use, any use, low use (1-10 days), moderate use (11-60
days), high use (61-180 days) and very high use (>180 days), based on defined daily doses. Antibiotics use
during one year before colorectal cancer diagnosis was excluded to account for potential use due to
undiagnosed but clinically manifest colorectal cancer. CI = confidence interval; HR = Hazard Ratio. Model 0 is
crude or univariable Cox analysis. Model 1 is a Cox proportional hazard model including main demographic and
socioeconomic factors (age and calendar year of diagnosis and prediagnostic Charlson comorbidity index as
continuous variables, and sex, place of birth, county of residence, marital status, level of education, income
status and family history of colorectal cancer as categorical variables). Model 2 includes tumour sites and
tumour stage in addition to the covariates in model 1. Model 3 includes therapy-related variables such as
operation type, preoperative chemotherapy (yes/no) and preoperative radiotherapy (yes/no) in addition to
covariates in model 2. Variable definitions and categorization for the covariates in models 1-3 are as presented
in Table 1. For each multivariable model, variables violating the proportional hazard assumption were
controlled for by stratification in the respective models. They were age at diagnosis (</≥ 50 years), sex
(men/women), prediagnostic Charlson comorbidity index (CCI) (no/any comorbidity), income status (quartile
categories) and family history of colorectal cancer (0/1/≥2 first degree relatives) for model 1; age at diagnosis,
prediagnostic CCI, family history of colorectal cancer, tumour sites (proximal colon, distal colon, rectum,
unspecified) and tumour stage (stage I-III) for model 2, age at diagnosis, prediagnostic CCI, type of surgery
2
(elective/emergency), tumour sites and tumour stage for model 3. For age at diagnosis and prediagnostic CCI (continuous variables), binary variable types (age at diagnosis: </≥ 50 years, prediagnostic CCI: no/any CCI) were used and continuous variable types were also adjusted to account for residual confounding in each stratum.
Supplemental Table 1 from Germline mutations in 12 genes and risk of ovarian cancer in three population-based cohorts
Authors: Kotsopoulos Joanne , Hathaway Cassandra A. , Narod Steven A. , Teras Lauren R. , Patel Alpa V. , Hu Chunling , Yadav Siddhartha , Couch Fergus J. , Tworoger Shelley S.
Published: September 2024

Supplemental Table 1: Characteristics from cohort (2004 NHS; 2007 NHSII, and baseline in CPSII) compared to women with blood or cheek sample
Supplementary Table S6 from Genome-scale methylation analysis identifies immune profiles and age acceleration associations with bladder cancer outcomes
Authors: Chen Ji-Qing , Salas Lucas A. , Wiencke John K. , Koestler Devin C. , Molinaro Annette M. , Andrew Angeline S. , Seigne John D. , Karagas Margaret R. , Kelsey Karl T. , Christensen Brock C.
Published: September 2024

Supplementary Table S6: Characteristics of subjects of each group based on the grouping results from both partDSA and SS-RPMM in all NMIBC patients
Supplementary Table S2 from Genome-scale methylation analysis identifies immune profiles and age acceleration associations with bladder cancer outcomes
Authors: Chen Ji-Qing , Salas Lucas A. , Wiencke John K. , Koestler Devin C. , Molinaro Annette M. , Andrew Angeline S. , Seigne John D. , Karagas Margaret R. , Kelsey Karl T. , Christensen Brock C.
Published: September 2024

Supplementary Table S2: Cox proportional hazards multivariable models for demographic and tumor characteristics of 601 NMIBC patients (For Hannum Age Acceleration)
Supplementary Table S3 from Heritable Traits and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study
Authors: Pettit Rowland W. , Byun Jinyoung , Han Younghun , Ostrom Quinn T. , Coarfa Cristian , Bondy Melissa L. , Amos Christopher I.
Published: September 2024

Supplementary Table S3. Univariate MR estimations of total causal effects between traits with lung cancer risks.
Supplementary Table S2 from Heritable Traits and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study
Authors: Pettit Rowland W. , Byun Jinyoung , Han Younghun , Ostrom Quinn T. , Coarfa Cristian , Bondy Melissa L. , Amos Christopher I.
Published: September 2024

Supplementary Table S2. Complete list of SNPs used as genetic instrumental variables (IVs) for causal effect modeling of traits with lung cancer outcomes.
Supplementary Table S1 from Heritable Traits and Lung Cancer Risk: A Two-Sample Mendelian Randomization Study
Authors: Pettit Rowland W. , Byun Jinyoung , Han Younghun , Ostrom Quinn T. , Coarfa Cristian , Bondy Melissa L. , Amos Christopher I.
Published: September 2024

Supplementary Table S1. Overview of Transdisciplinary Research of Cancer in Lung of the International Lung Cancer Consortium (TRICL-ILCCO) and the OncoArray Consortium GWAS studies
Supplementary Table 4 from The Association of Kidney Function and Inflammatory Biomarkers with Epithelial Ovarian Cancer Risk
Authors: Hathaway Cassandra A. , Townsend Mary K. , Sklar Elliot M. , Thomas-Purcell Kamilah B. , Terry Kathryn L. , Trabert Britton , Tworoger Shelley S.
Published: September 2024

Hazard ratios (HR) and 95% confidence intervals (95%CI) of kidney related markers measured in blood and ovarian cancer risk by C Reactive Protein (CRP) in the UK Biobank.
Supplementary Table 2 from The Association of Kidney Function and Inflammatory Biomarkers with Epithelial Ovarian Cancer Risk
Authors: Hathaway Cassandra A. , Townsend Mary K. , Sklar Elliot M. , Thomas-Purcell Kamilah B. , Terry Kathryn L. , Trabert Britton , Tworoger Shelley S.
Published: September 2024

Hazard ratios (HR) and 95% confidence intervals (95%CI) of kidney related markers measured in blood and ovarian cancer risk by histological subtype in the UK Biobank.
Supplementary Table 1 from The Association of Kidney Function and Inflammatory Biomarkers with Epithelial Ovarian Cancer Risk
Authors: Hathaway Cassandra A. , Townsend Mary K. , Sklar Elliot M. , Thomas-Purcell Kamilah B. , Terry Kathryn L. , Trabert Britton , Tworoger Shelley S.
Published: September 2024

Hazard ratios (HR) and 95% confidence intervals (95%CI) of C Reactive Protein and ovarian cancer risk in the UK Biobank using cutpoints considered in prior literature.
Supplementary Table 1 from Germline genetic variants associated with somatic TMPRSS2:ERG fusion status in prostate cancer: a genome-wide association study
Authors: Ma Chaoran , Wang Xiaoyu , Dai James Y. , Turman Constance , Kraft Peter , Stopsack Konrad H. , Loda Massimo , Pettersson Andreas , Mucci Lorelei A. , Stanford Janet L. , Penney Kathryn L.
Published: September 2024

Associations of previously reported prostate cancer risk variants with ERG fusion status in the Health Professionals Follow-up Study/Physicians Health Study (HPFS/PHS) and Fred Hutchinson (FH) Prostate Cancer Study.
Supplementary Figure 1 from Germline genetic variants associated with somatic TMPRSS2:ERG fusion status in prostate cancer: a genome-wide association study
Authors: Ma Chaoran , Wang Xiaoyu , Dai James Y. , Turman Constance , Kraft Peter , Stopsack Konrad H. , Loda Massimo , Pettersson Andreas , Mucci Lorelei A. , Stanford Janet L. , Penney Kathryn L.
Published: September 2024

Quantile-quantile plots of GWAS in the pooled analysis of ERG (+) vs. ERG (-) tumor (A) ERG (+) tumors vs. controls (B) and ERG (-) tumors vs. controls (C).
Supplementary Table S5 from Sex-specific associations between adiponectin and leptin signaling and pancreatic cancer survival
Authors: Babic Ana , Wang Qiao-Li , Lee Alice A. , Yuan Chen , Rifai Nader , Luo Juhua , Tabung Fred K. , Shadyab Aladdin , Wactawski-Wende Jean , Saquib Nazmus , Kim Jihye , Kraft Peter , Sesso Howard D. , Buring Julie E. , Giovannucci Edward L. , Manson JoAnn E. , Stampfer Meir J. , Ng Kimmie , Fuchs Charles S. , Wolpin Brian M.
Published: September 2024

SNPs in adiponectin and leptin receptor genes and their association with receptor expression