Phoenixin-14 Downregulation as a Novel Prognostic Biomarker in Sepsis: Molecular and Clinical Evidence
Conditions: Sepsis - to Reduce Mortality in the Intensive Care Unit, Sepsis Biomarker, Phoenixin-14 Molecular Downregulation
Sponsor: Firat University

Suppression of Phoenixin-14 Predicts Mortality in Sepsis: Evidence from 
Serum Levels and Gene Expression 
Assistant Professor, MD Gülsüm ALTUNTAŞ                                                      30.04.2025 
Department of Anesthesiology and Reanimation, Firat University Medicine Faculty Hospital, 
Elazig 23300, Turkey  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

 
 
 
 
 
Study Protocol 
Study Design and Patients 
In this prospective observational cohort study, patients aged 18 years and older admitted to 
our tertiary intensive care unit with diagnoses of sepsis and/or septic shock were evaluated. 
Ethical approval was obtained from the Fırat University Non-Interventional Research Ethics 
Committee on March 27, 2024, with the number 23376. The entire study was funded by the 
Fırat University Scientific Research Projects Coordination Unit (FUBAP), under project 
number TF.24.11. Our study adhered to the Helsinki Declaration and the Strengthening the 
Reporting of Observational Studies in Epidemiology (STROBE) guidelines. 
The treatment protocol for patients was followed according to the Sepsis-3 guidelines (1), and 
antibiotic therapy was administered in consultation with an infectious disease specialist, 
aiming at the source of sepsis. Septic shock was also defined according to the Sepsis-3 
guidelines (1), based on the requirement for vasopressor support to maintain adequate mean 
arterial pressure despite adequate fluid resuscitation. 
Patients under the age of 18, patients who were discharged/transferred or died within 72 hours 
after being diagnosed with sepsis, patients/relatives who refused to participate in the study, 
pregnant women, patients using chronic steroids, patients whose blood samples were collected 

under inappropriate conditions, and patients with a history of malignancy or 
immunosuppression were also excluded from the study. 
The study was started with participants who agreed after signing an informed consent form. 
Collection of Blood Samples and Data 
At the time of ICU admission, parameters such as age, gender, body mass index (BMI), 
comorbidities, infection focus, complete blood count, CRP, procalcitonin, Acute Physiology 
and Chronic Health Evaluation (APACHE) II score, Sequential Organ Failure Assessment 
(SOFA) score, vasopressor requirement, mechanical ventilation need, and renal replacement 
therapy requirement were recorded. On the first day of hospitalization, 3 mL of blood was 
routinely collected from patients for biochemical analysis. The levels of IL-6, TNF-α, IL-1β, 
IL-10, and PNX14 were measured using real-time PCR, while serum levels were determined 
using ELISA. Blood samples were collected once at ICU admission to assess baseline PNX-
14 levels for early prognostic evaluation. 
RNA Isolation 
Total RNA was isolated from peripheral blood samples using the NucleoSpin RNA Blood kit 
(Macherey-Nagel, Germany) according to the manufacturer’s instructions. RNA 
concentration and purity were assessed using a Qubit 2.0 Fluorometer (Thermo Fisher 
Scientific). 
RNA Quality Control – Agarose Gel Electrophoresis 
RNA integrity was assessed by agarose gel electrophoresis. Briefly, RNA samples were 
separated on a 1% agarose gel and visualized using a gel documentation system to confirm 
RNA integrity, according to standard laboratory procedures. 
RNA Concentration Measurement – Qubit Fluorometric Method 

RNA concentration and quality were assessed using a Qubit 2.0 Fluorometer (Thermo Fisher 
Scientific, USA) and the Qubit RNA HS Assay Kit, following the manufacturer’s 
recommended protocol. 
cDNA Synthesis 
Complementary DNA (cDNA) was synthesized from total RNA using the High-Capacity 
cDNA Reverse Transcription Kit (Thermo Fisher Scientific, USA) as per the manufacturer’s 
protocol. 
Quantitative Real-Time PCR (qRT-PCR) 
Quantitative real-time PCR analysis was performed using SYBR Green Master Mix 
(AMPLIFYME SYBR Universal Mix) on an Applied Biosystems StepOnePlus system. 
GAPDH was used as the reference gene due to its reported expression stability in peripheral 
blood samples under inflammatory conditions. Relative gene expression was calculated using 
the 2⁻ΔΔCt method, with GAPDH as the internal control. In the absence of a healthy control 
group, the survivor group was used as the calibrator for relative expression analysis. Primer 
sequences are listed in Table 1. 
Table 1. Primers used in the study 
 
Forward Primer 
Reverse Primer 
GAPDH 
5'-GAA GGT GAA GGT CGG AGT 
C-3' 
5'-GAA GAT GGT GAT GGG 
ATT TC-3' 
PNX-14 
5'-CGG CTT CAT CTC CCT GAT 
CG -3' 
5'ACA GCC CTC TCA TTT CCT 
GC-3' 
GAPDH (glyceraldehyde-3-phosphate dehydrogenase; PNX-14: Phoenixin14 
ELISA Protocol 

Serum levels of Phoenixin-14 and cytokines were measured using commercially available 
ELISA kits (BT Lab, Shanghai, China) according to the manufacturer’s instructions. 
End points 
Primary endpoint: the relationship between PNX14 levels and in-hospital mortality in patients 
with sepsis or septic shock. Secondary endpoints: to evaluate the tendency of PNX-14 levels 
to be suppressed at the molecular level in patients with sepsis and their potential as a 
translational biomarker. Additionally, to assess the relationship between PNX14 and SOFA 
scores, APACHE II scores, septic shock, requirements for mechanical ventilation or renal 
replacement therapy, and ICU length of stay in patients with sepsis. The correlation between 
PNX14 levels and those of CRP, procalcitonin, IL-1β, IL-6, IL-10, and TNF-α was also 
examined as part of the endpoints. 
Statistical Analysis 
Before the study, calculations were conducted using G*Power 3.1 with 80% power, α = 0.05, 
and an expected medium effect size (d = 0.5), indicating that at least 64 patients were needed. 
This requirement was met by including 77 patients in the study. 
The data were analyzed using IBM SPSS Statistics for macOS, version 29.0 (IBM Corp., 
Armonk, NY, USA). Descriptive statistics for continuous variables are presented as mean ± 
standard deviation for normally distributed data and median (Q1–Q3) for non-normal data; 
categorical variables are shown as frequency (percentage). Group comparisons between 
developing and non-developing patients were conducted with the independent samples t-test 
for normally distributed continuous data and the Mann–Whitney U test for non-normal data. 
Normality of continuous variables was checked with the Kolmogorov-Smirnov test. For 
categorical variables, either the Pearson chi-square test or Fisher's exact test was used, as 
appropriate.  

The relative quantification (RQ, 2^-ΔΔCt) values from qPCR experiments were compared 
between two groups (Dead and Survived). The distribution of both groups was assessed using 
the Shapiro–Wilk test prior to analysis, and the assumption of normality was not rejected (D 
group p=0.12; S group p=0.17). Homogeneity of variances was evaluated with the Levene 
test, which indicated unequal variances (p=0.018). Therefore, the differences between groups 
were analyzed using the Welch independent samples t-test. Additionally, the nonparametric 
Mann–Whitney U test was performed to verify robustness. 
The ability of each biomarker to predict mortality was evaluated using ROC (receiver 
operating characteristic) curve analysis. The area under the curve (AUC), 95% confidence 
interval, optimal cut-off value, sensitivity, specificity, Youden index, and p-values were 
calculated. Since PNX-14 levels were inversely related to mortality in the ROC analysis, the 
analysis was performed with the reversed direction. Comparison of ROC curves for PNX-14 
versus procalcitonin and CRP was conducted using the DeLong test. 
To identify variables linked to mortality, univariate logistic regression analysis was conducted. 
Variables that proved significant were included in the multivariate logistic regression model to 
determine independent risk factors. The Backward-Wald method was employed for variable 
selection. Multicollinearity among independent variables was evaluated using the variance 
inflation factor (VIF) and tolerance values; VIF less than 5 and tolerance values between 0.40 
and 0.80 were considered acceptable. The relationships between PNX, inflammatory 
biomarkers, and clinical severity scores were analyzed using Pearson or Spearman correlation 
tests, depending on the distribution of the variables, with correlation coefficients (r) and p-
values reported. In all analyses, a p-value less than 0.05 was regarded as statistically significant.