Diagnostic values of TPT1 in patients with sepsis,septic shock and in non-infected controls: A single-center prospective observational cohort study in ICU patients

Abstract

Objective

To evaluate the diagnostic value of tumor protein translationally-controlled 1 (TPT1) in patients with sepsis and septic shock in the intensive care unit (ICU).

Methods

This single-center, prospectively planned observational study included 53 ICU patients with sepsis (30 with non-shock sepsis, 23 with septic shock) and 20 non-infected ICU controls. Plasma levels of TPT1, procalcitonin (PCT), and C-reactive protein (CRP) were measured on day 1 of ICU admission. Diagnostic performance was assessed using receiver operating characteristic (ROC) analysis.

Results

TPT1 levels were associated with higher SOFA scores and serum creatinine levels (p < 0.01). TPT1 levels were consistently significantly higher in non-shock sepsis and septic shock patients than in non-infected controls (p < 0.001). On day 1, plasma TPT1 levels effectively differentiated non-shock from septic shock (p < 0.01). TPT1 has good diagnostic value for sepsis and septic shock (AUC 0.80 and 0.94, respectively, p < 0.0001). TPT1 levels were significantly elevated in both non-shock sepsis and septic shock groups compared to non-infected controls (p < 0.001). TPT1 also showed a positive correlation with SOFA score and serum creatinine (p < 0.01). The area under the ROC curve (AUC) for TPT1 was 0.80 for sepsis and 0.94 for septic shock (p < 0.0001), indicating moderate to high diagnostic accuracy. TPT1 outperformed CRP and PCT in distinguishing septic shock from non-shock sepsis (AUC = 0.71).

Conclusions

TPT1 has significant value as a diagnostic marker in sepsis and septic shock, with diagnostic capabilities comparable to procalcitonin and C-reactive protein.

Keywords

Sepsis septic shock TPT1 ICU biomarkers

Introduction

Sepsis is a life-threatening organ dysfunction caused by a dysregulated host response to infection¹ and is one of the leading causes of morbidity and mortality in intensive care units (ICUs) worldwide.^1,2 According to the Global Burden of Disease Study, sepsis affects tens of millions of patients each year and is particularly burdensome in low- and middle-income countries. Therefore, early diagnosis and accurate identification of sepsis are crucial to reduce mortality and improve prognosis. Research shows that early use of evidence-based therapeutic interventions, such as antimicrobials, fluid resuscitation, and organ support, can significantly improve survival and reduce complications.^3–6 Despite the widespread use of C-reactive protein (CRP) and procalcitonin (PCT), their diagnostic performance remains inconsistent across different patient populations,^7,8 underscoring the need for novel biomarkers. Several emerging biomarkers, such as Presepsin (sCD14-ST), serum lactate, and soluble urokinase-type plasminogen activator receptor (suPAR), have been proposed for stratifying sepsis severity.^9,10 However, none achieves sufficient standalone diagnostic accuracy. Recent systematic reviews and multicenter cohort studies have highlighted persistent challenges in identifying reliable biomarkers for sepsis diagnosis and risk stratification, particularly due to the marked heterogeneity in host immune responses.^11,12 These findings underscore the importance of developing novel biomarkers that reflect specific immune pathways and can complement existing tools like CRP and PCT.

TPT1, also known as translationally controlled tumor protein (TCTP), is a functional protein that can regulate the release of local signaling molecules by immune cells. TPT1 is expressed in a variety of cells during inflammation, such as macrophages, dendritic cells, and lymphocytes.¹³ In addition, TPT1 can enhance the secretion of IL-1 and IL-8 by B cells,¹⁴ stimulate basophils to release IL-4, IL-13, and histamine,¹⁵ and inhibit the release of IL-2 and IL-13 by T cells,^16,17 suggesting that TPT1 is closely related to the body's immune status. Recently, a study found that TPT1 continued to be upregulated in sepsis-induced cardiomyopathy (SIC) as the disease progressed, suggesting that TPT1 may play a key role in the progression of SIC.¹⁸ This finding further supports the potential role of TPT1 as a biomarker in sepsis. We speculate that TPT1 may have important value in the diagnosis of sepsis and may provide more clinical references as a new biomarker. However, there is no clinical study on the value of TPT1 as a diagnostic marker for sepsis and septic shock. Therefore, this study aims to evaluate the diagnostic potential of TPT1 in ICU patients with sepsis and septic shock and provide preliminary evidence for its clinical application.

Materials and methods

Study design and setting

This prospective observational cohort study was conducted between November 2023 and May 2024 in the ICU of the Affiliated Hospital of Southwest Medical University. The study was approved by the institutional ethics committee (approval number: KY2023359, approved on 14 November 2023), and written informed consent was obtained from all patients or their legal representatives. The study was conducted in accordance with the Declaration of Helsinki (1975), as revised in 2024.

Patient selection and group definitions

Patients were classified into three groups: non-shock sepsis, septic shock, and non-infected controls. All septic patients met the Sepsis-3.0 criteria, defined as suspected or confirmed infection and a Sequential Organ Failure Assessment (SOFA) score ≥2.¹ Septic shock was diagnosed in patients who required vasopressor therapy to maintain mean arterial pressure (MAP) ≥ 65 mmHg after adequate fluid resuscitation and had serum lactate levels >2 mmol/L.

The non-infected control group comprised postoperative ICU patients admitted for routine monitoring following clean (non-contaminated) elective surgeries (e.g. orthopedic or neurosurgical procedures). These individuals had no clinical signs or microbiological/imaging evidence of infection and no persistent elevation in inflammatory markers. Mild transient elevations due to surgical stress were permissible.

Inclusion criteria were age ≥18 years and ICU admission with confirmed classification as one of the above groups. Exclusion criteria included age <18 years, pregnancy, lactation, malignancy, immunosuppressive therapy, autoimmune disorders, allergic disease, diabetes, HIV or viral hepatitis, or refusal to participate.

Biomarker measurements

Peripheral blood samples were collected from all participants within 24 h of ICU admission. After centrifugation at 2000 rpm for 10 min, supernatants were collected for subsequent analysis. The total protein concentration in the samples was quantified using the BCA (Bicinchoninic Acid) method to standardize the protein content of each sample. The samples were then coated onto the ELISA (Enzyme-Linked Immunosorbent Assay) plate wells using specific antibodies to form solid-phase antibodies. Subsequently, horseradish peroxidase (HRP)-labeled detection antibodies were added to form antibody-antigen-enzyme-labeled antibody complexes. After the reaction was completed, the wells were washed, and TMB (3,3′,5,5′-Tetramethylbenzidine) substrate solution was added. The substrate reacted with the enzyme to generate color change, with the intensity of the color being positively correlated with the protein concentration in the sample. The absorbance (OD value) was measured at a wavelength of 450 nm using an enzyme reader, and the protein concentration was calculated according to the standard curve.

Statistical analysis

Data analysis and visualization were performed using GraphPad Prism software (version 8.0.1). A single-sample Kolmogorov–Smirnov test was performed on the measurement data to verify the normality of the data. For normally distributed variables, results were presented as mean ± standard deviation (SD) and compared using one-way analysis of variance (ANOVA), followed by LSD (least significant difference) t-tests for pairwise unpaired comparisons. If significant differences were found, pairwise comparisons were performed using the LSD t-test. For non-normally distributed data, they were expressed as median and quartiles (M (Q1, Q3)), and the Kruskal–Wallis H test was used for comparisons between multiple groups. For two-group comparisons involving non-normally distributed data, the Mann–Whitney U test was applied. All group comparisons were unpaired, as each participant contributed a single sample and no repeated or matched measurements were performed. Categorical variables were analyzed using the chi-square test.

The correlation between the two sets of data was assessed using the Spearman correlation coefficient. To reduce the risk of type I errors arising from multiple testing, Bonferroni correction was applied for confirmatory comparisons, while the Benjamini–Hochberg false discovery rate (FDR) method was used for exploratory correlation analyses to maintain statistical power while controlling for false discoveries. The receiver operating characteristic (ROC) curve was used to analyze the diagnostic capabilities of different indicators for sepsis and septic shock. Evaluation was based on the area under the ROC curve (AUC): an AUC between 0.5 and 0.7 indicated poor diagnostic value, 0.7–0.9 indicated moderate diagnostic value, and >0.9 indicated high diagnostic value. p-Values <0.05 were considered statistically significant.

A post-hoc power analysis was conducted using the pROC package (version 1.18.4)¹⁹ in R software (version 4.3.0) to estimate the achieved statistical power based on the observed AUC.

Given that age and gender may influence the development and outcomes of sepsis and septic shock, subgroup analyses were conducted to evaluate the diagnostic performance of TPT1 in different age (<65 vs. ≥65 years) and gender (male vs. female) groups. These analyses were exploratory in nature due to relatively small subgroup sample sizes.

Results

Baseline

Baseline characteristics are shown in Table 1. A total of 73 patients, including patients with non-shock sepsis (n = 30) and septic shock (n = 23), and 20 non-infected controls without evidence of infection were enrolled. The patient selection process is illustrated in the inclusion flowchart (Figure 1), which outlines ICU admissions, exclusions, and final enrollment during the study period.

Figure 1.

Patient inclusion flowchart for sepsis and non-infected ICU patients.

Table 1.

Baseline characteristics of the Mannheim Sepsis Study (MaSep).

	Non-infected controls (n = 20)	Non-shock sepsis (n = 30)	Septic shock(n = 23)	p-Value
Mean ± SD/median (interquartile range)
Age, years (mean, range)	65 (51–85)	69 (47–91)	62 (32–86)	0.0973
Gender, n (%)				0.6921
Male	13 (65)	17 (57)	12 (52)
Female	7 (35)	13 (43)	11 (48)
Site of infection, n (%)				0.8305
Lung	–	6 (20)	3 (13)
Urinary tract	–	3 (10）	2 (9)
Abdominal	–	13 （43）	11 (48)
Bone	–	1 （3)	1 (4)
Skin	–	4 (14)	1 (4)
Blood	–	2 (7)	3 (13)
Others	–	1 (3)	2 (9)
Laboratory values
White blood cells, 109/L	–	11.95 ± 1.192	11.16 ± 1.817	0.7096
Platelets, 109/L	–	150.4 ± 13.97	84 (39, 201)	0.056
Bilirubin, mg/dl	–	15.6 (10.8, 28.63)	15.7 (12.1, 34)	0.4462
Lactate, mg/dl	–	1.53 ± 0.13	3.88 (2.33, 5.46)	<0 . 0001
Creatinine, mg/dl	–	124.4 (84.18, 252.9)	156.4 (103.3, 270.3)	0.2143
C reactive protein, mg/l	22.66 (18.25, 35.63)	76.87 (50.42, 152)	140.4 ± 17.66	<0.0001
Procalcitonin, ng/ml	4.31 (2.35, 9.45)	26.64 (8.56, 60.47)	32.50 (14.67, 100)	<0.0001
Interleukin-6, pg/ml	147.8 (118.1, 421.8)	135.7 (66.32, 1029)	1647 (374.3, 5029)	0.0012
TPT1, ng/ml	81.82 (76.59, 88.82)	95.04 (76.86, 138.5)	125.1 (97.96, 263.9)	<0.0001
pCO₂, mmHg	–	37.28 ± 1.58	37.43 ± 2.14	0.9518
Positive blood cultures, n (%)	–	10 (33)	10 (43)	0.5698
ICU parameters
ICU days	–	2.0 (1, 4.3)	4 (1, 6)	0.1718
Ventilation days	–	1 (0, 3.25)	3 (1, 4)	0.025
Catecholamine days	–	1 (0, 3)	1 (1, 3)	0.1912
APACHE II	–	21.8 ± 1.15	27.96 ± 1.40	0.0012
SOFA score	–	8.1 ± 0.63	12 (10, 13)	<0.0001

Note: Data are presented as mean ± SD or median (interquartile range) as appropriate. p-Values represent comparisons between groups (non-infected control, non-shock sepsis, and septic shock) where data are available. Bold values indicate statistically significant differences between groups, corresponding to p-values < 0.05.

At enrollment, the most common sites of infection were the abdomen (45%) and the lungs (approximately 17%). The predominance of abdominal infections may reflect the local case mix of our ICU, which frequently admits postoperative abdominal surgery patients and critically ill cases such as severe acute pancreatitis with signs of peritonitis. However, given the lack of detailed data on the full ICU population, we acknowledge that selection bias cannot be completely excluded.

Univariate correlations of TPT1 levels TPT1

TPT1 levels demonstrated statistically significant correlations with several clinical and laboratory parameters among patients with sepsis (Table 2). After adjustment for multiple comparisons using the Benjamini–Hochberg procedure, significant positive correlations persisted between TPT1 and SOFA score (r = 0.47, FDR-adjusted p = 0.005), serum creatinine (r = 0.36, FDR-adjusted p = 0.032), APACHE II score (r = 0.34, FDR-adjusted p = 0.042), and arterial pCO₂ (r = 0.32, FDR-adjusted p = 0.049). Other variables, including platelet count, IL-6, PCT, lactate, and ICU treatment duration, exhibited nominal associations with TPT1 in unadjusted analyses; however, these did not remain statistically significant after correction. These findings suggest that elevated TPT1 levels may reflect both the severity of organ dysfunction and systemic inflammation in sepsis, supporting its potential utility as a biomarker of disease burden.

Table 2.

Univariate correlations of TPT1 with laboratory and clinical parameters in all patients (n = 55).

Variable	r	Raw p-value	Bonferroni-adjusted p	FDR-adjusted p (BH)
Creatinine	0.36	0.008	0.088	0.032
Lactate	0.28	0.04	0.44	0.064
Platelets	−0.29	0.035	0.385	0.064
pCO₂	0.32	0.018	0.198	0.049
Procalcitonin (PCT)	0.29	0.034	0.374	0.064
Interleukin-6	0.28	0.044	0.484	0.064
Mechanical ventilation days	0.28	0.042	0.462	0.064
Catecholamine days	0.29	0.038	0.418	0.064
APACHE II score	0.34	0.013	0.143	0.042
SOFA score	0.47	<0.001	<0.01	0.005

Note: Bonferroni-adjusted p-values were calculated based on 10 comparisons (α = 0.05/10 = 0.005). FDR-adjusted p-values were calculated using the Benjamini–Hochberg method. Significant correlations after FDR correction are highlighted in bold.

Diagnostic value of TPT1

Figure 2 shows the distribution of TPT1, IL-6, CRP, and PCT in the non-shock sepsis group and septic shock group. Serum TPT1 levels were significantly higher in sepsis patients compared with non-infected controls (p < 0.001). Furthermore, patients with septic shock had significantly higher TPT1 levels than those with non-shock sepsis (p < 0.01), based on unadjusted group comparisons. ROC analysis demonstrated that TPT1 had moderate diagnostic accuracy for sepsis (AUC = 0.80, p < 0.0001) and high diagnostic accuracy for septic shock (AUC = 0.94, p < 0.0001) (Table 3). The optimal cutoff value of TPT1 in sepsis was 91.88 ng/ml, with a sensitivity of 69.8% and a specificity of 90% (Figure 3(a)). The optimal cutoff value of TPT1 in septic shock was 92.31 ng/ml, with a sensitivity of 82.6% and a specificity of 100% (Figure 3(b)). In distinguishing septic shock from non-shock sepsis, TPT1 achieved an AUC of 0.71 (95% CI: 0.57–0.85, p = 0.01), outperforming PCT (AUC = 0.58, p = 0.3149) and CRP (AUC = 0.61, p = 0.1783) (Figure 3(c)). All reported AUCs and p-values reflect unadjusted analyses unless otherwise specified. Notably, while IL-6 showed limited diagnostic value for sepsis and septic shock compared to other biomarkers, it demonstrated the highest discriminatory power for shock occurrence in septic patients (AUC = 0.73, p = 0.0043) (Table 3).

Post-hoc power analysis confirmed that the study had sufficient power to detect the observed diagnostic performance of TPT1. The power was 81% for the sepsis group and 99% for the septic shock group, supporting the adequacy of the sample size.

Subgroup ROC analyses stratified by age and sex (Table 4) showed that TPT1 maintained moderate to high diagnostic accuracy across all demographic groups. In the sepsis subgroup, performance was higher in younger patients (AUC = 0.89) and males (AUC = 0.85). In the septic shock subgroup, the highest accuracy was observed in males aged ≥65 years (AUC = 0.99). These subgroup comparisons were exploratory in nature and not powered for formal statistical inference.

Figure 2.

Serum TPT1 levels (a), IL-6 plasma levels (b), C-reactive protein (c), and procalcitonin (d) in patients with non-shock sepsis, septic shock, and non-infected controls. Statistical comparisons were performed using the Kruskal–Wallis test followed by Mann–Whitney U tests. Data are presented as medians (boxes show interquartile range; whiskers show 5th–95th percentiles). TPT1: tumor protein translationally-controlled 1.

Figure 3.

Receiver operating characteristic (ROC) curve analysis shows the diagnostic value of TPT1, CRP, PCT, and IL-6 for sepsis (a) and septic shock (b), as well as the ability of the three to differentiate sepsis from septic shock (c). TPT1: tumor protein translationally-controlled 1; CRP: C-reactive protein; PCT: procalcitonin; IL-6: interleukin-6.

Table 3.

The discriminatory ability of different biomarkers in diagnosing the severity of sepsis, analyzed as the area under the curve (AUC [95% CI]).

	TPT1	C-reactive protein	Procalcitonin	Interleukin-6
≥Sepsis(n = 53)	0.80 (0.70, 0.90)	0.88 (0.80, 0.96)	0.85 (0.76, 0.94)	0.63 (0.51, 0.76)
	p < 0.0001	p < 0.0001	p < 0.0001	p = 0.079
Septic shock (n = 23)	0.94 (0.87, 1)	0.86 (0.73, 0.98)	0.88 (0.77, 0.99)	0.83 (0.69, 0.96)
	p < 0.0001	p < 0.0001	p < 0.0001	p = 0.0003
Non-shock sepsis (n = 30) vs. Septic shock (n = 23)	0.71 (0.57, 0.85)	0.61 (0.45, 0.77)	0.58 (0.42, 0.74)	0.73 (0.59, 0.87)
	p = 0.01	p = 0.1783	p = 0.3149	p = 0.0043

Note: “≥sepsis” includes all patients who meet the Sepsis 3.0 criteria definition, regardless of whether they have septic shock. AUC: area under the curve; CI: confidence interval; TPT1: tumor protein translationally-controlled 1. Bold values indicate statistically significant differences between groups, corresponding to p-values < 0.05.

Table 4.

Exploratory subgroup analysis of the diagnostic performance of TPT1 for sepsis and septic shock based on age and gender (AUC [95% CI]).

	Age ≥ 65 (n = 31)	Age <65 (n = 22)	Male (n = 29)	Female (n = 24)
≥Sepsis (n = 53)	0.76 (0.61, 0.90)	0.89 (0.78, 1)	0.85 (0.74, 0.96)	0.77 (0.62, 0.92)
	p = 0.0197	p = 0.0003	p < 0.0001	p = 0.0027
	Age ≥ 65 (n = 12)	Age <65 (n = 11)	Male (n = 12)	Female (n = 11)
Septic shock (n = 23)	0.99 (0.96, 1)	0.91 (0.76, 1)	0.99 (0.96, 1)	0.90 (0.75, 1)
	p = 0.0002	p = 0.0012	p < 0.0001	p = 0.0003

Note: ROC curve analysis was used. “≥sepsis” includes all patients who meet the Sepsis 3.0 criteria definition, regardless of whether they have septic shock. These analyses are exploratory in nature due to limited subgroup sample sizes. AUC: area under the curve; CI: confidence interval; TPT1: tumor protein translationally-controlled 1. Bold values indicate statistically significant differences between groups, corresponding to p-values < 0.05.

Discussion

This study is the first to clinically evaluate the diagnostic performance of translationally controlled tumor protein 1 (TPT1) in patients with sepsis and septic shock according to sepsis 3.0 definition. The results show that serum TPT1 levels can identify patients with sepsis and demonstrate higher efficacy in diagnosing septic shock.^2–11 In addition, TPT1 also has potential clinical value in distinguishing non-shock sepsis from septic shock, which may facilitate early identification and timely intervention to reduce the risk of poor outcomes. These findings highlight the potential of TPT1 as a novel biomarker for early triage and risk stratification. TPT1 also showed strong correlations with SOFA score and serum creatinine, suggesting possible utility in prognostic assessment. While interleukin-6 (IL-6) demonstrated diagnostic accuracy comparable to TPT1 in distinguishing septic shock from sepsis, its overall performance in identifying sepsis or septic shock was lower than that of TPT1, CRP, and PCT. These results suggest that TPT1 offers added diagnostic value beyond currently used inflammatory markers. From a clinical perspective, TPT1 could be applied in practice as a complementary biomarker alongside CRP and PCT to improve diagnostic accuracy, or as a rapid indicator for early identification of high-risk patients who may require prompt ICU admission or escalation of care. This interpretation is consistent with recent systematic reviews and prospective cohort studies, which emphasize the limited reliability of traditional biomarkers in heterogeneous sepsis populations and advocate for novel immune-pathway-based indicators.²⁰ TPT1, by reflecting immune activation and cellular stress responses, may complement existing tools in personalized risk stratification.²¹

Several limitations should be acknowledged. First, the final sample size (53 septic patients and 20 non-infected controls) was smaller than the originally submitted protocol estimate of 150. However, the initial figure was a formal requirement for ethics submission and was not based on statistical power calculations. Post-hoc power analysis confirmed sufficient statistical power to support the primary findings (81% for sepsis, 99% for septic shock). Secondly, the study was conducted in a single center, and the predominance of abdominal infections (45%) may affect the generalizability of the findings. Thirdly, TPT1 was assessed only at a single time point (day 1), limiting our ability to evaluate its dynamic behavior throughout disease progression. Additionally, CRP and PCT were part of the clinical information available to physicians during diagnostic classification, whereas TPT1 was not. This may have introduced a classification bias favoring CRP and PCT, potentially underestimating the relative diagnostic performance of TPT1. Lastly, although subgroup analyses by age and sex showed consistent results, these comparisons were exploratory due to limited sample size.

Future studies should focus on validating these findings in larger, multicenter cohorts with more heterogeneous populations. The optimal diagnostic thresholds for TPT1 identified in this study (e.g. 91.88 ng/ml for sepsis) may serve as candidate cutoffs in future clinical validation studies to guide triage and risk assessment strategies. Serial measurements of TPT1 will be needed to assess its temporal relationship with disease progression and outcome. In addition, we plan to analyze the association between TPT1 levels and 28-day mortality in follow-up analyses to evaluate its prognostic utility. Mechanistically, the secretion of TPT1 may be regulated by Toll-like receptor (TLR) activation.²² Lipopolysaccharide (LPS), as a key trigger for Gram-negative bacterial infection, can trigger an inflammatory cytokine storm by binding to TLR4. Whether TPT1 plays a role in this pathway and its specific functional mechanism still need further study. It is worth noting that the research on TPT1 has made a lot of progress in the fields of cancer and allergic diseases, and a variety of antagonists (such as artemisinin, dTBP2, and thymopentin) have been derived.^23–26 The potential application value of these TPT1 antagonists in the treatment of sepsis deserves further exploration. In summary, TPT1 has important research significance in sepsis. It not only shows potential in diagnosis, but also may play a role in prognostic evaluation and targeted therapy.

Conclusions

TPT1 is a valuable diagnostic marker for sepsis and can effectively distinguish between non-shock sepsis patients and septic shock patients in the ICU.

Footnotes

ORCID iD

Xianying Lei

Ethical approval

Medical research was conducted according to the World Medical Association Declaration of Helsinki.

Authors’ contribution

Gelan Miao and Dexiu Chen designed the experiments, collected samples, and performed data analysis. Xuelian Yang and Yulian Yang collected clinical information and analyzed the data. Xianying Lei was responsible for the guidance of the project. All authors participated in the writing and revision of the manuscript.

Funding

The authors disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Sichuan Science and Technology Program (2022YFS0632), Clinical Key Specialty Construction Project of the National Health Commission [(Number: 2021(451)] and Southwest Medical University School-level Project (Number: 2018-ZRQN-075).

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data availability

The data underlying this article cannot be shared publicly due to the need to protect the privacy and confidentiality of the individuals who participated in the study. The data will be made available upon reasonable request to the corresponding author.

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