S100P as a potential biomarker for immunosuppressive microenvironment in pancreatic cancer: a bioinformatics analysis and in vitro study

Abstract Background Immunosuppression is a significant factor contributing to the poor prognosis of cancer. S100P, a member of the S100 protein family, has been implicated in various cancers. However, its role in the tumor microenvironment (TME) of pancreatic cancer remains unclear. This study aimed to investigate the potential impact of S100P on TME characteristics in patients with pancreatic cancer. Methods Multiple data (including microarray, RNA-Seq, and scRNA-Seq) were obtained from public databases. The expression pattern of S100P was comprehensively evaluated in RNA-Seq data and validated in four different microarray datasets. Prognostic value was assessed through Kaplan-Meier plotter and Cox regression analyses. Immune infiltration levels were determined using the ESTIMATE and ssGSEA algorithms and validated at the single-cell level. Spearman correlation test was used to examine the correlation between S100P expression and immune checkpoint genes, and tumor mutation burden (TMB). DNA methylation analysis was performed to investigate the change in mRNA expression. Reverse transcription PCR (RT-PCR) and immunohistochemical (IHC) were utilized to validate the expression using five cell lines and 60 pancreatic cancer tissues. Results This study found that S100P was differentially expressed in pancreatic cancer and was associated with poor prognosis (P < 0.05). Notably, S100P exhibited a significant negative-correlation with immune cell infiltration, particularly CD8 + T cells. Furthermore, a close association between S100P and immunotherapy was observed, as it strongly correlated with TMB and the expression levels of TIGIT, HAVCR2, CTLA4, and BTLA (P < 0.05). Intriguingly, higher S100P expression demonstrated a negative correlation with methylation levels (cg14323984, cg27027375, cg14900031, cg14140379, cg25083732, cg07210669, cg26233331, and cg22266967), which were associated with CD8 + T cells. In vitro RT-PCR validated upregulated S100P expression across all five pancreatic cancer cell lines, and IHC confirmed high S100P levels in pancreatic cancer tissues (P < 0.05). Conclusion These findings suggest that S100P could serve as a promising biomarker for immunosuppressive microenvironment, which may provide a novel therapeutic way for pancreatic cancer

Prediction of esophageal cancer risk based on genetic variants and environmental risk factors in Chinese population

Abstract Background Results regarding whether it is essential to incorporate genetic variants into risk prediction models for esophageal cancer (EC) are inconsistent due to the different genetic backgrounds of the populations studied. We aimed to identify single-nucleotide polymorphisms (SNPs) associated with EC among the Chinese population and to evaluate the performance of genetic and non-genetic factors in a risk model for developing EC. Methods A meta-analysis was performed to systematically identify potential SNPs, which were further verified by a case-control study. Three risk models were developed: a genetic model with weighted genetic risk score (wGRS) based on promising SNPs, a non-genetic model with environmental risk factors, and a combined model including both genetic and non-genetic factors. The discrimination ability of the models was compared using the area under the receiver operating characteristic curve (AUC) and the net reclassification index (NRI). The Akaike information criterion (AIC) and Bayesian information criterion (BIC) were used to assess the goodness-of-fit of the models. Results Five promising SNPs were ultimately utilized to calculate the wGRS. Individuals in the highest quartile of the wGRS had a 4.93-fold (95% confidence interval [CI]: 2.59 to 9.38) increased risk of EC compared with those in the lowest quartile. The genetic or non-genetic model identified EC patients with AUCs ranging from 0.618 to 0.650. The combined model had an AUC of 0.707 (95% CI: 0.669 to 0.743) and was the best-fitting model (AIC = 750.55, BIC = 759.34). The NRI improved when the wGRS was added to the risk model with non-genetic factors only (NRI = 0.082, P = 0.037). Conclusions Among the three risk models for EC, the combined model showed optimal predictive performance and can help to identify individuals at risk of EC for tailored preventive measures

Human Proteome Microarray identifies autoantibodies to tumor‐associated antigens as serological biomarkers for the diagnosis of hepatocellular carcinoma

The identification of the high‐efficiency and non‐invasive biomarkers for hepatocellular carcinoma (HCC) detection is urgently needed. This study aims to screen out potential autoantibodies to tumor‐associated antigens (TAAbs) and to assess their diagnostic value for HCC. Fifteen potential TAAbs were screened out from the Human Proteome Microarray by 30 HCC sera and 22 normal control sera, of which eight passed multiple‐stage validations by ELISA with a total of 1625 human serum samples from normal controls (NCs) and patients with HCC, liver cirrhosis, chronic hepatitis B, gastric cancer, esophageal cancer, and colorectal cancer. Finally, an immunodiagnostic model including six TAAbs (RAD23A, CAST, RUNX1T1, PAIP1, SARS, PRKCZ) was constructed by logistic regression, and yielded the area under curve (AUC) of 0.835 and 0.788 in training and validation sets, respectively. The serial serum samples from HCC model mice were tested to explore the change in TAAbs during HCC formation, and an increasing level of autoantibodies was observed. In conclusion, the panel of six TAAbs can provide potential value for HCC detection, and the strategy to identify novel serological biomarkers can also provide new clues in understanding immunodiagnostic biomarkers