Construction of immune-related signature and identification of S100A14 determining immune-suppressive microenvironment in pancreatic cancer

Machine learning‑based construction of damage‑associated molecular patterns related score identifies subtypes of pancreatic adenocarcinoma with distinct prognosis

The present study aimed to assess the prognostic significance of Damage-Associated Molecular Pattern (DAMP)-related gene expression in pancreatic adenocarcinoma (PAAD) and to develop a scoring system based on these genes. Consensus clustering was performed on patients with PAAD using data from The Cancer Genome Atlas (TCGA) and Meta-cohort datasets, identifying three distinct clusters: C1 (pro-DAMP), C2 (intermediate) and C3 (anti-DAMP). Differential gene expression analysis between clusters C1 and C3 identified 141 significant genes. Least Absolute Shrinkage and Selection Operator Cox regression was utilized to derive an optimal predictor set, leading to the identification of six hub genes associated with the DAMP status, which were then employed to calculate the DAMPscore. Weighted Gene Co-expression Network Analysis revealed a strong correlation between these eight hub genes and the DAMPscore. The functionality of these hub genes in PAAD was validated using a Cell Counting Kit-8 assay and Transwell assays. The results indicated that patients with PAAD with elevated DAMPscores exhibited significantly reduced survival times. Receiver operating characteristic (ROC) curve analysis indicated that the DAMPscore has robust prognostic capabilities. In the Meta-cohort, the area under the ROC curve (AUC) values for the DAMPscore to predict overall survival at 1, 3 and 5 years were 0.65, 0.70 and 0.77, respectively, while the AUC values for the TCGA-PAAD cohort were 0.71, 0.73 and 0.72, respectively. Additional cohorts, such as E-MTAB-6134 and ICGC-AU, corroborated the predictive power of the DAMPscore. A comparison of the DAMPscore with other prognostic models revealed that it consistently exhibited a superior C-index across most PAAD cohorts. Furthermore, in vitro experiments demonstrated that PLEK2, a hub gene related to the DAMPscore, is involved in critical biological processes such as cell proliferation, migration and invasion. In conclusion, the DAMPscore is a promising prognostic biomarker for PAAD, surpassing traditional models in various datasets. This study emphasizes the role of DAMP-related pathways in influencing tumor biology and highlights the importance of immune modulation in PAAD prognosis, suggesting that therapeutic strategies targeting DAMP signaling could improve patient outcomes.

Establishment and validation of an immune-related nomogram for the prognosis of pancreatic adenocarcinoma

Pancreatic adenocarcinoma (PDAC) is a highly aggressive neoplasm characterized by limited therapeutic options, particularly in the realm of immunotherapy. This study aims to improve prognosis prediction to guide therapeutic decision-making, and to identify novel targets for immunotherapy of PDAC. We conducted Cox and LASSO regression analyses to develop immune-related gene signature and corresponding nomogram, and the robustness of these signatures was demonstrated using multiple approaches. Additionally, CIBERSORT, ESTIMATE, and xCell algorithms were utilized to assess immune cell infiltration, with experimental validation performed though qPCR. An immune-related gene signature consisting of 18 genes, and the prognostic nomogram was established with superior performance compared to the conventional staging system. Key parameters incorporated into the nomogram included the gene signature, tumor stage, and postoperative treatment. Patients identified as high-risk exhibited an anti-inflammatory tumor microenvironment, characterized by an increase in M2-like tumor-associated macrophages and heightened tumor purity. Notably, the expression of interleukin 6 receptor (IL6R) in PDAC was predominantly derived from macrophages and was significantly associated with patient survival outcomes. Furthermore, attenuated IL-6/IL-6R signaling was found to promote M2-like macrophage differentiation. This study successfully established an immune-related gene signature and a robust nomogram for predicting clinical outcomes in patients with PDAC. Furthermore, we identified IL6R as a promising target for future immunotherapeutic strategies.

Immune-related signature identifies IL1R2 as an immunological and prognostic biomarker in pancreatic cancer

Objective

Pancreatic cancer is one of the most aggressive malignancies, a robust prognostic signature and novel biomarkers are urgently needed for accurate stratification of the patients and optimization of clinical decision-making.

Methods

A list of bioinformatic analysis were applied in public dataset to construct an immune-related signature. Furthermore, the most pivotal gene in the signature was identified. The potential mechanism of the core gene function was revealed through GSEA, CIBERSORT, ESTIMATE, immunophenoscore (IPS) algorithm, single-cell analysis, and functional experiment.

Results

An immune-related prognostic signature and associated nomogram were constructed and validated. Among the genes constituting the signature, interleukin 1 receptor type II (IL1R2) was identified as the gene occupying the most paramount position in the risk signature. Meanwhile, knockdown of IL1R2 significantly inhibited the proliferation, invasion, and migration ability of pancreatic cancer cells. Additionally, high IL1R2 expression was associated with reduced CD8+ T cell infiltration in pancreatic cancer microenvironment, which may be due to high programmed cell death-ligand-1 (PD-L1) expression in cancer cells. Finally, the IPS algorithm proved that patients with high IL1R2 expression possessed a higher tumor mutation burden and a higher probability of benefiting from immunotherapy.

Conclusion

In conclusion, our study constructed an efficient immune-related prognostic signature and identified the key role of IL1R2 in the development of pancreatic cancer, as well as its potential to serve as a biomarker for immunotherapy efficacy prediction for pancreatic cancer.

MicroRNAs in Pancreatic Cancer: Advances in Biomarker Discovery and Therapeutic Implications

Pancreatic cancer remains a formidable malignancy characterized by high mortality rates, primarily attributable to late-stage diagnosis and a dearth of effective therapeutic interventions. The identification of reliable biomarkers holds paramount importance in enhancing early detection, prognostic evaluation, and targeted treatment modalities. Small non-coding RNAs, particularly microRNAs, have emerged as promising candidates for pancreatic cancer biomarkers in recent years. In this review, we delve into the evolving role of cellular and circulating miRNAs, including exosomal miRNAs, in the diagnosis, prognosis, and therapeutic targeting of pancreatic cancer. Drawing upon the latest research advancements in omics data-driven biomarker discovery, we also perform a case study using public datasets and address commonly identified research discrepancies, challenges, and limitations. Lastly, we discuss analytical approaches that integrate multimodal analyses incorporating clinical and molecular features, presenting new insights into identifying robust miRNA-centric biomarkers.

Machine learning algorithm integrates bulk and single-cell transcriptome sequencing to reveal immune-related personalized therapy prediction features for pancreatic cancer

Pancreatic cancer (PC) is a digestive malignancy with worse overall survival. Tumor immune environment (TIME) alters the progression and proliferation of various solid tumors. Hence, we aimed to detect the TIME-related classifier to facilitate the personalized treatment of PC. Based on the 1612 immune-related genes (IRGs), we classified patients into Immune_rich and Immune_desert subgroups via consensus clustering. Patients in distinct subtypes exhibited a difference in sensitivity to immune checkpoint blockers (ICB). Next, the immune-related signature (IRS) model was established based on 8 IRGs (SYT12, TNNT1, TRIM46, SMPD3, ANLN, AFF3, CXCL9 and RP1L1) and validated its predictive efficiency in multiple cohorts. RT-qPCR experiments demonstrated the differential expression of 8 IRGs between tumor and normal cell lines. Patients who gained lower IRS score tended to be more sensitive to chemotherapy and immunotherapy, and obtained better overall survival compared to those with higher IRS scores. Moreover, scRNA-seq analysis revealed that fibroblast and ductal cells might affect malignant tumor cells via MIF-(CD74+CD44) and SPP1-CD44 axis. Eventually, we identified eight therapeutic targets and one agent for IRS high patients. Our study screened out the specific regulation pattern of TIME in PC, and shed light on the precise treatment of PC.

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

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.