Overexpressing lipid raft protein STOML2 modulates the tumor microenvironment via NF-κB signaling in colorectal cancer.

Colorectal cancer (CRC) is characterized by a complex tumor inflammatory microenvironment, while angiogenesis and immunosuppression frequently occur concomitantly. However, the exact mechanism that controls angiogenesis and immunosuppression in CRC microenvironment remains unclear. Herein, we found that expression levels of lipid raft protein STOML2 were increased in CRC and were associated with advanced disease stage and poor survival outcomes. Intriguingly, we revealed that STOML2 is essential for CRC tumor inflammatory microenvironment, which induces angiogenesis and facilitates tumor immune escape simultaneously both in vitro and in vivo. Moreover, tumors with STOML2 overexpression showed effective response to anti-angiogenesis treatment and immunotherapy in vivo. Mechanistically, STOML2 regulates CRC proliferation, angiogenesis, and immune escape through activated NF-κB signaling pathway via binding to TRADD protein, resulting in upregulation of CCND1, VEGF, and PD-L1. Furthermore, treatment with NF-κB inhibitor dramatically reversed the ability of proliferation and angiogenesis. Clinically, we also observed a strong positive correlation between STOML2 expression and Ki67, CD31, VEGFC and PD-1 of CD8+T cell expression. Taken together, our results provided novel insights into the role of STOML2 in CRC inflammatory microenvironment, which may present a therapeutic opportunity for CRC.

An endoplasmic reticulum stress-related signature could robustly predict prognosis and closely associate with response to immunotherapy in pancreatic ductal adenocarcinoma.

PURPOSE: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors. Endoplasmic reticulum stress (ERS) plays an essential role in PDAC progression. Here, we aim to identify the ERS-related genes in PDAC and build reliable risk models for diagnosis, prognosis and immunotherapy response of PDAC patients as well as investigate the potential mechanism. METHODS: We obtained PDAC cohorts with transcriptional profiles and clinical data from the ArrayExpress, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Univariate Cox regression, LASSO regression and multivariate Cox regression analyses were used to construct an ERS-related prognostic signature. The CIBERSORT and ssGSEA algorithms were applied to explore the correlation between the prognostic signature and immune cell infiltration and immune-related pathways. The GDSC database and TIDE algorithm were used to predict responses to chemotherapy and immunotherapy, identifying potential drugs for treating patients with PDAC. RESULTS: We established and validated an ERS-related prognostic signature comprising eight genes (HMOX1, TGFB1, JSRP1, GAPDH, CAV1, CHRNE, CD74 and ERN2). Patients with higher risk scores displayed worse outcomes than those with lower risk scores. PDAC patients in low-risk groups might benefit from immunotherapy. Dasatinib and lapatinib might have potential therapeutic implications in high-risk PDAC patients. CONCLUSION: We established and validated an ERS-related prognostic signature comprising eight genes to predict the overall survival outcome of PDAC patients, which closely correlating with the response to immunotherapy and sensitivity to anti-tumor drugs, as well as could be beneficial for formulating clinical strategies and administering individualized treatments.

Identification of an unfolded protein response-related signature for predicting the prognosis of pancreatic ductal adenocarcinoma.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive lethal malignancy. An effective prognosis prediction model is urgently needed for treatment optimization. Methods: The differentially expressed unfolded protein response (UPR)‒related genes between pancreatic tumor and normal tissue were analyzed using the TCGA-PDAC dataset, and these genes that overlapped with UPR‒related prognostic genes from the E-MTAB-6134 dataset were further analyzed. Univariate, LASSO and multivariate Cox regression analyses were applied to establish a prognostic gene signature, which was evaluated by Kaplan‒Meier curve and receiver operating characteristic (ROC) analyses. E‒MTAB‒6134 was set as the training dataset, while TCGA-PDAC, GSE21501 and ICGC-PACA-AU were used for external validation. Subsequently, a nomogram integrating risk scores and clinical parameters was established, and gene set enrichment analysis (GSEA), tumor immunity analysis and drug sensitivity analysis were conducted. Results: A UPR-related signature comprising twelve genes was constructed and divided PDAC patients into high- and low-risk groups based on the median risk score. The UPR-related signature accurately predicted the prognosis and acted as an independent prognostic factor of PDAC patients, and the AUCs of the UPR-related signature in predicting PDAC prognosis at 1, 2 and 3 years were all more than 0.7 in the training and validation datasets. The UPR-related signature showed excellent performance in outcome prediction even in different clinicopathological subgroups, including the female (p<0.0001), male (p<0.0001), grade 1/2 (p<0.0001), grade 3 (p=0.028), N0 (p=0.043), N1 (p<0.001), and R0 (p<0.0001) groups. Furthermore, multiple immune-related pathways were enriched in the low-risk group, and risk scores in the low-risk group were also associated with significantly higher levels of tumor-infiltrating lymphocytes (TILs). In addition, DepMap drug sensitivity analysis and our validation experiment showed that PDAC cell lines with high UPR-related risk scores or UPR activation are more sensitive to floxuridine, which is used as an antineoplastic agent. Conclusion: Herein, we identified a novel UPR-related prognostic signature that showed high value in predicting survival in patients with PDAC. Targeting these UPR-related genes might be an alternative for PDAC therapy. Further experimental studies are required to reveal how these genes mediate ER stress and PDAC progression.