Background. The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME‐related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer. Methods. We calculated immune and stromal scores for 385 colon cancer samples from The Cancer Genome Atlas (TCGA) database using the ESTIMATE algorithm. We identified nine TME‐related prognostic genes using Cox regression analysis. We evaluated associations between protein expression, extent of immune cell infiltrate, and patient survival. We calculated risk scores and built a clinical predictive model for the TME‐related gene signature. Receiver operating characteristic (ROC) curves were generated to assess the predictive power of the signature. We estimated the half‐maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients using the pRRophetic algorithm. The expression of immune checkpoint genes was evaluated. Results. High immune and stromal scores are significantly associated with poor overall survival (p < 0.05). We identified 773 differential TME‐related prognostic genes associated with survival; these genes were enriched in immune‐related pathways. Nine key prognostic genes were identified and were used to construct a TME‐related prognostic signature: CADM3, LEP, CD1B, PDE1B, CCL22, ABI3BP, IGLON5, SELE, and TGFB1. This signature identified a high‐risk group with worse survival outcomes, based on Kaplan‐Meier analysis. A nomogram composed of clinicopathological factors and risk score exhibited good accuracy. Drug sensitivity analysis identified no difference in sensitivity between the high‐risk and low‐risk groups. High‐risk patients had higher expression of PD‐1, PDL‐1, and CTLA‐4 and lower expression of LAG‐3 and VSIR. Infiltration of dendritic cells was higher in the high‐risk group. Conclusions. We identified a novel prognostic TME‐related gene expression signature in colon cancer. Stratification of patients based on this gene signature could be used to improve outcomes and guide better therapy for colon cancer patients.

Rationale: Abnormal metabolic states contribute to a variety of diseases, including cancer. RNA modifications have diverse biological functions and are implicated in cancer development, including gastric cancer (GC). However, the direct relationship between glucose homeostasis and 4-acetylcytosine (ac4C) modification in GC remains unclear.

Cuproptosis is a newly discovered non-apoptotic form of cell death that may be related to the development of tumors. Nonetheless, the potential role of cuproptosis-related lncRNAs in tumor microenvironment (TME) formation and patient-tailored treatment optimization of gastric cancer (GC) is still unclear. In this study, the six-lncRNA signature was constructed to quantify the molecular patterns of GC using LASSO-Cox regression model. Receiver operating characteristic (ROC) curves, C-index curves, independent prognostic analysis and principal component analysis (PCA) were conducted to verify and evaluate the model. The results showed that this risk model was accurate and reliable in predicting GC patient survival. In addition, two distinct subgroups were identified based on the risk model, which showed significant difference in biological functions of the associated genes, TME scores, characteristics of infiltrating immune cells and immunotherapy responses. We found that the high-risk subgroup was associated with immune activation and tumor-related pathways. Furthermore, compared with the low-risk subgroup, the high-risk subgroup had higher TME scores, richer immune cell infiltration and a better immunotherapy response. To accurately identify immune cold tumors and hot tumors, all samples of GC were divided into four distinct clusters by consensus clustering. Among them, Cluster 3 was identified as an immune hot tumor and was more sensitive to immunotherapy. Overall, this study demonstrates that cuproptosis-related lncRNAs could accurately predict the prognosis of patients with GC, help make a distinction between immune cold tumors and hot tumors and provide a basis for the precision medicine of GC.

Purpose: N6-methyladenosine (m6A) RNA modification plays an important role in regulating tumor microenvironment (TME) infiltration. However, the relationship between the expression pattern of m6A-related long non-coding RNAs (lncRNAs) and the immune microenvironment of gastric cancer (GC) is unclear. Methods: In this study, 23 m6A-related lncRNAs were identified by Pearson’s correlation analysis and univariate Cox regression analysis. According to the expression of these lncRNAs, we identified two distinct molecular clusters by consensus clustering and compared the differences of the TME and enriched pathways between the two clusters. We further constructed a prognostic risk signature and verified it using The Cancer Genome Atlas training and testing cohorts. Results: The results showed that cluster 1 was associated with tumor-related and immune activation-related pathways. In addition, cluster 1 was also associated with higher ImmuneScore, StromalScore, and ESTIMATEScore. The results of the stratified survival analysis and independent prognosis analysis indicated that the risk signature is an independent prognostic indicator for patients with GC. In addition, it can effectively predict survival status in patients with different clinical characteristics. Furthermore, we found that the risk signature was associated with a variety of tumor-infiltrating immune cells, and that low risk scores were significantly correlated with high expression of programmed death-1 (PD-1) and cytotoxic T-lymphocyte associated protein 4 (CTLA4), as well as sensitivity to chemotherapeutic drugs (eg, fluorouracil and oxaliplatin). Conclusion: This evidence contributes to our understanding of the regulation of TME infiltration by m6A-related lncRNAs and may lead to more effective immunotherapy and chemotherapy for patients with GC.

Hepatocellular carcinoma (HCC) is a common malignancy with a poor prognosis worldwide. However, the pathogenesis of HCC remains poorly understood. In this study, we found that NOL12 was significantly overexpressed in independent HCC datasets from TCGA database. We confirmed that the expression level of NOL12 was upregulated in human HCC tissues and cell lines by RT‐qPCR. High expression of NOL12 is associated with worse reduced overall survival (OS), high pathological grade, node metastasis, and advanced clinical stage in patients with HCC. Moreover, knockdown of NOL12 dramatically inhibits the proliferation and metastasis of HCC cells in vitro and in vivo. CIBERSORTx analysis revealed that twelve types of tumor‐infiltrating immune cells (TICs) are correlated with NOL12 expression. The risk signature based on 8 NOL12‐related genes is an independent prognostic factor for patients with HCC. The OS rate of patients in the low‐risk score group was better than that in the high‐risk score group. In addition, the total tumor mutation burden (TMB) in the high‐risk score group increased significantly, and the risk scores could be used as an alternative indicator of immune checkpoint inhibitor (ICI) response. In conclusion, our findings indicated that NOL12 might be involved in the progression of HCC and can be used as a potential therapeutic target. Moreover, the NOL12‐related risk signature may have predictive relevance with regard to ICI therapy.