Interferon-induced transmembrane protein 3 (IFITM3) is an interferon-induced membrane protein, which has been identified as a functional gene in multiple human cancers. The role of IFITM3 in cancer has been preliminarily summarized, but its relationship to antitumor immunity is still unclear. A pancancer analysis was conducted to investigate the expression pattern and immunological role of IFITM3 based on transcriptomic data downloaded from The Cancer Genome Atlas (TCGA) database. Next, correlations between IFITM3 and immunological features in the bladder cancer (BLCA) tumor microenvironment (TME) were assessed. In addition, the role of IFITM3 in estimating the clinical characteristics and the response to various therapies in BLCA was also evaluated. These results were next confirmed in the IMvigor210 cohort and a recruited cohort. In addition, correlations between IFITM3 and emerging immunobiomarkers, such as microbiota and N6-methyladenosine (m6A) genes, were assessed. IFITM3 was enhanced in most tumor tissues in comparison with adjacent tissues. IFITM3 was positively correlated with immunomodulators, tumor-infiltrating immune cells (TIICs), cancer immunity cycles, and inhibitory immune checkpoints. In addition, IFITM3 was associated with an inflamed phenotype and several established molecular subtypes. IFITM3 expression also predicted a notably higher response to chemotherapy, anti-EGFR therapy, and immunotherapy but a low response to anti-ERBB2, anti-ERBB4, and antiangiogenic therapy. In addition, IFITM3 was correlated with immune-related microbiota and m6A genes. In addition to BLCA, IFITM3 is expected to be a marker of high immunogenicity in most human cancers. In conclusion, IFITM3 expression can be used to identify immuno-hot tumors in most cancers, and IFITM3 may be a promising pancancer biomarker to estimate the immunological features of tumors.

Endometrial carcinoma (EnCa) is one of the deadliest gynecological malignancies. The purpose of the current study was to develop an immune-related lncRNA prognostic signature for EnCa. In the current research, a series of systematic bioinformatics analyses were conducted to develop a novel immune-related lncRNA prognostic signature to predict disease-free survival (DFS) and response to immunotherapy and chemotherapy in EnCa. Based on the newly developed signature, immune status and mutational loading between high-and low-risk groups were also compared. A novel 13-lncRNA signature associated with DFS of EnCa patients was ultimately developed using systematic bioinformatics analyses. The prognostic signature allowed us to distinguish samples with different risks with relatively high accuracy. In addition, univariate and multivariate Cox regression analyses confirmed that the signature was an independent factor for predicting DFS in EnCa. Moreover, a predictive nomogram combined with the risk signature and clinical stage was constructed to accurately predict 1-, 2-, 3-, and 5-year DFS of EnCa patients. Additionally, EnCa patients with different levels of risk had markedly different immune statuses and mutational loadings. Our findings indicate that the immune-related 13-lncRNA signature is a promising classifier for prognosis and response to immunotherapy and chemotherapy for EnCa.

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Background: Ovarian cancer (OvCa) is one of the most fatal cancers among females in the world. With growing numbers of individuals diagnosed with OvCa ending in deaths, it is urgent to further explore the potential mechanisms of OvCa oncogenesis and development and related biomarkers. Methods: The gene expression profiles of GSE49997 were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was applied to explore the most potent gene modules associated with the overall survival (OS) and progression-free survival (PFS) events of OvCa patients, and the prognostic values of these genes were exhibited and validated based on data from training and validation sets. Next, protein-protein interaction (PPI) networks were built by GeneMANIA. Besides, enrichment analysis was conducted using DAVID website. Results: According to the WGCNA analysis, a total of eight modules were identified and four hub genes (MM > 0.90) in the blue module were reserved for next analysis. Kaplan-Meier analysis exhibited that these four hub genes were significantly associated with worse OS and PFS in the patient cohort from GSE49997. Moreover, we validated the short-term (4-years) and long-term prognostic values based on the GSE9891 data, respectively. Last, PPI networks analysis, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed several potential mechanisms of four hub genes and their co-operators participating in OvCa progression. Conclusion: Four hub genes (COL6A3, CRISPLD2, FBN1 and SERPINF1) were identified to be associated with the prognosis in OvCa, which might be used as monitoring biomarkers to evaluate survival time of OvCa patients.