Gene/Protein Disease Symptom Drug Enzyme Compound
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Query: UMLS:C0476089 (endometrial cancer)
 11,379 document(s) hit in 31,850,051 MEDLINE articles (0.00 seconds)

Immunotherapy has provided a promising therapeutic strategy for endometrial cancer (EC). The present study aims to develop a prognostic classifier based on immune-related genes (IRGs) to stratify EC patients. A total of 15 prognosis-related IRGs were further filtrated by multivariate Cox regression: LTA, TMSB15A, S100A14, PLA2G2A, PDGFRA, CLDN4, CTF1, PRLH, PTN, SST, HTR3E, NRP1, RORA, THRA and CBLC. A prognostic signature was constructed to split EC patients into the high-risk and low-risk group with statistically different survival outcomes, indicating good potential for the prognostic signature in survival surveillance. Furthermore, five compounds with potential anti-tumor effects were selected, including ciclopirox, ikarugamycin, vincamine, mevalolactone, and thiamazole. The abundance of follicular helper T cells, regulatory T cells and M0 macrophages were significantly enhanced in the high-risk group while resting memory CD4+ T cells, gamma delta T cells, M2 macrophages and resting mast cells were markedly elevated in the low-risk group. Memory activated CD4+ T cells, CD8+ T cells and activated mast cells were three most correlative with riskscore. An immunophenoscore (IPS) analysis revealed that patients of the low-risk group had a higher IPS and more inclined to respond to immune checkpoint inhibitors. Mutation analysis showed that patients of the low-risk group represented more tumor mutation burden but low riskscore, thus getting better prognosis. Patients of the low-risk group were more sensitive for gemcitabine, bleomycin, vinblastine, vinorelbine and methotrexate by prediction. We constructed a potential prognostic model and might offer new insight on the identification of new immune-related biomarkers and target therapy in EC.
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PMID:Development of an immune gene prognostic classifier for survival prediction and respond to immunocheckpoint inhibitor therapy/chemotherapy in endometrial cancer. 3261 57

Cervical cancer and endometrial cancer remain serious threats to women's health. Even though some patients can be treated with surgery plus chemoradiotherapy as a conventional option, the overall efficacy is deemed unsatisfactory. As such, the development for new treatment approaches is truly necessary. In recent years, immunotherapy has been widely used in clinical practice and it is an area of great interest that researchers are keeping attention on. However, a thorough immune-related genes (IRGs) study for cervical cancer and endometrial cancer is still lacking. We therefore aim to make a comprehensive evaluation of IRGs through bioinformatics and large databases, and also investigate the relationship between the two types of cancer. We reviewed the transcriptome RNAs of IRGs and clinical data based on the TCGA database. Survival-associated IRGs in cervical/endometrial cancer were identified using univariable and multivariable Cox proportional-hazard regression analysis for developing an IRG signature model to evaluate the risk of patients. In the end, this model was validated based on the enrichment analyses through GO, KEGG, and GSEA pathways, Kaplan-Meier survival curve, ROC curves, and immune cell infiltration. Our results showed that out of 25/23 survival-associated IRGs for cervical/endometrial cancer, 13/12 warranted further examination by multivariate Cox proportional-hazard regression analysis and were selected to develop an IRGs signature model. As a result, enrichment analyses for high-risk groups indicated main enriched pathways were associated with tumor development and progression, and statistical differences were found between high-risk and low-risk groups as shown by Kaplan-Meier survival curve. This model could be used as an independent measure for risk assessment and was considered relevant to immune cell infiltration, but it had nothing to do with clinicopathological characteristics. In summary, based on comprehensive analysis, we obtained the IRGs signature model in cervical cancer (LTA, TFRC, TYK2, DLL4, CSK, JUND, NFATC4, SBDS, FLT1, IL17RD, IL3RA, SDC1, PLAU) and endometrial cancer (LTA, PSMC4, KAL1, TNF, SBDS, HDGF, LTB, HTR3E, NR2F1, NR3C1, PGR, CBLC), which can effectively evaluate the prognosis and risk of patients and provide justification in immunology for further researches.
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PMID:Prognostic Implications of Immune-Related Genes' (IRGs) Signature Models in Cervical Cancer and Endometrial Cancer. 3279 81