Background: Endometrial cancer (UCEC) is a highly heterogeneous gynecologic malignancy that exhibits variable prognostic outcomes and responses to immunotherapy. The Familial sequence similarity (FAM) gene family is known to contribute to the pathogenesis of various malignancies, but the extent of their involvement in UCEC has not been systematically studied. This investigation aimed to develop a robust risk profile based on FAM family genes (FFGs) to predict the prognosis and suitability for immunotherapy in UCEC patients.Methods: Using the TCGA-UCEC cohort from The Cancer Genome Atlas (TCGA) database, we obtained expression profiles of FFGs from 552 UCEC and 35 normal samples, and analyzed the expression patterns and prognostic relevance of 363 FAM family genes. The UCEC samples were randomly divided into training and test sets (1:1), and univariate Cox regression analysis and Lasso Cox regression analysis were conducted to identify the differentially expressed genes (FAM13C, FAM110B, and FAM72A) that were significantly associated with prognosis. A prognostic risk scoring system was constructed based on these three gene characteristics using multivariate Cox proportional risk regression. The clinical potential and immune status of FFGs were analyzed using CiberSort, SSGSEA, and tumor immune dysfunction and rejection (TIDE) algorithms. qRT-PCR and IHC for detecting the expression levels of 3-FFGs.Results: Three FFGs, namely, FAM13C, FAM110B, and FAM72A, were identified as strongly associated with the prognosis of UCEC and effective predictors of UCEC prognosis. Multivariate analysis demonstrated that the developed model was an independent predictor of UCEC, and that patients in the low-risk group had better overall survival than those in the high-risk group. The nomogram constructed from clinical characteristics and risk scores exhibited good prognostic power. Patients in the low-risk group exhibited a higher tumor mutational load (TMB) and were more likely to benefit from immunotherapy.Conclusion: This study successfully developed and validated novel biomarkers based on FFGs for predicting the prognosis and immune status of UCEC patients. The identified FFGs can accurately assess the prognosis of UCEC patients and facilitate the identification of specific subgroups of patients who may benefit from personalized treatment with immunotherapy and chemotherapy.

Recent studies demonstrated that pyroptosis plays a crucial role in shaping the tumor-immune microenvironment. However, the influence of pyroptosis on lower-grade glioma regarding immunotherapy and targeted therapy is still unknown. This study analyzed the variations of 33 pyroptosis-related genes in lower-grade glioma and normal tissues. Our study found considerable genetic and expression alterations in heterogeneity among lower-grade gliomas and normal brain tissues. There are two pyroptosis phenotypes in lower-grade glioma, and they exhibited differences in cell infiltration characteristics and clinical characters. Then, a PyroScore model using the lasso-cox method was constructed to measure the level of pyroptosis in each patient. PyroScore can refine the lower-grade glioma patients with a stratified prognosis and a distinct tumor immune microenvironment. Pyscore may also be an effective factor in predicting potential therapeutic benefits. In silico analysis showed that patients with a lower PyroScore are expected to be more sensitive to targeted therapy and immunotherapy. These findings may enhance our understanding of pyroptosis in lower-grade glioma and might help optimize risk stratification for the survival and personalized management of lower-grade glioma patients.

Background: Necroptosis has been identified recently as a newly recognized programmed cell death that has an impact on tumor progression and prognosis, although the necroptosis-related gene (NRGs) potential prognostic value in skin cutaneous melanoma (SKCM) has not been identified. The aim of this study was to construct a prognostic model of SKCM through NRGs in order to help SKCM patients obtain precise clinical treatment strategies.Methods: RNA sequencing data collected from The Cancer Genome Atlas (TCGA) were used to identify differentially expressed and prognostic NRGs in SKCM. Depending on 10 NRGs via the univariate Cox regression analysis usage and LASSO algorithm, the prognostic risk model had been built. It was further validated by the Gene Expression Omnibus (GEO) database. The prognostic model performance had been assessed using receiver operating characteristic (ROC) curves. We evaluated the predictive power of the prognostic model for tumor microenvironment (TME) and immunotherapy response.Results: We constructed a prognostic model based on 10 NRGs (FASLG, TLR3, ZBP1, TNFRSF1B, USP22, PLK1, GATA3, EGFR, TARDBP, and TNFRSF21) and classified patients into two high- and low-risk groups based on risk scores. The risk score was considered a predictive factor in the two risk groups regarding the Cox regression analysis. A predictive nomogram had been built for providing a more beneficial prognostic indicator for the clinic. Functional enrichment analysis showed significant enrichment of immune-related signaling pathways, a higher degree of immune cell infiltration in the low-risk group than in the high-risk group, a negative correlation between risk scores and most immune checkpoint inhibitors (ICIs), anticancer immunity steps, and a more sensitive response to immunotherapy in the low-risk group.Conclusions: This risk score signature could be applied to assess the prognosis and classify low- and high-risk SKCM patients and help make the immunotherapeutic strategy decision.

Clear cell renal cell carcinoma (ccRCC) is one of the most common renal malignancies worldwide. SLC22A8 plays a key role in renal excretion of organic anions. However, its role in ccRCC remains unclear; therefore, this study aimed to elucidate the relationship between SLC22A8 and ccRCC. The The Cancer Genome Atlas-kidney renal clear cell carcinoma cohort was included in this study. The Wilcoxon signed-rank test and logistic regression were used to analyze the relationship between SLC22A8 expression and clinicopathological characteristics. Multifactorial analysis and Kaplan–Meier survival curves were adopted for correlation between SLC22A8 expression and clinicopathological parameters and overall survival. Utilizing the UALCAN database, the correlation of the expression levels of SLC22A8 DNA methylation in ccRCC was explored. Immunological characterization of SLC22A8 regarding the ccRCC tumor microenvironment was carried out by the single sample Gene Set Enrichment Analysis algorithm and the CIBERSORT algorithm. With the CellMiner database, the analysis of the association between SLC22A8 gene expression and drug sensitivity was further performed. Eventually, gene ontology and Kyoto Encyclopedia of Gene and Genome enrichment analyses were applied to identify the functional and signaling pathways involved in SLC22A8. SLC22A8 expression is associated with age, grade, stage, and tumor status. SLC22A8 protein expression levels, phosphorylated protein levels, and DNA methylation expression levels were lower in ccRCC tissues than in normal tissues, and low methylation levels predicted poor overall survival. Comprehensive analysis of tumor immune infiltration and the tumor microenvironment indicated a higher level of overall immunity in the SLC22A8 low expression group. Gene Enrichment Analysis results showed that low expression of SLC22A8 was associated with immune pathways, such as phagocytosis recognition and humoral immune response. SLC22A8 expression was significantly correlated with survival and immune infiltration in ccRCC and can be used as a prognostic biomarker for ccRCC.

Background Hepatocellular carcinoma (HCC) is one of the leading malignancies in the world due to its progressively increasing incidence. Nonetheless, the impact of ferroptosis and immune microenvironment on prognosis of HCC remains unclear. This research work aims to identify ferroptosis-immune microenvironment-related genes (FIMRGs) which influence the prognosis of HCC, providing a theoretical basis for an in-depth understanding of the pathogenesis and prognosis of HCC. Methods TCGA-LIHC cohort and GSE14520 cohort were enrolled in this study. 167 ferroptosis-related genes (FRGs) were retrieved from the FerrDb data resource. Consensus clustering assessment, immune infiltration assessment, weighted gene co-expression network analysis (WGCNA), Pearson correlation analysis along with Cox regression were used to identify and establish FIMRGs signature in HCC. Univariate coupled with multivariate regression assessment were executed to ascertain the independent prognostic worthiness of the risk score. The tumor microenvironment (TME) and drug sensitivity profiling were compared between the high-HCC risk and low-HCC risk groups. Subsequently, the drugs or molecular compounds targeting each prognostic gene were predicted via the DGIdb database. Results Based on 15 DEFRGs related with prognosis in individuals with HCC, the two clusters with significant prognostic differences have been mined. 9 genes (PTPRO, CRHBP, HMOX1, FABP5, TREM2, ADAMTS14, LILRA6, CAPG, and RGS2) have been identified from 55 FIMRGs, which were adopted to generate and verify the risk score model in the training along with the validation set. Cox regression assessment manifested the risk score to be independent predictive factor. TME analysis and drug sensitivity profiling exhibited remarkable differences in the immune microenvironment between high-HCC and low-HCC risk groups, with the low-HCC risk group being more likely to benefit from chemotherapy. Furthermore, A miRNA-mRNA regulatory network consisting of four prognostic genes and five prognostic miRNAs was formed. Finally, there were four drugs targeting HMOX1, one drug targeting RGS2, CAPG and CRHBP respectively, which have been extracted from the DGIdb database. Conclusion This study proposed a novel and promising prognostic gene signature, which could provide us a panoramic view of the prediction of prognosis for HCC patients.