Identification and validation of a m7G-related lncRNA signature for predicting the prognosis and therapy response in hepatocellular carcinoma.

BACKGROUND: N7-methylguanosine (m7G) is one of the most common RNA posttranscriptional modifications; however, its potential role in hepatocellular carcinoma (HCC) remains unknown. We developed a prediction signature based on m7G-related long noncoding RNAs (lncRNAs) to predict HCC prognosis and provide a reference for immunotherapy and chemotherapy. METHODS: RNA-seq data from The Cancer Genome Atlas (TCGA) database and relevant clinical data were used. Univariate and multivariate Cox regression analyses were conducted to identify m7G-related lncRNAs with prognostic value to build a predictive signature. We evaluated the prognostic value and clinical relevance of this signature and explored the correlation between the predictive signature and the chemotherapy treatment response of HCC. Moreover, an in vitro study to validate the function of CASC19 was performed. RESULTS: Six m7G-related lncRNAs were identified to create a signature. This signature was considered an independent risk factor for the prognosis of patients with HCC. TIDE analyses showed that the high-risk group might be more sensitive to immunotherapy. ssGSEA indicated that the predictive signature was strongly related to the immune activities of HCC. HCC in high-risk patients was more sensitive to the common chemotherapy drugs bleomycin, doxorubicin, gemcitabine, and lenalidomide. In vitro knockdown of CASC19 inhibited the proliferation, migration and invasion of HCC cells. CONCLUSION: We established a 6 m7G-related lncRNA signature that may assist in predicting the prognosis and response to chemotherapy and immunotherapy of HCC.

Integrated Analysis of Single-Cell RNA-Seq and Bulk RNA-Seq Combined with Multiple Machine Learning Identified a Novel Immune Signature in Diabetic Nephropathy.

Background: Increasing evidence suggests that immune modulation contributes to the pathogenesis and progression of diabetic nephropathy (DN). However, the role of immune modulation in DN has not been elucidated. The purpose of this study was to search for potential immune-related therapeutic targets and molecular mechanisms of DN. Methods: Gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. A total of 1793 immune-related genes were acquired from the Immunology Database and Analysis Portal (ImmPort). Weighted gene co-expression network analysis (WGCNA) was performed for GSE142025, and the red and turquoise co-expression modules were found to be key for DN progression. We utilized four machine learning algorithms, namely, random forest (RF), support vector machine (SVM), adaptive boosting (AdaBoost), and k-nearest neighbor (KNN), to evaluate the diagnostic value of hub genes. Immune infiltration patterns were analyzed using the CIBERSORT algorithm, and the correlation between immune cell type abundance and hub gene expression was also investigated. Results: A total of 77 immune-related genes of advanced DN were selected for subsequent analyzes. Functional enrichment analysis showed that the regulation of cytokine-cytokine receptor interactions and immune cell function play a corresponding role in the progression of DN. The final 10 hub genes were identified through multiple datasets. In addition, the expression levels of the identified hub genes were corroborated through a rat model. The RF model exhibited the highest AUC. CIBERSORT analysis and single-cell sequencing analysis revealed changes in immune infiltration patterns between control subjects and DN patients. Several potential drugs to reverse the altered hub genes were identified through the Drug-Gene Interaction database (DGIdb). Conclusion: This pioneering work provided a novel immunological perspective on the progression of DN, identifying key immune-related genes and potential drug targets, thus stimulating future mechanistic research and therapeutic target identification for DN.

Construction and validation of a prognostic signature based on necroptosis-related genes in hepatocellular carcinoma.

BACKGROUND: Necroptosis is a necrotic programmed cell death with potent immunogenicity. Due to the dual effects of necroptosis on tumor growth, metastasis and immunosuppression, we evaluated the prognostic value of necroptosis-related genes (NRGs) in hepatocellular carcinoma (HCC). METHODS: We first analyzed RNA sequencing and clinical HCC patient data obtained to develop an NRG prognostic signature based on the TCGA dataset. Differentially expressed NRGs were further evaluated by GO and KEGG pathway analyses. Next, we conducted univariate and multivariate Cox regression analyses to build a prognostic model. We also used the dataset obtained from the International Cancer Genome Consortium (ICGC) database to verify the signature. The Tumor Immune Dysfunction and Exclusion (TIDE) algorithm was used to investigate the immunotherapy response. Furthermore, we investigated the relationship between the prediction signature and chemotherapy treatment response in HCC. RESULTS: We first identified 36 differentially expressed genes out of 159 NRGs in hepatocellular carcinoma. Enrichment analysis showed that they were mainly enriched in the necroptosis pathway. Four NRGs were screened by Cox regression analysis to establish a prognostic model. The survival analysis revealed that the overall survival of patients with high-risk scores was significantly shorter than that of patients with low-risk scores. The nomogram demonstrated satisfactory discrimination and calibration. The calibration curves validated a fine concordance between the nomogram prediction and actual observation. The efficacy of the necroptosis-related signature was also validated by an independent dataset and immunohistochemistry experiments. TIDE analysis revealed that patients in the high-risk group were possibly more susceptible to immunotherapy. Furthermore, high-risk patients were found to be more sensitive to conventional chemotherapeutic medicines such as bleomycin, bortezomib, and imatinib. CONCLUSION: We identified 4 necroptosis-related genes and established a prognostic risk model that could potentially predict prognosis and response to chemotherapy and immunotherapy in HCC patients in the future.