Integrated Analysis of Tumor Mutation Burden and Immune Infiltrates in Hepatocellular Carcinoma

Comprehensive investigation in oncogenic functions and immunological roles of NCBP2 and its validation in prostate cancer

BACKGROUND

Nuclear cap-binding protein 2 (NCBP2), as the component of the cap-binding complex, participates in a number of biological processes, including pre-mRNA splicing, transcript export, translation regulation and other gene expression steps. However, the role of NCBP2 on the tumor cells and immune microenvironment remains unclear. To systematically analyze and validate functions of NCBP2, we performed a pan-cancer analysis using multiple approaches.

METHODS

The data in this study were derived from sequencing, mutation, and methylation data in the TCGA cohort, normal sample sequencing data in the GTEx project, and cell line expression profile data in the CCLE database.

RESULTS

Survival analyses including the Cox proportional-hazards model and log-rank test revealed the poor prognostic role of NCBP2 in multiple tumors. We further validated the oncogenic ability of NCBP2 in prostate cancer cell lines, organoids and tumor-bearing mice. A negative correlation was observed between NCBP2 expression and immune score by the ESTIMATE algorithm. Simultaneously, the NCBP2-induced immunosuppressive microenvironment might be related to the decline in CD8+T cells and the increase in regulatory T cells and neutrophils, examined by flow cytometry experiments for NCBP2 overexpressed tumor-bearing mice.

CONCLUSION

This research offered strong proof supporting NCBP2 as the prognostic marker and the therapeutic target in the future.

Prognostic model and ceRNA network of m7G- and radiosensitivity-related genes in hepatocellular carcinoma

Background

Radiotherapy is an effective treatment for hepatocellular carcinoma (HCC). Recent studies indicated that N7-methylguanosine (m7G)-associated genes are involved in radioresistance and prognosis of HCC. However, the prognostic value and underlying mechanism of m7G-and radiosensitivity-associated genes are still lacking.

Methods

The related statistics of HCC were downloaded from The Cancer Genome Atlas (TCGA). M7G- and radiosensitivity-associated genes were screened and evaluated using correlation, differential, univariate, and multivariate analysis. The least absolute shrinkage and selection operator (LASSO) algorithm was used to establish a prognostic model. Prognostic efficacy, functional analysis, immune cell infiltration,and drug sensitivity of the prognostic model were assessed. The ceRNA network was predicted and evaluated through the StarBase database, correlation analysis, expression analysis, and survival analysis.

Result

METTL1, EIF3D, NCBP2, and WDR4 participated in prognosis model construction. The favorable prediction efficiency has been verified in both the training and verification sets. Different risk groups have differences in prognosis outcome, function analysis, immune cell infiltration, and drug sensitivity. NCBP2 can be used to predict the prognosis and has excellent potential in immunotherapy. A prognostic ceRNA network based on the NCBP2/miR-122-5p axis was established.

Conclusion

The prognosis model of m7G- and radiosensitivity-related genes is constructed, and widely used in clinical prognosis, immunotherapy, and drug therapy. NCBP2, as a hub gene, may be a prognostic biomarker for HCC and is related to immunotherapy. Establishing the NCBP2/miR-122-5p axis helps study the mechanism of ceRNA and provides new ideas for finding a new candidate biomarker.

Integrated analysis of single-cell sequencing and weighted co-expression network identifies a novel signature based on cellular senescence-related genes to predict prognosis in glioblastoma

BACKGROUND

Glioblastoma (GBM) is a highly aggressive cancer with heavy mortality rates and poor prognosis. Cellular senescence exerts a pivotal influence on the development and progression of various cancers. However, the underlying effect of cellular senescence on the outcomes of patients with GBM remains to be elucidated.

METHODS

Transcriptome RNA sequencing data with clinical information and single-cell sequencing data of GBM cases were obtained from CGGA, TCGA, and GEO (GSE84465) databases respectively. Single-sample gene set enrichment analysis (ssGSEA) analysis was utilized to calculate the cellular senescence score. WGCNA analysis was employed to ascertain the key gene modules and identify differentially expressed genes (DEGs) associated with the cellular senescence score in GBM. The prognostic senescence-related risk model was developed by least absolute shrinkage and selection operator (LASSO) regression analyses. The immune infiltration level was calculated by microenvironment cell populations counter (MCPcounter), ssGSEA, and xCell algorithms. Potential anti-cancer small molecular compounds of GBM were estimated by "oncoPredict" R package.

RESULTS

A total of 150 DEGs were selected from the pink module through WGCNA analysis. The risk-scoring model was constructed based on 5 cell senescence-associated genes (CCDC151, DRC1, C2orf73, CCDC13, and WDR63). Patients in low-risk group had a better prognostic value compared to those in high-risk group. The nomogram exhibited excellent predictive performance in assessing the survival outcomes of patients with GBM. Top 30 potential anti-cancer small molecular compounds with higher drug sensitivity scores were predicted.

CONCLUSION

Cellular senescence-related genes and clusters in GBM have the potential to provide valuable insights in prognosis and guide clinical decisions.

Exploration of the role of oxidative stress-related genes in LPS-induced acute lung injury via bioinformatics and experimental studies

During the progression of acute lung injury (ALI), oxidative stress and inflammatory responses always promote each other. The datasets analyzed in this research were acquired from the Gene Expression Omnibus (GEO) database. The Weighted Gene Co-expression Network Analysis (WGCNA) and limma package were used to obtain the ALI-related genes (ALIRGs) and differentially expressed genes (DEGs), respectively. In total, two biological markers (Gch1 and Tnfaip3) related to oxidative stress were identified by machine learning algorithms, Receiver Operator Characteristic (ROC), and differential expression analyses. The area under the curve (AUC) value of biological markers was greater than 0.9, indicating an excellent power to distinguish between ALI and control groups. Moreover, 15 differential immune cells were selected between the ALI and control samples, and they were correlated to biological markers. The transcription factor (TF)-microRNA (miRNA)-Target network was constructed to explore the potential regulatory mechanisms. Finally, based on the quantitative reverse transcription polymerase chain reaction (qRT-PCR), the expression of Gch1 and Tnfaip3 was significantly higher in ALI lung tissue than in healthy controls. In conclusion, the differences in expression profiles between ALI and normal controls were found, and two biological markers were identified, providing a research basis for further understanding the pathogenesis of ALI.

Clinical plasma cells-related genes to aid therapy in colon cancer

The tumor immune microenvironment (TIME) of colon cancer (CC) has been associated with extensive immune cell infiltration (IMI). Increasing evidence demonstrated that plasma cells (PC) have an extremely important role in advance of antitumor immunity. Nonetheless, there is a lack of comprehensive analyses of PC infiltration in clinical prognosis and immunotherapy in CC. This study systematically addressed the gene expression model and clinical information of CC patients. Clinical samples were obtained from the TCGA (The Cancer Genome Atlas) databases. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), GSVA, and the MAlignant Tumors using Expression data (ESTIMATE) algorithm were employed to research the potential mechanism and pathways. Immunophenoscore (IPS) was obtained to evaluate the immunotherapeutic significance of risk score. Half maximal inhibitory concentration (IC50) of chemotherapeutic medicine was predicted by employing the pRRophetic algorithm. A total of 513 CC samples (including 472 tumor samples and 41 normal samples) were collected from the TCGA-GDC database. Significant black modules and 313 candidate genes were considered PC-related genes by accessing WGCNA. Five pivotal genes were established through multiple analyses, which revealed excellent prognostic. The underlying correlation between risk score with tumor mutation burden (TMB) was further explored. In addition, the risk score was obviously correlated with various tumor immune microenvironment (TIME). Also, risk CC samples showed various signaling pathways activity and different pivotal sensitivities to administering chemotherapy. Finally, the biological roles of the CD177 gene were uncovered in CC.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.