Identification of a seven-gene signature predicting clinical outcome of liver cancer based on tumor mutational burden

m1A regulator-mediated methylation modification patterns correlated with autophagy to predict the prognosis of hepatocellular carcinoma

BACKGROUND

N1-methyladenosine (m1A), among the most common internal modifications on RNAs, has a crucial role to play in cancer development. The purpose of this study were systematically investigate the modification characteristics of m1A in hepatocellular carcinoma (HCC) to unveil its potential as an anticancer target and to develop a model related to m1A modification characteristics with biological functions. This model could predict the prognosis for patients with HCC.

METHODS

An integrated analysis of the TCGA-LIHC database was performed to explore the gene signatures and clinical relevance of 10 m1A regulators. Furthermore, the biological pathways regulated by m1A modification patterns were investigated. The risk model was established using the genes that showed differential expression (DEGs) between various m1A modification patterns and autophagy clusters. These in vitro experiments were subsequently designed to validate the role of m1A in HCC cell growth and autophagy. Immunohistochemistry was employed to assess m1A levels and the expression of DEGs from the risk model in HCC tissues and paracancer tissues using tissue microarray.

RESULTS

The risk model, constructed from five DEGs (CDK5R2, TRIM36, DCAF8L, CYP26B, and PAGE1), exhibited significant prognostic value in predicting survival rates among individuals with HCC. Moreover, HCC tissues showed decreased levels of m1A compared to paracancer tissues. Furthermore, the low m1A level group indicated a poorer clinical outcome for patients with HCC. Additionally, m1A modification may positively influence autophagy regulation, thereby inhibiting HCC cells proliferation under nutrient deficiency conditions.

CONCLUSIONS

The risk model, comprising m1A regulators correlated with autophagy and constructed from five DEGs, could be instrumental in predicting HCC prognosis. The reduced level of m1A may represent a potential target for anti-HCC strategies.

Multilayered insights: a machine learning approach for personalized prognostic assessment in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a complex malignancy, and precise prognosis assessment is vital for personalized treatment decisions.

Objective

This study aimed to develop a multi-level prognostic risk model for HCC, offering individualized prognosis assessment and treatment guidance.

Methods

By utilizing data from The Cancer Genome Atlas (TCGA) and the Surveillance, Epidemiology, and End Results (SEER) database, we performed differential gene expression analysis to identify genes associated with survival in HCC patients. The HCC Differential Gene Prognostic Model (HCC-DGPM) was developed through multivariate Cox regression. Clinical indicators were incorporated into the HCC-DGPM using Cox regression, leading to the creation of the HCC Multilevel Prognostic Model (HCC-MLPM). Immune function was evaluated using single-sample Gene Set Enrichment Analysis (ssGSEA), and immune cell infiltration was assessed. Patient responsiveness to immunotherapy was evaluated using the Immunophenoscore (IPS). Clinical drug responsiveness was investigated using drug-related information from the TCGA database. Cox regression, Kaplan-Meier analysis, and trend association tests were conducted.

Results

Seven differentially expressed genes from the TCGA database were used to construct the HCC-DGPM. Additionally, four clinical indicators associated with survival were identified from the SEER database for model adjustment. The adjusted HCC-MLPM showed significantly improved discriminative capacity (AUC=0.819 vs. 0.724). External validation involving 153 HCC patients from the International Cancer Genome Consortium (ICGC) database verified the performance of the HCC-MLPM (AUC=0.776). Significantly, the HCC-MLPM exhibited predictive capacity for patient response to immunotherapy and clinical drug efficacy (P < 0.05).

Conclusion

This study offers comprehensive insights into HCC prognosis and develops predictive models to enhance patient outcomes. The evaluation of immune function, immune cell infiltration, and clinical drug responsiveness enhances our comprehension and management of HCC.

An integrative analysis of GEO data to identify possible therapeutic biomarkers of prostate cancer and targeting potential protein through Zea mays phytochemicals by virtual screening approaches

Prostate cancer (PC) is a prevalent type of cancer among men. Delaying the treatment of patients with upgraded or upstaged cancer may lead to unmanageable circumstances. The aim of this study is to contribute to the finding of biomarkers that are specific to PC and identify drug candidates derived from plants. The information about cancer is critical for clinicians to make decisions about patient treatment in the era of precision medicine. Advances in genomics technology have opened up new possibilities for identifying genes that are associated with cancer, including PC. This study identifies novel differentially expressed genes for PC. The seven PC microarray datasets were selected from the National Center for Biotechnology Information (NCBI)/Gene Expression Omnibus (GEO). The differentially expressed genes (DEGs) were found based on a fold change of |logFC| ≥ 1 and an adjusted p-value of <0.05. The DEGs were further studied using several bioinformatics tools, including STRING, CytoHubba, SRplot, Coremine Medical database, FunRich and GeneMANIA, cBioPortal. The six new potential biomarkers, GAGE2A, GAGE12G, GAGE2E, GAGE13, GAGE12F and CSAG1 were identified. These biomarkers are associated with biological processes (BPs) such as cell division, and gene expression regulation, so these genes may have a crucial role in PC progression and may serve as potential biomarkers for PC. A total of 497 phytochemicals from corn plants have been screened against the target protein and found LTS0176591 as the best lead molecule with docking score of -6.31 kcal/mol. Further, molecular mechanics-generalized born surface area (MM-GBSA), molecular dynamics simulation, principal component analysis (PCA), free energy landscape (FEL) and molecular mechanics-Poisson-Boltzmann surface area (MM-PBSA) were carried out to validate the findings.Communicated by Ramaswamy H. Sarma.

High expression of cuproptosis-related gene FDX1 in relation to good prognosis and immune cells infiltration in colon adenocarcinoma (COAD)

BACKGROUND

Cuproptosis induced by FDX1 is a newly discovered mechanism regulating cell death. However, the role of FDX1 in the pathogenesis of colon adenocarcinoma (COAD) remains to be studied.

METHODS

FDX1 expression was analyzed with The Cancer Genome Atlas (TCGA) database and Human Protein Atlas (HPA) database. Association between FDX1 expression and COAD prognosis was investigated via the Kaplan-Meier (KM) survival curve. The differentially expressed genes (DEGs) of FDX1 were screened with R packages and the PPI were constructed via STRING database. Cytoscape software was used to detect the most profound modules in the PPIs network. CancerSEA database was used to analyze the effect of FDX1 expression levels on different functional status of COAD cells. The relationship between FDX1 expression and immune infiltration of COAD was analyzed by TIMER2.0 database. The COAD patients with high expression of FDX1 by Western blot, and the levels of immune infiltration were measured by flow cytometry.

RESULTS

FDX1 was low expressed in most cancers, such as BRCA, KICH, and COAD. The overall survival (OS) and disease-specific survival (DSS) of COAD with high FDX1 expression were better than that of the low expression group. GO-KEGG enrichment analysis revealed that FDX1 and its co-expressed genes played an important role in the pathogenesis of COAD. Moreover, FDX1 expression in COAD were positively associated with "quiescence" and "inflammation" but negatively correlated with "invasion". FDX1 expression was positively correlated with infiltration levels of CD8⁺ T cells, NK cells, and neutrophils. Oppositely, FDX1 expression was negatively correlated with that of CD4⁺ T cells and cancer-associated fibroblasts (CAFs). Finally, 6 COAD patients with high expression of FDX1 were screened, and the proportion of CD8⁺ T cells in cancer tissues of these patients was significantly higher than that in paracancerous, while the CD4⁺ T cells presented the opposite pattern.

CONCLUSION

FDX1 plays a role in inducing cuproptosis and modulating tumor immunity, which could be considered as potential therapeutic targets in COAD.

Development and evaluation of a risk score model based on a WNT score gene-associated signature for predicting the clinical outcome and the tumour microenvironment of hepatocellular carcinoma

Background: Hepatocellular carcinoma (HCC) is currently one of the most life-threatening diseases worldwide. However, the factors, genes, and processes involved in the mechanisms of HCC initiation, development, and metastasis remain to be identified.Methods: WNT signalling pathways may play important roles in cancer initiation and progression. Thus, it would be informative to construct a WNT signature-based gene model for the prognosis of HCC and the prediction of therapeutic efficacy. We curated genomic profiles for HCC from The Cancer Genome Atlas (TCGA) and divided them into training and internal validation datasets. We also used samples from GSE14520 and HCCDB18 as validation datasets and clustered them by ConsensusClusterPlus analysis. We applied WebGestaltR to the WNT score-associated differentially expressed genes (DEGs) and conducted a signalling pathway enrichment analysis. We assessed the tumour immune microenvironment with ESTIMATE, Microenvironment Cell Populations (MCP)-counter, single-sample gene set enrichment analysis (ssGSEA), and tumour immune dysfunction and exclusion (TIDE).Results: We performed a least absolute shrinkage and selection operator (LASSO) regression analysis to identify the prognosis-related hub genes, identified the risk and protective factor genes associated with HCC, classified them into two clusters, and found that Cluster 2 had a significantly better prognosis than Cluster 1. Moreover, the latter had advanced clinical features compared with the former. Uridine-cytosine kinase 1 (UCK1), myristoylated alanine-rich C-kinase substrate-like protein 1 (MARCKSL1), P-antigen family member 1 (PAGE1), and killer cell lectin-like receptor B1 (KLRB1) were detected and used to construct a simplified prognostic model for HCC. The high risk score subgroup showed a poorer prognosis than the low risk score subgroup, and the model assessed HCC prognosis consistently and effectively.Conclusions: The WNT score-related gene-based model designed and evaluated herein had strong prognostic and predictive ability for HCC and could, therefore, facilitate decision-making in the prognosis and therapeutic efficacy assessment of HCC.

Deciphering a mitochondria-related signature to supervise prognosis and immunotherapy in hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a major public health problem in humans. The imbalance of mitochondrial function has been discovered to be closely related to the development of cancer recently. However, the role of mitochondrial-related genes in HCC remains unclear.

Methods

The RNA-sequencing profiles and patient information of 365 samples were derived from the Cancer Genome Atlas (TCGA) dataset. The mitochondria-related prognostic model was established by univariate Cox regression analysis and LASSO Cox regression analysis. We further determined the differences in immunity and drug sensitivity between low- and high-risk groups. Validation data were obtained from the International Cancer Genome Consortium (ICGC) dataset of patients with HCC. The protein and mRNA expression of six mitochondria-related genes in tissues and cell lines was verified by immunohistochemistry and qRT-PCR.

Results

The six mitochondria-related gene signature was constructed for better prognosis forecasting and immunity, based on which patients were divided into high-risk and low-risk groups. The ROC curve, nomogram, and calibration curve exhibited admirable clinical predictive performance of the model. The risk score was associated with clinicopathological characteristics and proved to be an independent prognostic factor in patients with HCC. The above results were verified in the ICGC validation cohort. Compared with normal tissues and cell lines, the protein and mRNA expression of six mitochondria-related genes was upregulated in HCC tissues and cell lines.

Conclusion

The signature could be an independent factor that supervises the immunotherapy response of HCC patients and possess vital guidance value for clinical diagnosis and treatment.