Identification of Novel Characteristics in TP53-Mutant Hepatocellular Carcinoma Using Bioinformatics

Integration of single-cell and bulk RNA-sequencing to analyze the heterogeneity of hepatocellular carcinoma and establish a prognostic model

BACKGROUND

The highly heterogeneous nature of hepatocellular carcinoma (HCC) results in different responses and prognoses to the same treatment in patients with similar clinical stages.

AIMS

Thus, it is imperative to investigate the association between HCC tumor heterogeneity and treatment response and prognosis.

METHODS AND RESULTS

At first, we downloaded scRNA-seq, bulk RNA-seq, and clinical data from TCGA and GEO databases. We conducted quality control, normalization using SCTransform, dimensionality reduction using PCA, batch effect removal using Harmony, dimensionality reduction using UMAP, and cell annotation-based marker genes on the scRNA-seq data. We recognized tumor cells, identified tumor-related genes (TRGs), and performed cell communication analysis. Next, we developed a prognostic model using univariable Cox, LASSO, and multivariate Cox analyses. The signature was evaluated using survival analysis, ROC curves, C-index, and nomogram. Last, we studied the predictability of the signature in terms of prognosis and immunotherapeutic response for HCC, assessed a variety of drugs for clinical treatment, and used the qRT-PCR analysis to validate the mRNA expression levels of prognostic TRGs.

CONCLUSION

To conclude, this study expounded upon the influence of tumor cell heterogeneity on the prediction of treatment outcomes and prognosis in HCC. This, in turn, enhances the predictive ability of the TNM staging system and furnishes novel perspectives on the prognostic assessment and therapy 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.

Bioinformatic analysis the expression and clinical significance of CDRT15 in cholangiocarcinoma using TCGA database

Cholangiocarcinoma (CCA) is a common and lethal malignant tumor originating from bile duct epithelial cells. Various tumor biomarkers have been used for its clinical screening, such as carbohydrate antigen 19-9 and carcinoembryonic antigen. This study aimed to demonstrate the value of associated genes-CMT1A duplicated region transcript 15 (CDRT15) for prognosis of CCA by integrated bioinformatics analysis. We obtained CDRT15 expression data and clinical information on patients with CCA from The Cancer Genome Atlas database. Then, we processed the data by differentially expressed gene analysis, gene set enrichment analysis, statistical analysis, etc. Gene Ontology enrichment analysis was aimed to explore the function of gene-related proteins. Single-sample gene set enrichment analysis was used to analyze the correlation between CDRT15 and immune cells. Finally, we constructed the nomogram to predict the prognosis of patients with CCA. The analysis of data in The Cancer Genome Atlas database revealed that CDRT15 was overexpressed in CCA tissues. We performed the interrelation analysis of immune infiltration, showing that CDRT15 are mainly associated with the immune/inflammatory response. ROC curve showed that CDRT15 can be a diagnostic marker of CCA. Subsequently, the prognostic analysis showed that the high expression of CDRT15 was correlated with the poor OS, and patients with high CDRT15 expression may have a poor prognosis. CDRT15 is more highly expressed in CCA, thus we identified that CDRT15 could be an efficient biomarker for patients. CDRT15 expression was negatively correlated with prognosis of CCA. CDRT15 may be involved in the immune infiltration process of CCA.

LOCC: a novel visualization and scoring of cutoffs for continuous variables

Objective

There is a need for new methods to select and analyze cutoffs employed to define genes that are most prognostic significant and impactful. We designed LOCC (Luo's Optimization Categorization Curve), a novel tool to visualize and score continuous variables for a dichotomous outcome.

Methods

To demonstrate LOCC with real world data, we analyzed TCGA hepatocellular carcinoma gene expression and patient data using LOCC. We compared LOCC visualization to receiver operating characteristic (ROC) curve for prognostic modeling to showcase its utility in understanding predictors in various TCGA datasets.

Results

Analysis of E2F1 expression in hepatocellular carcinoma using LOCC demonstrated appropriate cutoff selection and validation. In addition, we compared LOCC visualization and scoring to ROC curves and c-statistics, demonstrating that LOCC better described predictors. Analysis of a previously published gene signature showed large differences in LOCC scoring, and removing the lowest scoring genes did not affect prognostic modeling of the gene signature demonstrating LOCC scoring could distinguish which predictors were most critical.

Conclusion

Overall, LOCC is a novel visualization tool for understanding and selecting cutoffs, particularly for gene expression analysis in cancer. The LOCC score can be used to rank genes for prognostic potential and is more suitable than ROC curves for prognostic modeling.

Prognosis and personalized medicine prediction by integrated whole exome and transcriptome sequencing of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a clinically and genetically heterogeneous disease. To better describe the clinical value of the main driver gene mutations of HCC, we analyzed the whole exome sequencing data of 125 patients, and combined with the mutation data in the public database, 14 main mutant genes were identified. And we explored the correlation between the main mutation genes and clinical features. Consistent with the results of previous data, we found that TP53 and LRP1B mutations were related to the prognosis of our patients by WES data analysis. And we further explored the associated characteristics of TP53 and LRP1B mutations. However, it is of great clinical significance to tailor a unique prediction method and treatment plan for HCC patients according to the mutation of TP53. For TP53 wild-type HCC patients, we proposed a prognostic risk model based on 11 genes for better predictive value. According to the median risk score of the model, HCC patients with wild-type TP53 were divided into high-risk and low-risk groups. We found significant transcriptome changes in the enrichment of metabolic-related pathways and immunological characteristics between the two groups, suggesting the predictability of HCC immunotherapy by using this model. Through the CMap database, we found that AM580 had potential therapeutic significance for high-risk TP53 wild-type HCC patients.

Immune-related gene signature to predict TACE refractoriness in patients with hepatocellular carcinoma based on artificial neural network

Background: Transarterial chemoembolization (TACE) is the standard treatment option for intermediate-stage hepatocellular carcinoma (HCC), while response varies among patients. This study aimed to identify novel immune-related genes (IRGs) and establish a prediction model for TACE refractoriness in HCC patients based on machine learning methods. Methods: Gene expression data were downloaded from GSE104580 dataset of Gene Expression Omnibus (GEO) database, differential analysis was first performed to screen differentially expressed genes (DEGs). The least absolute shrinkage and selection operator (LASSO) regression analysis was performed to further select significant DEGs. Weighted gene co-expression network analysis (WGCNA) was utilized to build a gene co-expression network and filter the hub genes. Final signature genes were determined by the intersection of LASSO analysis results, WGCNA results and IRGs list. Based on the above results, the artificial neural network (ANN) model was constructed in the training cohort and verified in the validation cohort. Receiver operating characteristics (ROC) analysis was used to assess the prediction accuracy. Correlation of signature genes with tumor microenvironment scores, immune cells and immune checkpoint molecules were further analyzed. The tumor immune dysfunction and exclusion (TIDE) score was used to evaluate the response to immunotherapy. Results: One hundred and forty-seven samples were included in this study, which was randomly divided into the training cohort (n = 103) and validation cohort (n = 44). In total, 224 genes were identified as DEGs. Further LASSO regression analysis screened out 25 genes from all DEGs. Through the intersection of LASSO results, WGCNA results and IRGs list, S100A9, TREM1, COLEC12, and IFIT1 were integrated to construct the ANN model. The areas under the curves (AUCs) of the model were .887 in training cohort and .765 in validation cohort. The four IRGs also correlated with tumor microenvironment scores, infiltrated immune cells and immune checkpoint genes in various degrees. Patients with TACE-Response, lower expression of COLEC12, S100A9, TREM1 and higher expression of IFIT1 had better response to immunotherapy. Conclusion: This study constructed and validated an IRG signature to predict the refractoriness to TACE in patients with HCC, which may have the potential to provide insights into the TACE refractoriness in HCC and become the immunotherapeutic targets for HCC patients with TACE refractoriness.