Integrated analysis of the impact of age on genetic and clinical aspects of hepatocellular carcinoma

A novel diagnostic four-gene signature for hepatocellular carcinoma based on artificial neural network: Development, validation, and drug screening

Background: Hepatocellular carcinoma (HCC) is one of the most common cancers with high mortality in the world. HCC screening and diagnostic models are becoming effective strategies to reduce mortality and improve the overall survival (OS) of patients. Here, we expected to establish an effective novel diagnostic model based on new genes and explore potential drugs for HCC therapy.

Methods: The gene expression data of HCC and normal samples (GSE14811, GSE60502, GSE84402, GSE101685, GSE102079, GSE113996, and GSE45436) were downloaded from the Gene Expression Omnibus (GEO) dataset. Bioinformatics analysis was performed to distinguish two differentially expressed genes (DEGs), diagnostic candidate genes, and functional enrichment pathways. QRT-PCR was used to validate the expression of diagnostic candidate genes. A diagnostic model based on candidate genes was established by an artificial neural network (ANN). Drug sensitivity analysis was used to explore potential drugs for HCC. CCK-8 assay was used to detect the viability of HepG2 under various presentative chemotherapy drugs.

Results: There were 82 DEGs in cancer tissues compared to normal tissue. Protein–protein interaction (PPI), Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses and infiltrating immune cell analysis were administered and analyzed. Diagnostic-related genes of MT1M, SPINK1, AKR1B10, and SLCO1B3 were selected from DEGs and used to construct a diagnostic model. The receiver operating characteristic (ROC) curves were 0.910 and 0.953 in the training and testing cohorts, respectively. Potential drugs, including vemurafenib, LOXO-101, dabrafenib, selumetinib, Arry-162, and NMS-E628, were found as well. Vemurafenib, dabrafenib, and selumetinib were observed to significantly affect HepG2 cell viability.

Conclusion: The diagnostic model based on the four diagnostic-related genes by the ANN could provide predictive significance for diagnosis of HCC patients, which would be worthy of clinical application. Also, potential chemotherapy drugs might be effective for HCC therapy.

Identification of Age-associated Proteins and Functional Alterations in Human Retinal Pigment Epithelium

Retinal pigment epithelium (RPE) has essential functions, such as nourishing and supporting the neural retina, and is of vital importance in the pathogenesis of age-related retinal degeneration. However, the exact molecular changes of RPE during aging remain poorly understood. Here, we isolated human primary RPE (hRPE) cells from 18 eye donors distributed over a wide age range (10-67 years old). A quantitative proteomic analysis was performed to analyze changes in their intracellular and secreted proteins. Age-group related subtypes and age-associated proteins were revealed and potential age-associated mechanisms were validated in ARPE-19 and hRPE cells. The results of proteomic data analysis and verifications suggest that RNF123- and RNF149-related protein ubiquitination plays an important role in protecting hRPE cells from oxidative damage during aging. In older hRPE cells, apoptotic signaling-related pathways were up-regulated, and endoplasmic reticulum organization was down-regulated both in the intracellular and secreted proteomes. Our work paints a detailed molecular picture of hRPE cells during the aging process and provides new insights into the molecular characteristics of RPE during aging and under other related clinical retinal conditions.

Identification of Aging-Related Genes Associated With Clinical and Prognostic Features of Hepatocellular Carcinoma

Background: Aging is a well-studied concept, but no studies have comprehensively analyzed the association between aging-related genes (AGs) and hepatocellular carcinoma (HCC) prognosis. Methods: Gene candidates were selected from differentially expressed genes and prognostic genes in The Cancer Genome Atlas (TCGA) database. A gene risk score for overall survival prediction was established using the least absolute shrinkage and selection operator (LASSO) regression analysis, and this was validated using data from the International Cancer Genome Consortium (ICGC) database. Functional analysis was conducted using gene ontology enrichment, Kyoto Encyclopedia of Genes and Genomes analysis, gene set enrichment analysis, and immune microenvironment and tumor stemness analyses. Results: Initially, 72 AGs from the TCGA database were screened as differentially expressed between normal and tumor tissues and as genes associated with HCC prognosis. Then, seven AGs (POLA1, CDK1, SOCS2, HDAC1, MAPT, RAE1, and EEF1E1) were identified using the LASSO regression analysis. The seven AGs were used to develop a risk score in the training set, and the risk was validated to have a significant prognostic value in the ICGC set (p < 0.05). Patients with high risk scores had lower tumor differentiation, higher stage, and worse prognosis (all p < 0.05). Multivariate Cox regression analyses also confirmed that the risk score was an independent prognostic factor for HCC in both the TCGA and ICGC sets (all p < 0.05). Further analysis showed that a high risk score was correlated with the downregulation of metabolism and tumor immunity. Conclusion: The risk score predicts HCC prognosis and could thus be used as a biomarker not only for predicting HCC prognosis but also for deciding on treatment.

New landscapes and horizons in hepatocellular carcinoma therapy

Hepatocellular carcinoma (HCC), is the sixth most frequent form of cancer and leads to the fourth highest number of deaths each year. HCC results from a combination of environmental factors and aging as there are driver mutations at oncogenes which occur during aging. Most of HCCs are diagnosed at advanced stage preventing curative therapies. Treatment in advanced stage is a challenging and pressing problem, and novel and well-tolerated therapies are urgently needed. We will discuss further advances beyond sorafenib that target additional signaling pathways and immune checkpoint proteins. The scenario of possible systemic therapies for patients with advanced HCC has changed dramatically in recent years. Personalized genomics and various other omics approaches may identify actionable biochemical targets, which are activated in individual patients, which may enhance therapeutic outcomes. Further studies are needed to identify predictive biomarkers and aberrantly activated signaling pathways capable of guiding the clinician in choosing the most appropriate therapy for the individual patient.

Diagnostic and Prognostic Potential of AKR1B10 in Human Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related death worldwide. Although diagnostic measures and surgical interventions have improved in recent years, the five-year survival rate for patients with advanced HCC remains bleak-a reality that is largely attributable to an absence of early stage symptoms, lack of adequate diagnostic and prognostic biomarkers, and the common occurrence of acquired resistance to chemotherapeutic agents during HCC treatment. A limited understanding of the molecular mechanisms underlying HCC pathogenesis also presents a challenge for the development of specific and efficacious pharmacological strategies to treat, halt, or prevent progression to advanced stages. Over the past decade, aldo-keto reductase family 1 member 10 (AKR1B10) has emerged as a potential biomarker for the diagnosis and prognosis of HCC, and experimental studies have demonstrated roles for this enzyme in biological pathways underlying the development and progression of HCC and acquired resistance to chemotherapeutic agents used in the treatment of HCC. Here we provide an overview of studies supporting the diagnostic and prognostic utility of AKR1B10, summarize the experimental evidence linking AKR1B10 with HCC and the induction of chemoresistance, and discuss the clinical value of AKR1B10 as a potential target for HCC-directed drug development. We conclude that AKR1B10-based therapies in the clinical management of specific HCC subtypes warrant further investigation.