Identification of a novel gene expression signature associated with overall survival in patients with lung adenocarcinoma: A comprehensive analysis based on TCGA and GEO databases.

OBJECTIVES: Lung adenocarcinoma (LUAD) is the most common subtype of non-small cell lung cancer. Understanding the molecular mechanisms underlying tumor progression is of clinical significance. This study aimed to identify novel molecular markers associated with LUAD prognosis. MATERIALS AND METHODS: RNA sequencing data from the Cancer Genome Atlas (TCGA) database of LUAD tumors and paired normal tissues, and microarray data from the Gene Expression Omnibus (GEO) database were obtained. In the TCGA dataset, differentially expressed (DE) genes were identified by comparing gene expression between early-stage tumors and normal tissue, as well as between advanced-stage and early-stage tumors. A risk score was developed using a weighted linear combination of individual dysregulated protein-coding genes that was associated with overall survival (OS). The prognostic value of the risk score was evaluated using Kaplan-Meier and multivariate Cox analysis. The gene signature was further validated using independent datasets from GEO. RESULTS: Among the 68 identified DE genes, 19 were individually associated with OS in univariate analyses. A risk score was constructed for each patient based on the coefficients in multivariate Cox model and normalized expression levels of these 19 genes. LUAD patients with a low risk score had a significantly better survival than those with a high risk score (log-rank P < 0.0001). After adjusting for age, sex, clinical stage, smoking history, and treatments, the patients with a low risk score had a 81 % decreased risk for death, compared to those with a high risk score (hazard ratio 0.19, 95 % confidence interval 0.097-0.36). The significant association of the risk score with OS in LUAD patients was further validated in three independent GEO datasets. CONCLUSION: A novel 19-gene prognostic signature based on gene expression was identified in LUAD patients. The findings further improve the understanding of LUAD prognostication and have the potential to facilitate risk-stratified disease management.

Machine learning-based genome-wide interrogation of somatic copy number aberrations in circulating tumor DNA for early detection of hepatocellular carcinoma.

BACKGROUND: DNAs released from tumor cells into blood (circulating tumor DNAs, ctDNAs) carry tumor-specific genomic aberrations, providing a non-invasive means for cancer detection. In this study, we aimed to leverage somatic copy number aberration (SCNA) in ctDNA to develop assays to detect early-stage HCCs. METHODS: We conducted low-depth whole-genome sequencing (WGS) to profile SCNAs in 384 plasma samples of hepatitis B virus (HBV)-related HCC and cancer-free HBV patients, using one discovery and two validation cohorts. To fully capture the robust signals of WGS data from the complete genome, we developed a machine learning-based statistical model that is focused on detection accuracy in early-stage HCC. FINDINGS: We built the model using a discovery cohort of 209 patients, achieving an overall area under curve (AUC) of 0.893, with 0.874 for early-stage (Barcelona clinical liver cancer [BCLC] stage 0-A) and 0.933 for advanced-stage (BCLC stage B-D). The performance of the model was then assessed in two validation cohorts (76 and 99 patients) that only consisted of patients with stage 0-A HCC. Our model exhibited a robust predictive performance, with an AUC of 0.920 and 0.812 for the two validation cohorts. Further analyses showed the impact of tumor sample heterogeneity in model training on detecting early-stage tumors, and a refined model addressing the heterogeneity in the discovery cohort significantly increased model performance in validation. INTERPRETATION: We developed an SCNA-based, machine learning-driven model in the non-invasive detection of early-stage HCC in HBV patients and demonstrated its performance through strict independent validations.

Integrated approach for the identification of human hepatocyte nuclear factor 4alpha target genes using protein binding microarrays.

UNLABELLED: Hepatocyte nuclear factor 4 alpha (HNF4alpha), a member of the nuclear receptor superfamily, is essential for liver function and is linked to several diseases including diabetes, hemophilia, atherosclerosis, and hepatitis. Although many DNA response elements and target genes have been identified for HNF4alpha, the complete repertoire of binding sites and target genes in the human genome is unknown. Here, we adapt protein binding microarrays (PBMs) to examine the DNA-binding characteristics of two HNF4alpha species (rat and human) and isoforms (HNF4alpha2 and HNF4alpha8) in a high-throughput fashion. We identified approximately 1400 new binding sequences and used this dataset to successfully train a Support Vector Machine (SVM) model that predicts an additional approximately 10,000 unique HNF4alpha-binding sequences; we also identify new rules for HNF4alpha DNA binding. We performed expression profiling of an HNF4alpha RNA interference knockdown in HepG2 cells and compared the results to a search of the promoters of all human genes with the PBM and SVM models, as well as published genome-wide location analysis. Using this integrated approach, we identified approximately 240 new direct HNF4alpha human target genes, including new functional categories of genes not typically associated with HNF4alpha, such as cell cycle, immune function, apoptosis, stress response, and other cancer-related genes. CONCLUSION: We report the first use of PBMs with a full-length liver-enriched transcription factor and greatly expand the repertoire of HNF4alpha-binding sequences and target genes, thereby identifying new functions for HNF4alpha. We also establish a web-based tool, HNF4 Motif Finder, that can be used to identify potential HNF4alpha-binding sites in any sequence.

Hepatocyte nuclear factor 4alpha orchestrates expression of cell adhesion proteins during the epithelial transformation of the developing liver.

Epithelial formation is a central facet of organogenesis that relies on intercellular junction assembly to create functionally distinct apical and basal cell surfaces. How this process is regulated during embryonic development remains obscure. Previous studies using conditional knockout mice have shown that loss of hepatocyte nuclear factor 4alpha (HNF4alpha) blocks the epithelial transformation of the fetal liver, suggesting that HNF4alpha is a central regulator of epithelial morphogenesis. Although HNF4alpha-null hepatocytes do not express E-cadherin (also called CDH1), we show here that E-cadherin is dispensable for liver development, implying that HNF4alpha regulates additional aspects of epithelial formation. Microarray and molecular analyses reveal that HNF4alpha regulates the developmental expression of a myriad of proteins required for cell junction assembly and adhesion. Our findings define a fundamental mechanism through which generation of tissue epithelia during development is coordinated with the onset of organ function.