Identification and Analysis of Dysfunctional Genes and Pathways in CD8+ T Cells of Non-Small Cell Lung Cancer Based on RNA Sequencing

Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide

The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available.

LncRNA RP11‑805J14.5 functions as a ceRNA to regulate CCND2 by sponging miR‑34b‑3p and miR‑139‑5p in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common lung cancer with high incidence. The prognosis of LUAD is poor due to its aggressive behavior. Long non‑coding RNAs (lncRNAs) have been reported as a key modulator on LUAD progression. Therefore, the present study aimed to clarify the molecular mechanism of lncRNAs in LUAD development. The expression of lncRNA RP11‑805J14.5 (RP11‑805J14.5) in LUAD tissues and cells was quantified based on the data in The Cancer Genome Atlas (TCGA). Cell viability was determined using Cell Counting Kit‑8 method. Apoptotic cells were sorted and determined by flow cytometry. Cell migration and invasion abilities were detected by the Transwell assay. Luciferase reporter experiment and RNA pull‑down assay were utilized to determine the interactions between RP11‑805J14.5, microRNA (miR)‑34b‑3p, miR‑139‑5p, and cyclin D2 (CCND2). A xenograft tumor was established to determine tumor growth in vivo. RP11‑805J14.5 was highly expressed in LUAD and associated with poor survival of LUAD patients. Knockdown of RP11‑805J14.5 suppressed LUAD cell growth, invasion, migration and tumor growth, indicating that RP11‑805J14.5 is an important regulator of LUAD. Our study demonstrated that the regulation of RP11‑805J14.5 on LUAD was mediated by CCND2 whose expression was regulated by sponging miR‑34b‑3p and miR‑139‑5p. The expression of RP11‑805J14.5 was increased in LUAD, and the knockdown of RP11‑805J14.5 expression suppressed LUAD cell growth, invasion and migration by downregulating CCND2 by sponging miR‑34b‑3p and miR‑139‑5p, indicating that RP11‑805J14.5 could be a prospective target for LUAD therapy.

Genetic variability assessment of 127 Triticum turgidum L. accessions for mycorrhizal susceptibility-related traits detection

Positive effects of arbuscular mycorrhizal fungi (AMF)-wheat plant symbiosis have been well discussed by research, while the actual role of the single wheat genotype in establishing this type of association is still poorly investigated. In this work, the genetic diversity of Triticum turgidum wheats was exploited to detect roots susceptibility to AMF and to identify genetic markers in linkage with chromosome regions involved in this symbiosis. A tetraploid wheat collection of 127 accessions was genotyped using 35K single-nucleotide polymorphism (SNP) array and inoculated with the AMF species Funneliformis mosseae (F. mosseae) and Rhizoglomus irregulare (R. irregulare), and a genome-wide association study (GWAS) was conducted. Six clusters of genetically related accessions were identified, showing a different mycorrhizal colonization among them. GWAS revealed four significant quantitative trait nucleotides (QTNs) involved in mycorrhizal symbiosis, located on chromosomes 1A, 2A, 2B and 6A. The results of this work enrich future breeding activities aimed at developing new grains on the basis of genetic diversity on low or high susceptibility to mycorrhization, and, possibly, maximizing the symbiotic effects.

Single-Cell TCR and Transcriptome Analysis: An Indispensable Tool for Studying T-Cell Biology and Cancer Immunotherapy

T cells have been known to be the driving force for immune response and cancer immunotherapy. Recent advances on single-cell sequencing techniques have empowered scientists to discover new biology at the single-cell level. Here, we review the single-cell techniques used for T-cell studies, including T-cell receptor (TCR) and transcriptome analysis. In addition, we summarize the approaches used for the identification of T-cell neoantigens, an important aspect for T-cell mediated cancer immunotherapy. More importantly, we discuss the applications of single-cell techniques for T-cell studies, including T-cell development and differentiation, as well as the role of T cells in autoimmunity, infectious disease and cancer immunotherapy. Taken together, this powerful tool not only can validate previous observation by conventional approaches, but also can pave the way for new discovery, such as previous unidentified T-cell subpopulations that potentially responsible for clinical outcomes in patients with autoimmunity or cancer.

Multiple Feature Selection Strategies Identified Novel Cardiac Gene Expression Signature for Heart Failure

Heart failure (HF) is a serious condition in which the support of blood pumped by the heart is insufficient to meet the demands of body at a normal cardiac filling pressure. Approximately 26 million patients worldwide are suffering from heart failure and about 17–45% of patients with heart failure die within 1-year, and the majority die within 5-years admitted to a hospital. The molecular mechanisms underlying the progression of heart failure have been poorly studied. We compared the gene expression profiles between patients with heart failure (n = 177) and without heart failure (n = 136) using multiple feature selection strategies and identified 38 HF signature genes. The support vector machine (SVM) classifier based on these 38 genes evaluated with leave-one-out cross validation (LOOCV) achieved great performance with sensitivity of 0.983 and specificity of 0.963. The network analysis suggested that the hub gene SMOC2 may play important roles in HF. Other genes, such as FCN3, HMGN2, and SERPINA3, also showed great promises. Our results can facilitate the early detection of heart failure and can reveal its molecular mechanisms.

Statistical Approach for Biologically Relevant Gene Selection from High-Throughput Gene Expression Data

Selection of biologically relevant genes from high-dimensional expression data is a key research problem in gene expression genomics. Most of the available gene selection methods are either based on relevancy or redundancy measure, which are usually adjudged through post selection classification accuracy. Through these methods the ranking of genes was conducted on a single high-dimensional expression data, which led to the selection of spuriously associated and redundant genes. Hence, we developed a statistical approach through combining a support vector machine with Maximum Relevance and Minimum Redundancy under a sound statistical setup for the selection of biologically relevant genes. Here, the genes were selected through statistical significance values and computed using a nonparametric test statistic under a bootstrap-based subject sampling model. Further, a systematic and rigorous evaluation of the proposed approach with nine existing competitive methods was carried on six different real crop gene expression datasets. This performance analysis was carried out under three comparison settings, i.e., subject classification, biological relevant criteria based on quantitative trait loci and gene ontology. Our analytical results showed that the proposed approach selects genes which are more biologically relevant as compared to the existing methods. Moreover, the proposed approach was also found to be better with respect to the competitive existing methods. The proposed statistical approach provides a framework for combining filter and wrapper methods of gene selection.