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This paper presents a consensus-based approach that incorporates three microarray and three RNA-Seq methods for unbiased and integrative identification of differentially expressed genes (DEGs) as potential biomarkers for critical disease(s). The proposed method performs satisfactorily on two microarray datasets (GSE20347 and GSE23400) and one RNA-Seq dataset (GSE130078) for esophageal squamous cell carcinoma (ESCC). Based on the input dataset, our framework employs specific DE methods to detect DEGs independently. A consensus based function that first considers DEGs common to all three methods for further downstream analysis has been introduced. The consensus function employs other parameters to overcome information loss. Differential co-expression (DCE) and preservation analysis of DEGs facilitates the study of behavioral changes in interactions among DEGs under normal and diseased circumstances. Considering hub genes in biologically relevant modules and most GO and pathway enriched DEGs as candidates for potential biomarkers of ESCC, we perform further validation through biological analysis as well as literature evidence. We have identified 25 DEGs that have strong biological relevance to their respective datasets and have previous literature establishing them as potential biomarkers for ESCC. We have further identified 8 additional DEGs as probable potential biomarkers for ESCC, but recommend further in-depth analysis.
This paper presents a consensus-based approach that incorporates three microarray and three RNA-Seq methods for unbiased and integrative identification of differentially expressed genes (DEGs) as potential biomarkers for critical disease(s). The proposed method performs satisfactorily on two microarray datasets (GSE20347 and GSE23400) and one RNA-Seq dataset (GSE130078) for esophageal squamous cell carcinoma (ESCC). Based on the input dataset, our framework employs specific DE methods to detect DEGs independently. A consensus based function that first considers DEGs common to all three methods for further downstream analysis has been introduced. The consensus function employs other parameters to overcome information loss. Differential co-expression (DCE) and preservation analysis of DEGs facilitates the study of behavioral changes in interactions among DEGs under normal and diseased circumstances. Considering hub genes in biologically relevant modules and most GO and pathway enriched DEGs as candidates for potential biomarkers of ESCC, we perform further validation through biological analysis as well as literature evidence. We have identified 25 DEGs that have strong biological relevance to their respective datasets and have previous literature establishing them as potential biomarkers for ESCC. We have further identified 8 additional DEGs as probable potential biomarkers for ESCC, but recommend further in-depth analysis.
BackgroundHuman leukocyte antigen-G (HLA-G) is a cancer-associated immune checkpoint protein implicated in tumor-driven immune escape mechanisms. This study was undertaken to determine genetic variations at the 3’-UTR of the HLA-G gene that may alter its expression, identify risk alleles and genotypes for their association with hepatocellular carcinoma (HCC), and treatment responses in the Indian population.ObjectivesCase-control genetic association study of HLA-G gene UTR polymorphisms with HCC and response to locoregional therapy (LRT).MethodsHCC cases (n = 100) and healthy controls (n = 110) were recruited for the genetic association study, of which 88 patients received LRT. Single nucleotide polymorphisms (SNPs) at the HLA-G 3’-UTR gene were genotyped by sequencing and PCR-RFLP. The genetic association of 14 SNPs with HCC and LRT responses was determined using population genetic approaches.ResultsThree of the 14 SNPs (rs1707, rs1710, and rs1063320) were found to be genetically associated with HCC risk and treatment responses. These three UTR SNPs are important for miRNA binding. We did not observe significant association of the most studied SNP, rs371194629 (INDEL, +2960), with HCC or treatment response. Serum sHLA-G levels were found to be significantly (p = 0.027) higher in HCC patients as compared to healthy controls. Highly prevalent UTR haplotypes in Indian HCC patients were UTR-4, -1, and -7 whereas in healthy controls it was UTR-3, and 15 as determined by a linkage disequilibrium (LD) plot using 8 SNPs.ConclusionHLA-G SNPs are genetically associated with HCC and treatment response. Haplotypes associated with high levels of HLA-G expression are more prevalent in HCC than in healthy controls.Core tipPopulation genetic approaches were used to study HLA-G gene polymorphisms in the Indian population for its genetic association with HCC risk, treatment response and altered gene expression. Out of the 14 SNPs studied for HLA-G UTR, three were linked to HCC and response to locoregional therapy. Linkage disequilibrium and UTR haplotyping analysis show that the UTR-4 haplotype linked to high HLA-G levels, is more common in HCC patients, while the UTR-3 haplotype, linked to low HLA-G levels, is more common in healthy controls. This study is the first to look at the UTR types based on HLA-G gene polymorphisms of Indian HCC patients and their response to therapy.
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