Background: Human papillomavirus (HPV) is the main risk factor for the development of squamous cell cervical cancer, and E6 oncoprotein and E7 oncoprotein are important components of the viral genome and its oncogenic potential. It is known that different viral variants of HPV16 have different pathology and impact on the development of neoplasia, although few studies have been performed on South American variants. Objective: Therefore, the present study aimed to analyze in silico the genomic diversity of HPV16 in 20 complete genome variants of South America in the National Center for Biotechnology Information (NCBI) database. Methods: We performed a descriptive study to characterize the polymorphic regions of the E6 and E7 genes in HPV16 variants, using software for genomic data and single nucleotide polymorphism (SNP) analysis and others for phylogenetic analysis. Results: The variants analyzed included six SNPs linked to cancer (A131G, G145T, C335T, T350G, C712A, and T732C) and significant variation (798 nucleotide substitutions). Despite this, the variants showed low genetic diversity. Eighteen variants of unclear significance (VUS) were identified, 10 of which were in the coding E6 regions and 8 in the coding E7 regions. The prevalence of lineage D variants is of concern due to their pathology in cervical cancer and requires more research and epidemiological vigilance regarding their prevalence in the population. Conclusion: The data obtained in this study may contribute to future research on South American variants of HPV16, their pathogenicity, and the development of treatments.

Background: Understanding organic functions at a molecular level is important for scientists to unveil the disease mechanism and to develop diagnostic or therapeutic methods. Aim: The present study tried to find genes selectively expressed in 11 rat organs, including the adrenal gland, brain, colon, duodenum, heart, ileum, kidney, liver, lung, spleen, and stomach. Materials and Methods: Three normal male Sprague-Dawley (SD) rats were anesthetized, their organs mentioned above were harvested, and RNA in the fresh organs was extracted. Purified RNA was reversely transcribed and sequenced using the Solexa high-throughput sequencing technique. The abundance of a gene was measured by the expected value of fragments per kilobase of transcript sequence per million base pairs sequenced (FPKM). Genes in organs with the highest expression level were sought out and compared with their median value in organs. If a gene in the highest expressed organ was significantly different (p < 0.05) from that in the medianly expressed organ, accompanied by q value < 0.05, and accounted for more than 70% of the total abundance, the gene was assumed as the selective gene in the organ. Results & Discussion: The Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Ontology (GO) pathways were enriched by the highest expressed genes. Based on the criterion, 1,406 selective genes were screened out, 1,283 of which were described in the gene bank and 123 of which were waiting to be described. KEGG and GO pathways in the organs were partly confirmed by the known understandings and a good portion of the pathways needed further investigation. Conclusion: The novel selective genes and organic functional pathways are useful for scientists to unveil the mechanisms of the organs at the molecular level, and the selective genes’ products are candidate disease markers for organs.

Background: Although the application of mesenchymal stem cells (MSCs) in engineered medicine, such as tissue regeneration, is well known, new evidence is emerging that shows that MSCs can also promote cancer progression, metastasis, and drug resistance. However, no large-scale cohort analysis of MSCs has been conducted to reveal their impact on the prognosis of cancer patients. Objective: We propose the MSC score as a novel surrogate for poor prognosis in pan-cancer. Methods: We used single sample gene set enrichment analysis to quantify MSC-related genes into a signature score and identify the signature score as a potential independent prognostic marker for cancer using multivariate Cox regression analysis. TIDE algorithm and neural network were utilized to assess the predictive accuracy of MSC-related genes for immunotherapy. Results: MSC-related gene expression significantly differed between normal and tumor samples across the 33 cancer types. Cox regression analysis suggested the MSC score as an independent prognostic marker for kidney renal papillary cell carcinoma, mesothelioma, glioma, and stomach adenocarcinoma. The abundance of fibroblasts was also more representative of the MSC score than the stromal score. Our findings supported the combined use of the TIDE algorithm and neural network to predict the accuracy of MSC-related genes for immunotherapy. Conclusion: We comprehensively characterized the transcriptome, genome, and epigenetics of MSCs in pan-cancer and revealed the crosstalk of MSCs in the tumor microenvironment, especially with cancer-related fibroblasts. It is suggested that this may be one of the key sources of resistance to cancer immunotherapy.

Abstract: The fastest way to significantly change the composition of a population is through admixture, an evolutionary mechanism. In animal breeding history, genetic admixture has provided both short-term and long-term advantages by utilizing the phenomenon of complementarity and heterosis in several traits and genetic diversity, respectively. The traditional method of admixture analysis by pedigree records has now been replaced greatly by genome-wide marker data that enables more precise estimations. Among these markers, SNPs have been the popular choice since they are cost-effective, not so laborious, and automation of genotyping is easy. Certain markers can suggest the possibility of a population's origin from a sample of DNA where the source individual is unknown or unwilling to disclose their lineage, which are called Ancestry-Informative Markers (AIMs). Revealing admixture level at the locus-specific level is termed as local ancestry and can be exploited to identify signs of recent selective response and can account for genetic drift. Considering the importance of genetic admixture and local ancestry, in this mini-review, both concepts are illustrated, encompassing basics, their estimation/identification methods, tools/- software used and their applications.

Abstract: Biobanks are necessary resources for the storage and management of human biological materials, such as biofluids, tissues, cells, or nucleotides. They play a significant role in the development of new treatments and the advancement of basic and translational research, especially in the field of biomarkers discovery and validation. The regulatory landscape for biobanks, which is necessary to safeguard both privacy and scientific discoveries, exhibits significant heterogeneity across different countries and regions. This article outlines the standards that modern biobanks should fulfill in the European Union (EU), including general, structural, resource, process, and quality requirements. Special attention is given to the importance of transparency and donor consent following the General Data Protection Regulation (GDPR) and the ISO 20387:2018, the international standard specifies general requirements for biobanks. A dedicated section covers the preparation of donor information materials, emphasizing consent for research involvement and personal data processing. The delicate balance between donors' privacy rights and scientific research promotion is also discussed, with a focus on the patenting and economic use of biological material- derived inventions and data. Considering these factors, it would be warranted to refine legal frameworks and foster interdisciplinary collaboration to ethically and responsibly expand biobanking.

Background: The damage in the liver and hepatocytes is where the primary liver cancer begins, and this is referred to as Hepatocellular Carcinoma (HCC). One of the best methods for detecting changes in gene expression of hepatocellular carcinoma is through bioinformatics approaches. Objective: This study aimed to identify potential drug target(s) hubs mediating HCC progression using computational approaches through gene expression and protein-protein interaction datasets. Methodology: Four datasets related to HCC were acquired from the GEO database, and Differentially Expressed Genes (DEGs) were identified. Using Evenn, the common genes were chosen. Using the Fun Rich tool, functional associations among the genes were identified. Further, protein-protein interaction networks were predicted using STRING, and hub genes were identified using Cytoscape. The selected hub genes were subjected to GEPIA and Shiny GO analysis for survival analysis and functional enrichment studies for the identified hub genes. The up-regulating genes were further studied for immunohistopathological studies using HPA to identify gene/protein expression in normal vs HCC conditions. Drug Bank and Drug Gene Interaction Database were employed to find the reported drug status and targets. Finally, STITCH was performed to identify the functional association between the drugs and the identified hub genes. Results: The GEO2R analysis for the considered datasets identified 735 upregulating and 284 downregulating DEGs. Functional gene associations were identified through the Fun Rich tool. Further, PPIN network analysis was performed using STRING. A comparative study was carried out between the experimental evidence and the other seven data evidence in STRING, revealing that most proteins in the network were involved in protein-protein interactions. Further, through Cytoscape plugins, the ranking of the genes was analyzed, and densely connected regions were identified, resulting in the selection of the top 20 hub genes involved in HCC pathogenesis. The identified hub genes were: KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Further, GEPIA and Shiny GO analyses provided insights into survival ratios and functional enrichment studied for the hub genes. The HPA database studies further found that upregulating genes were involved in changes in protein expression in Normal vs HCC tissues. These findings indicated that hub genes were certainly involved in the progression of HCC. STITCH database studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, could be used as leads to identify novel drugs, and identified hub genes could also be considered as potential and promising drug targets as they are involved in the gene-chemical interaction networks. Conclusion: The present study involved various integrated bioinformatics approaches, analyzing gene expression and protein-protein interaction datasets, resulting in the identification of 20 topranked hubs involved in the progression of HCC. They are KIF2C, CDK1, TPX2, CEP55, MELK, TTK, BUB1, NCAPG, ASPM, KIF11, CCNA2, HMMR, BUB1B, TOP2A, CENPF, KIF20A, NUSAP1, DLGAP5, PBK, and CCNB2. Gene-chemical interaction network studies uncovered that existing drug molecules, including sorafenib, regorafenib, cabozantinib, and lenvatinib, can be used as leads to identify novel drugs, and the identified hub genes can be promising drug targets. The current study underscores the significance of targeting these hub genes and utilizing existing molecules to generate new molecules to combat liver cancer effectively and can be further explored in terms of drug discovery research to develop treatments for HCC.

Popular gene set enrichment analysis approaches assumed that genes in the gene set contributed to the statistics equally. However, the genes in the transcription factors (TFs) derived gene sets, or gene sets constructed by TF targets identified by the ChIP-Seq experiment, have a rank attribute, as each of these genes have been assigned with a p-value which indicates the true or false possibilities of the ownerships of the genes belong to the gene sets. Ignoring the rank information during the enrichment analysis will lead to improper statistical inference. We address this issue by developing of new method to test the significance of ranked gene sets in genome-wide transcriptome profiling data. A method was proposed by first creating ranked gene sets and gene lists and then applying weighted Kendall's tau rank correlation statistics to the test. After introducing top-down weights to the genes in the gene set, a new software called "Flaver" was developed. Theoretical properties of the proposed method were established, and its differences over the GSEA approach were demonstrated when analyzing the transcriptome profiling data across 55 human tissues and 176 human cell-lines. The results indicated that the TFs identified by our method have higher tendency to be differentially expressed across the tissues analyzed than its competitors. It significantly outperforms the well-known gene set enrichment analyzing tools, GOStats (9%) and GSEA (17%), in analyzing well-documented human RNA transcriptome datasets. The method is outstanding in detecting gene sets of which the gene ranks were correlated with the expression levels of the genes in the transcriptome data.