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Introduction: Androgenetic alopecia (AGA) is a common progressive scalp hair loss disorder that leads to baldness. This study aimed to identify core genes and pathways involved in premature AGA through an in-silico approach., Methods: Gene expression data (GSE90594) from vertex scalps of men with premature AGA and men without pattern hair loss was downloaded from the Gene Expression Omnibus database. Differentially expressed genes (DEGs) between the bald and haired samples were identified using the limma package in R. Gene ontology and Reactome pathway enrichment analyses were conducted separately for the up-regulated and down-regulated genes. The DEGs were annotated with the AGA risk loci, and motif analysis in the promoters of the DEGs was also carried out. STRING Protein-protein interaction (PPI) and Reactome Functional Interaction (FI) networks were constructed using the DEGs, and the networks were analyzed to identify hub genes that play could play crucial roles in AGA pathogenesis., Results and Discussion: The in-silico study revealed that genes involved in the structural makeup of the skin epidermis, hair follicle development, and hair cycle are down-regulated, while genes associated with the innate and adaptive immune systems, cytokine signaling, and interferon signaling pathways are up-regulated in the balding scalps of AGA. The PPI and FI network analyses identified 25 hub genes namely CTNNB1, EGF, GNAI3, NRAS, BTK, ESR1, HCK, ITGB7, LCK, LCP2, LYN, PDGFRB, PIK3CD, PTPN6, RAC2, SPI1, STAT3, STAT5A, VAV1, PSMB8, HLA-A, HLA-F, HLA-E, IRF4, and ITGAM that play crucial roles in AGA pathogenesis. The study also implicates that Src family tyrosine kinase genes such as LCK, and LYN in the up-regulation of the inflammatory process in the balding scalps of AGA highlighting their potential as therapeutic targets for future investigations., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Premanand and Reena Rajkumari.)

Background. A common research goal in transcriptome projects is to find genes that are differentially expressed in different phenotype classes. Biologists might wish to validate such gene candidates experimentally, or use them for downstream systems biology analysis. Producing a coherent differential gene expression analysis from RNA-seq count data requires an understanding of how numerous sources of variation such as the replicate size, the hypothesized biological effect size, and the specific method for making differential expression calls interact. We believe an explicit demonstration of such interactions in real RNA-seq data sets is of practical interest to biologists.Results. Using two large public RNA-seq data sets—one representing strong, and another mild, biological effect size—we simulated different replicate size scenarios, and tested the performance of several commonly-used methods for calling differentially expressed genes in each of them. We found that, when biological effect size was mild, RNA-seq experiments should focus on experimental validation of differentially expressed gene candidates. Importantly, at least triplicates must be used, and the differentially expressed genes should be called using methods with high positive predictive value (PPV), such as NOISeq or GFOLD. In contrast, when biological effect size was strong, differentially expressed genes mined from unreplicated experiments using NOISeq, ASC and GFOLD had between 30 to 50% mean PPV, an increase of more than 30-fold compared to the cases of mild biological effect size. Among methods with good PPV performance, having triplicates or more substantially improved mean PPV to over 90% for GFOLD, 60% for DESeq2, 50% for NOISeq, and 30% for edgeR. At a replicate size of six, we found DESeq2 and edgeR to be reasonable methods for calling differentially expressed genes at systems level analysis, as their PPV and sensitivity trade-off were superior to the other methods’.Conclusion. When biological effect size is weak, systems level investigation is not possible using RNAseq data, and no meaningful result can be obtained in unreplicated experiments. Nonetheless, NOISeq or GFOLD may yield limited numbers of gene candidates with good validation potential, when triplicates or more are available. When biological effect size is strong, NOISeq and GFOLD are effective tools for detecting differentially expressed genes in unreplicated RNA-seq experiments for qPCR validation. When triplicates or more are available, GFOLD is a sharp tool for identifying high confidence differentially expressed genes for targeted qPCR validation; for downstream systems level analysis, combined results from DESeq2 and edgeR are useful.

The rising price of fishmeal and the need for sustainable use of fishery resources have encouraged efforts by the aquaculture industry to identify alternative protein sources including plants and single cell organisms. This is particularly the case for the tiger pufferfish, Takifugu rubripes , which is fed a high level of fishmeal as the protein source. In this study, we compared four possible replacement diets: in three diets, fishmeal was reduced from 77% to 47% and replaced mainly by plant (PP), bacterial (BAC), or yeast (LY)-derived ingredients; in the fourth, reduced to 20% and replaced mainly by yeast (HY). Growth performance, blood chemistry, transcriptomic responses in the liver, and resistance to the parasite Heterobothrium okamotoi were compared in 360 cultured tiger pufferfish (9.8 months old at the start of the experiment). After a 3-month feeding trial, the HY-fed fish showed increased standard lengths (p = 0.015) and body weights (p = 0.004) compared to control fish fed the standard FM (77% fishmeal) diet. At the end of the feeding trial, FM-fed fish had a mean hematocrit (Ht) of 29.3 ± 5.6%; HY-fed fish had the lowest mean Ht of 25.2 ± 5.1% (p = 0.005). Mean total cholesterol levels in BAC-, LY- and HY-fed fish were lower than in control fish (p = 0.008, 0.004, and 0.024, respectively). In contrast, no significant difference was found for triglyceride, total protein, and glucose levels among the groups. Subsequently, 150 individuals were subjected to artificial infection with H. okamotoi ; no significant differences were found for mean parasite counts and Ht values between fish fed the experimental diets and the control fish. Analysis of gene expression in the liver showed that the gene hmgcra was significantly up-regulated (based on a log2 fold change threshold) in HY-fed fish compared to FM-fed fish. Overall, the analyses indicate that a low fishmeal diet in which 71% of fishmeal is replaced mainly by yeast meal (i.e., containing 40% of yeast meal in total mass) may be a suitable replacement for the commercial FM diet as it promotes growth but does not induce severe deleterious effects. • The standard feed of the tiger pufferfish includes high level of fishmeal contents (70– 80%). • Low fishmeal diet containing 27% of fishmeal and 40% of yeast meal enhances growth in the tiger pufferfish. • Low fishmeal diet including 47% of fishmeal and 23% of bacterial meal leads to high hepatosomatic index. • The hmgcra gene was upregulated in fish fed low fishmeal diets. [ABSTRACT FROM AUTHOR]

Introduction: Long non-coding ribonucleic acids (lncRNAs) are involved in the cellular damage response following exposure to ionizing radiation as applied in radiotherapy. However, the role of lncRNAs in radiation response concerning intrinsic susceptibility to late effects of radiation exposure has not been examined in general or in long-term survivors of childhood cancer with and without potentially radiotherapy-related second primary cancers, in particular., Methods: Primary skin fibroblasts (n=52 each) of long-term childhood cancer survivors with a first primary cancer only (N1), at least one second primary neoplasm (N2+), as well as tumor-free controls (N0) from the KiKme case-control study were matched by sex, age, and additionally by year of diagnosis and entity of the first primary cancer. Fibroblasts were exposed to 0.05 and 2 Gray (Gy) X-rays. Differentially expressed lncRNAs were identified with and without interaction terms for donor group and dose. Weighted co-expression networks of lncRNA and mRNA were constructed using WGCNA . Resulting gene sets (modules) were correlated to the radiation doses and analyzed for biological function., Results: After irradiation with 0.05Gy, few lncRNAs were differentially expressed (N0: AC004801.4 ; N1: PCCA-DT , AF129075.3 , LINC00691 , AL158206.1 ; N2+: LINC02315 ). In reaction to 2 Gy, the number of differentially expressed lncRNAs was higher (N0: 152, N1: 169, N2+: 146). After 2 Gy, AL109976.1 and AL158206.1 were prominently upregulated in all donor groups. The co-expression analysis identified two modules containing lncRNAs that were associated with 2 Gy (module1: 102 mRNAs and 4 lncRNAs: AL158206.1 , AL109976.1 , AC092171.5 , TYMSOS , associated with p53-mediated reaction to DNA damage ; module2: 390 mRNAs, 7 lncRNAs: AC004943.2 , AC012073.1 , AC026401.3 , AC092718.4 , MIR31HG , STXBP5-AS1 , TMPO-AS1 , associated with cell cycle regulation )., Discussion: For the first time, we identified the lncRNAs AL158206.1 and AL109976.1 as involved in the radiation response in primary fibroblasts by differential expression analysis. The co-expression analysis revealed a role of these lncRNAs in the DNA damage response and cell cycle regulation post-IR. These transcripts may be targets in cancer therapy against radiosensitivity, as well as provide grounds for the identification of at-risk patients for immediate adverse reactions in healthy tissues. With this work we deliver a broad basis and new leads for the examination of lncRNAs in the radiation response., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Grandt, Brackmann, Poplawski, Schwarz, Marini, Hankeln, Galetzka, Zahnreich, Mirsch, Spix, Blettner, Schmidberger and Marron.)

Low-grade gliomas (LGG) are central nervous system Grade I tumors, and as they progress they are becoming one of the deadliest brain tumors. There is still great need for timely and accurate diagnosis and prognosis of LGG. Herein, we aimed to identify diagnostic and prognostic biomarkers associated with LGG, by employing diverse computational approaches. For this purpose, differential gene expression analysis on high-throughput transcriptomics data of LGG versus corresponding healthy brain tissue, derived from TCGA and GTEx, respectively, was performed. Weighted gene co-expression network analysis of the detected differentially expressed genes was carried out in order to identify modules of co-expressed genes significantly correlated with LGG clinical traits. The genes comprising these modules were further used to construct gene co-expression and protein-protein interaction networks. Based on the network analyses, we derived a consensus of eighteen hub genes, namely, CD74, CD86, CDC25A, CYBB, HLA-DMA, ITGB2, KIF11, KIFC1, LAPTM5, LMNB1, MKI67, NCKAP1L, NUSAP1, SLC7A7, TBXAS1, TOP2A, TYROBP , and WDFY4 . All detected hub genes were up-regulated in LGG, and were also associated with unfavorable prognosis in LGG patients. The findings of this study could be applicable in the clinical setting for diagnosing and monitoring LGG., Competing Interests: Gökhan Karakülah and Aslı Suner are Academic Editors for PeerJ., (© 2023 Özbek et al.)

Epacromius coerulipes (Ivanov) is one of the most widely distributed locusts. To date, the main methods to kill locusts still rely on chemical controls, which can result in the selection of locusts with resistance to chemical pesticides. Butene-fipronil is a new pesticide that was discovered by the structural modification of fipronil. This pesticide has been used to control various agricultural pests and has become an important pesticide product to control pests that exhibit resistance to other pesticides, including locusts. To extend its useful half-life, studies of the initiation and progression of resistance to this pesticide are needed. Herein, two E. coerulipes strains, a pesticide-sensitive (PS) and a pesticide-resistant (PR) strain, were chosen to undergo de novo assembly by paired-end transcriptome Illumina sequencing. Overall, 63,033 unigenes were detected; the average gene length was 772 bp and the N50 was 1,589 bp. Among these unigenes, -25,132 (39.87% of the total) could be identified as known proteins in bioinformatic databases from national centers. A comparison of the PR and PS strains revealed that 2,568 genes were differentially expressed, including 1,646 and 922 genes that were upand down-regulated, respectively. According to the Gene Ontology (GO) database, among biological processes the metabolic process group was the largest group (6,900 genes, 22.47%) and contained a high frequency of differentially expressed genes (544 genes, 27.54%). According to the Clusters of Orthologous Groups (COG) categories, 28 genes, representing 2.98% of all genes, belonged to the group of genes involved in the biosynthesis, transportation, and catabolism of secondary metabolites. The differentially expressed genes that we identified are involved in 50 metabolic pathways. Among these pathways, the metabolism pathway was the most represented. After enrichment analysis of differential gene expression pathways, six pathways-ribosome; starch, and sucrose metabolism; ascorbate and aldarate metabolism; drug metabolism-cytochrome P450; metabolism of xenobiotics by cytochrome P450; and glutathione metabolism-showed a high degree of enrichment. Among these pathways, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and glutathione metabolism have been associated with pesticide metabolism. Furthermore, 316 unigenes in the E. coerulipes transcriptome encode detoxifying enzymes and 76 unigenes encode target proteins of pesticides. Among these genes, 23 genes that encode detoxifying enzymes in the resistance group were found to be upregulated. The transcriptome sequencing results of E. coerulipes established a genomics database of E. coerulipes for the first time. This study also establishes a molecular basis for gene function analysis of E. coerulipes. Moreover, it provides a theoretical resource for mechanistic studies on pesticide resistance through the screening and investigation of resistance genes. [ABSTRACT FROM AUTHOR]

Understanding the molecular mechanism of clear cell renal cell carcinoma (ccRCC) is essential for predicting the prognosis and developing new targeted therapies. Our study is to identify hub genes related to ccRCC and to further analyze its prognostic significance. The ccRCC gene expression profiles of GSE46699 from the Gene Expression Omnibus (GEO) database and datasets from the Cancer Genome Atlas Database The Cancer Genome Atlas were used for the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We screened out 397 overlapping genes from the four sets of results, and then performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. In addition, the protein-protein interaction (PPI) network of 397 overlapping genes was mapped using the STRING database. We identified ten hub genes (KNG1, TIMP1, ALB, C3, GPC3, VCAN, P4HB, CHGB, LGALS1, EGF) using the CytoHubba plugin of Cytoscape based on the Maximal Clique Centrality (MCC) score. According to Kaplan-Meier survival analysis, higher expression of LGALS1 and TIMP1 was related to poorer overall survival (OS) in patients with ccRCC. Univariate and multivariate Cox proportional hazard analysis showed that the expression of LGALS1 was an independent risk factor for poor prognosis. Moreover, the higher the clinical grade and stage of ccRCC, the higher the expression of LGALS1. LGALS1 may play an important role in developing ccRCC and may be potential a biomarker for prognosis and treatment targets., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Fang, Wang, Xie, Zhang, Xiao, Li and Luo.)

RNA sequencing (RNA-seq) is a powerful method of transcriptional analysis that allows for the sequence identification and quantification of cellular transcripts. RNA-seq can be used for differential gene expression (DGE) analysis, gene fusion detection, allele-specific expression, isoform and splice variant quantification, and identification of novel genes. These applications can be used for downstream systems biology analyses such as gene ontology or pathway analysis to provide insight into processes altered between biological conditions. Given the wide range of signaling pathways subject to chaperone activity as well as numerous chaperone functions in RNA metabolism, RNA-seq may provide a valuable tool for the study of chaperone proteins in biology and disease. This chapter outlines an example RNA-seq workflow to determine differentially expressed (DE) genes between two or more sample conditions and provides some considerations for RNA-seq experimental design., (© 2023. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Bladder cancer (BLCA) is an extremely common carcinoma of the urinary system that has a high incidence of relapse. Although intensive studies have investigated its pathology in the past decades, there are significant knowledge gaps regarding the characterization of the molecular processes underlying the progression of disease and consequently its prognosis. The purpose of current research was to identify significant genes that could serve as prognostic and progression biomarkers., Methods: Gene expression profiles were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Differential gene expression analysis (DGEA) and weighted gene co-expression network analysis (WGCNA) were conducted to recognize differential co-expression genes. Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to explore gene function. Moreover, protein-protein interactions (PPI) network, overall survival (OS) and disease-free survival (DFS) were used to identify survival-related hub genes. Additionally, associations between these gene's expression and clinical parameters were determined. Finally, the Human Protein Atlas (HPA) database and qRT-PCR were used to validate gene's expression., Results: About 124 differential co-expression genes were identified. These genes were mainly enriched in muscle system process and muscle contraction (biological process, BP), contractile fiber, myofibril, sarcomere, focal adhesion and cell-substrate junction (cellular component, CC) and actin binding (molecular function, MF) in GO enrichment analysis, while enriched in vascular smooth muscle contraction, focal adhesion, cardiac muscle contraction, hypertrophic cardiomyopathy, dilated cardiomyopathy and regulation of actin cytoskeleton in KEGG analysis. Furthermore, five survival-related hub genes (MYH11, ACTA2, CALD1, TPM1, MYLK) were identified via OS and DFS. In addition, these survival-related gene's expression was correlated with grade, stage and TNM stage. Finally, all survival-related hub genes were determined to be down-regulated in BLCA tissues by qRT-PCR and HPA databases., Conclusions: Our current study verified five new key genes in BLCA, which may participate in the prognosis and progression and serve as novel biomarkers of BLCA.

In recent years, rNa-Seq technologies became a powerful tool for transcriptome studies. However, computational methods dedicated to the analysis of high-throughput sequencing data are yet to be standardized. In particular, it is known that the choice of a normalization procedure leads to a great variability in results of differential gene expression analysis. the present study compares the most widespread normalization procedures and proposes a novel one aiming at removing an inherent bias of studied transcriptomes related to their relative size. Comparisons of the normalization procedures are performed on real and simulated data sets. real rNa-Seq data sets analyses, performed with all the different normalization methods, show that only 50% of significantly differentially expressed genes are common. This result highlights the influence of the normalization step on the differential expression analysis. Real and simulated data sets analyses give similar results showing 3 different groups of procedures having the same behavior. The group including the novel method named "median ratio Normalization"(mrN) gives the lower number of false discoveries. Within this group the mrN method is less sensitive to the modification of parameters related to the relative size of transcriptomes such as the number of down- and upregulated genes and the gene expression levels. the newly proposed mrN method efficiently deals with intrinsic bias resulting from relative size of studied transcriptomes. Validation with real and simulated data sets confirmed that mrN is more consistent and robust than existing methods. [ABSTRACT FROM AUTHOR]

This article describes specific procedures for conducting quality assessment of Affymetrix GeneChip® soybean genome data and for performing analyses to determine differential gene expression using the open-source R programming environment in conjunction with the open-source Bioconductor software. We describe procedures for extracting those Affymetrix probe set IDs related specifically to the soybean genome on the Affymetrix soybean chip and demonstrate the use of exploratory plots including images of raw probe-level data, boxplots, density plots and M versus A plots. RNA degradation and recommended procedures from Affymetrix for quality control are discussed. An appropriate probe-level model provides an excellent quality assessment tool. To demonstrate this, we discuss and display chip pseudo-images of weights, residuals and signed residuals and additional probe-level modeling plots that may be used to identify aberrant chips. The Robust Multichip Averaging (RMA) procedure was used for background correction, normalization and summarization of the AffyBatch probe-level data to obtain expression level data and to discover differentially expressed genes. Examples of boxplots and MA plots are presented for the expression level data. Volcano plots and heatmaps are used to demonstrate the use of (log) fold changes in conjunction with ordinary and moderated t-statistics for determining interesting genes. We show, with real data, how implementation of functions in R and Bioconductor successfully identified differentially expressed genes that may play a role in soybean resistance to a fungal pathogen, Phakopsora pachyrhizi. Complete source code for performing all quality assessment and statistical procedures may be downloaded from our web source: http://css. ncifcrf.gov/services/download/MicroarraySoybean.zip. [ABSTRACT FROM AUTHOR]

Nephrotoxicity is the most important dose-limiting factor in cisplatin based anti-neoplastic treatment. Pretreatment with bismuth salts, used as pharmaceuticals to treat gastric disorders, has been demonstrated to reduce cisplatin-induced renal cell death in clinical settings and during in vivo and in vitro animal experiments. To investigate the genomic basis of this renoprotective effect, we exposed NRK-52E cells, a cell line of rat proximal tubular epithelial origin, to 33 μ M Bi[sup 3] for 12 hours, which made them resistant to cisplatin-induced apoptosis. Differentially expressed genes in treated and untreated NRK-52E cells were detected by subtraction PCR and microarray techniques. Genes found to be down regulated (0.17-0.31-times) were cytochrome c oxidase subunit I, BAR (an apoptosis regulator), heat-shock protein 70-like protein, and three proteins belonging to the translation machinery (ribosomal proteins S7 and L17, and S1, a member of the elongation factor 1-alpha family). The only up-regulated gene was glutathione Stransferase subunit 3A (1.89-times). Guided by the expression levels of these genes, it may be possible to improve renoprotective treatments during anti-neoplastic therapies. [ABSTRACT FROM AUTHOR]

Biologics represent the fastest growing group of therapeutics, but many advanced recombinant protein moieties remain difficult to produce. Here, we identify metabolic engineering targets limiting expression of recombinant human proteins through a systems biology analysis of the transcriptomes of CHO and HEK293 during recombinant expression. In an expression comparison of 24 difficult to express proteins, one third of the challenging human proteins displayed improved secretion upon host cell swapping from CHO to HEK293. Guided by a comprehensive transcriptomics comparison between cell lines, especially highlighting differences in secretory pathway utilization, a co-expression screening of 21 secretory pathway components validated ATF4, SRP9, JUN, PDIA3 and HSPA8 as productivity boosters in CHO. Moreover, more heavily glycosylated products benefitted more from the elevated activities of the N- and O-glycosyltransferases found in HEK293. Collectively, our results demonstrate the utilization of HEK293 for expression rescue of human proteins and suggest a methodology for identification of secretory pathway components for metabolic engineering of HEK293 and CHO., (Copyright © 2022 The Authors. Published by Elsevier Inc. All rights reserved.)

Background: Acute myeloid leukemia (AML) is a malignant and molecularly heterogeneous disease. It is essential to clarify the molecular mechanisms of AML and develop targeted treatment strategies to improve patient prognosis., Methods: AML mRNA expression data and survival status were extracted from TCGA and GEO databases (GSE37642, GSE76009, GSE16432, GSE12417, GSE71014). Weighted gene co-expression network analysis (WGCNA) and differential gene expression analysis were performed. Functional enrichment analysis and protein-protein interaction (PPI) network were used to screen out hub genes. In addition, we validated the expression levels of hub genes as well as the prognostic value and externally validated TRIM32 with clinical data from our center. AML cell lines transfected with TRIM32 shRNA were also established to detect the proliferation in vitro ., Results: A total of 2192 AML patients from TCGA and GEO datasets were included in this study and 20 differentially co-expressed genes were screened by WGCNA and differential gene expression analysis methods. These genes were mainly enriched in phospholipid metabolic processes (biological processes, BP), secretory granule membranes (cellular components, CC), and protein serine/threonine kinase activity (molecular functions, MF). In addition, the protein-protein interaction (PPI) network contains 15 nodes and 15 edges and 10 hub genes (TLE1, GLI2, HDAC9, MICALL2, DOCK1, PDPN, RAB27B, SIX3, TRIM32 and TBX1) were identified. The expression of 10 central genes, except TLE1, was associated with survival status in AML patients ( p <0.05). High expression of TRIM32 was tightly associated with poor relapse-free survival (RFS) and overall survival (OS) in AML patients, which was verified in the bone marrow samples from our center. In vitro , knockdown of TRIM32 can inhibit the proliferation of AML cell lines., Conclusion: TRIM32 was associated with the progression and prognosis of AML patients and could be a potential therapeutic target and biomarker for AML in the future., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Xu, Qi, Yang, Pan, Han, Yang and Han.)

An improved understanding of the molecular mechanism of colorectal adenocarcinoma is necessary to predict the prognosis and develop new target gene therapy strategies. This study aims to identify hub genes associated with colorectal adenocarcinoma and further analyze their prognostic significance. In this study, The Cancer Genome Atlas (TCGA) COAD-READ database and the gene expression profiles of GSE25070 from the Gene Expression Omnibus were collected to explore the differentially expressed genes between colorectal adenocarcinoma and normal tissues. The weighted gene co-expression network analysis (WGCNA) and differential expression analysis identified 82 differentially co-expressed genes in the collected datasets. Enrichment analysis was applied to explore the regulated signaling pathway in colorectal adenocarcinoma. In addition, 10 hub genes were identified in the protein-protein interaction (PPI) network by using the cytoHubba plug-in of Cytoscape, where five genes were further proven to be significantly related to the survival rate. Compared with normal tissues, the expressions of the five genes were both downregulated in the GSE110224 dataset. Subsequently, the expression of the five hub genes was confirmed by the Human Protein Atlas database. Finally, we used Cox regression analysis to identify genes associated with prognosis, and a 3-gene signature (CLCA1-CLCA4-GUCA2A) was constructed to predict the prognosis of patients with colorectal cancer. In conclusion, our study revealed that the five hub genes and CLCA1-CLCA4-GUCA2A signature are highly correlated with the development of colorectal adenocarcinoma and can serve as promising prognosis factors to predict the overall survival rate of patients., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2022 Liu, Chen, Meng, Ye and Ma.)

RNA-seq is a common approach used to explore gene expression data between experimental conditions or cell types and ultimately leads to information that can shed light on the biological processes involved and inform further hypotheses. While the protocols required to generate samples for sequencing can be performed in most research facilities, the resulting computational analysis is often an area in which researchers have little experience. Here we present a user-friendly bioinformatics workflow which describes the methods required to take raw data produced by RNA sequencing to interpretable results. Widely used and well documented tools are applied. Data quality assessment and read trimming were performed by FastQC and Cutadapt, respectively. Following this, STAR was utilized to map the trimmed reads to a reference genome and the alignment was analyzed by Qualimap. The subsequent mapped reads were quantified by featureCounts. DESeq2 was used to normalize and perform differential expression analysis on the quantified reads, identifying differentially expressed genes and preparing the data for functional enrichment analysis. Gene set enrichment analysis identified enriched gene sets from the normalized count data and clusterProfiler was used to perform functional enrichment against the GO, KEGG, and Reactome databases. Example figures of the functional enrichment analysis results were also generated. The example data used in the workflow are derived from HUVECs, an in vitro model used in the study of endothelial cells, published and publicly available for download from the European Nucleotide Archive., (© 2022. The Author(s), under exclusive license to Springer Science+Business Media, LLC, part of Springer Nature.)

RNA-sequencing (RNA-seq) is a relatively new technology that lacks standardisation. RNA-seq can be used for Differential Gene Expression (DGE) analysis, however, no consensus exists as to which methodology ensures robust and reproducible results. Indeed, it is broadly acknowledged that DGE methods provide disparate results. Despite obstacles, RNA-seq assays are in advanced development for clinical use but further optimisation will be needed. Herein, five DGE models (DESeq2, voom + limma, edgeR, EBSeq, NOISeq) for gene-level detection were investigated for robustness to sequencing alterations using a controlled analysis of fixed count matrices. Two breast cancer datasets were analysed with full and reduced sample sizes. DGE model robustness was compared between filtering regimes and for different expression levels (high, low) using unbiased metrics. Test sensitivity estimated as relative False Discovery Rate (FDR), concordance between model outputs and comparisons of a 'population' of slopes of relative FDRs across different library sizes, generated using linear regressions, were examined. Patterns of relative DGE model robustness proved dataset-agnostic and reliable for drawing conclusions when sample sizes were sufficiently large. Overall, the non-parametric method NOISeq was the most robust followed by edgeR, voom, EBSeq and DESeq2. Our rigorous appraisal provides information for method selection for molecular diagnostics. Metrics may prove useful towards improving the standardisation of RNA-seq for precision medicine., Competing Interests: Professor Richard Kennedy receives payment as the medical director for Almac Diagnostic Services, M.S.T has recently received honoraria for advisory work in relation to the following companies: Incyte, MindPeak, QuanPathDerivatives and MSD. He is part of academia-industry consortia supported by the UK government (Innovate UK). These are all unrelated to this work. The other authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2021 The Authors.)

Purpose: Detecting and diagnosing gastric cancer (GC) during its early period remains greatly difficult. Our analysis was performed to detect core genes correlated with GC and explore their prognostic values., Methods: Microarray datasets from the Gene Expression Omnibus (GEO) (GSE54129) and The Cancer Genome Atlas (TCGA)-stomach adenocarcinoma (STAD) datasets were applied for common differentially co-expressed genes using differential gene expression analysis and Weighted Gene Co-expression Network Analysis (WGCNA). Functional enrichment analysis and protein-protein interaction (PPI) network analysis of differentially co-expressed genes were performed. We identified hub genes via the CytoHubba plugin. Prognostic values of hub genes were explored. Afterward, Gene Set Enrichment Analysis (GSEA) was used to analyze survival-related hub genes. Finally, the tumor-infiltrating immune cell (TIC) abundance profiles were estimated., Results: Sixty common differentially co-expressed genes were found. Functional enrichment analysis implied that cell-cell junction organization and cell adhesion molecules were primarily enriched. Hub genes were identified using the degree, edge percolated component (EPC), maximal clique centrality (MCC), and maximum neighborhood component (MNC) algorithms, and serpin family E member 1 (SERPINE1) was highly associated with the prognosis of GC patients. Moreover, GSEA demonstrated that extracellular matrix (ECM) receptor interactions and pathways in cancers were correlated with SERPINE1 expression. CIBERSORT analysis of the proportion of TICs suggested that CD8+ T cell and T-cell regulation were negatively associated with SERPINE1 expression, showing that SERPINE1 may inhibit the immune-dominant status of the tumor microenvironment (TME) in GC., Conclusions: Our analysis shows that SERPINE1 is closely correlated with the tumorigenesis and progression of GC. Furthermore, SERPINE1 acts as a candidate therapeutic target and prognostic biomarker of GC., (© 2021 The Author(s).)

In the current study, we investigated the role of PAK1 (P21 (RAC1) Activated Kinase 1) gene in breast cancer and to this end, we performed differential gene expression analysis of PAK1 in breast cancer tissues compared to the normal adjacent tissue. We also studied its significance in protein-protein interaction (PPI) network, and analysed biological pathways, cellular processes, and role of PAK1 in different diseases. We found PAK1 to have significant role in breast cancer pathways such as integrin signaling, axonal guidance signaling, signaling by Rho family GTPases, ERK5 signaling. Additionally, it has been found as hub gene in PPI network, suggesting its possible regulatory role in breast carcinogenesis. Moreover, PAK1 had role in progression of various diseases as neoplasia, tumorigenesis, lymphatic neoplasia. Thereby, PAK1 can be used as a therapeutic target in breast cancer. Further, we put our efforts in identification of potential small molecules inhibitors against PAK1 by developing a composite virtual screening protocol involving molecular dynamics (MD) and molecular docking. The chemical library of compounds from NCI diversity sets, Pubchem and eMolecules were screened against PAK1 protein and hits which showed good binding affinity were considered for MD simulation study. Moreover, to assess binding of selected hits, MMGBSA (Molecular Mechanics-Generalized Born Surface Area) analysis was performed using AMBER (Assisted Model Building with Energy Refinement) package. MMGBSA calculations exhibited that the identified ligands showed good binding affinity with PAK1. HighlightsThe PAK1 has been found to be upregulated in breast cancer samples and is a potential oncogene playing role in different cellular functions and processes.The molecular docking studies revealed ligands showed good binding affinity towards PAK1 protein.The residues Glu345, Leu347, Thr406, Asp299, Asp393 and Gly350 were found to make H-bond interactions with small molecule inhibitors.The residues Ile276, Val284, Ala297, Tyr346, Leu396 and Asp407 were found to make hydrophobic interactions.The RMSD analysis confirmed stability of complexes throughout 40 ns production period.The MD simulations studies revealed the binding site flexibility, binding free energy of complexes and per-residue contribution in ligand binding.Communicated by Ramaswamy H. Sarma.

Background: Spinal cord injury (SCI) has an immediate and devastating impact on the control over various movements and sensations. However, no effective therapies for SCI currently exist., Methods: To identify and analyze SCI subtypes, we obtained the expression profile data of the 1,057 genes (889 intersection genes) in GSE45550 using weighted gene co-expression network analysis (WGCNA), and 14 co-expression gene modules were identified. Next, we filtered out the network degree top 10 (degree >80) genes, considered the final key SCI genes. A multifactor regulatory network (105 interaction pairs), consisting of messenger RNAs (mRNAs), long non-coding RNAs (lncRNAs), and transcription factors (TFs) was constructed. This network was involved in the co-expression of key genes. We selected the top 10 regulatory factors (degree >4) as core regulators in the multifactor regulatory network., Results: The results of functional enrichment analysis of the target gene expressing the core regulatory factor [1,059] showed that these target genes were enriched in pathways for human cytomegalovirus infection, chronic myeloid leukemia, and pancreatic cancer. Further, we used the key genes in the co-expression network to categorize the SCI samples in GSE45550. The expression levels of the top 6 genes ( CCNB2, CCNB1, CKS2, COL5A1, KIF20A, and RACGAP1 ) may act as potential marker genes for different SCI subtypes. On the basis of these different subtypes, 8 SCI core gene CDK1-associated drugs were also found to provide potential therapeutic options for SCI., Conclusions: These results may provide a novel therapeutic strategy for the treatment of SCI., Competing Interests: Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/atm-21-340). The authors have no conflicts of interest to declare., (2021 Annals of Translational Medicine. All rights reserved.)

ProTExA is a web-tool that provides a post-processing workflow for the analysis of protein and gene expression datasets. Using network-based bioinformatics approaches, ProTExA facilitates differential expression analysis and co-expression network analysis as well as pathway and post-pathway analysis. Specifically, for a given set of protein-gene expression data across samples, ProTExA: (1) performs statistical analysis and filtering to highlight the differentially expressed proteins-genes, (2) performs enrichment analysis to identify top-scored pathways, (3) generates pathway-to-pathway and pathway-to-gene networks (4) generates protein and gene co-expression networks using a variety of methodologies, and (5) applies clustering methodologies to identify sub-networks of co-expressed proteins-genes. The proposed web-tool is a simple yet informative tool, towards understanding and exploitation of protein and gene expression datasets, especially for those that do not have the expertise and local resources to replicate specific analyses in the context of collaborative and scientific data exchanging., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2020 The Author(s).)

Improved insight into the molecular mechanisms of head and neck squamous cell carcinoma (HNSCC) is required to predict prognosis and develop a new therapeutic strategy for targeted genes. The aim of this study is to identify significant genes associated with HNSCC and to further analyze its prognostic significance. In our study, the cancer genome atlas (TCGA) HNSCC database and the gene expression profiles of GSE6631 from the Gene Expression Omnibus (GEO) were used to explore the differential co-expression genes in HNSCC compared with normal tissues. A total of 29 differential co-expression genes were screened out by Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis methods. As suggested in functional annotation analysis using the R clusterProfiler package, these genes were mainly enriched in epidermis development and differentiation (biological process), apical plasma membrane and cell-cell junction (cellular component), and enzyme inhibitor activity (molecular function). Furthermore, in a protein-protein interaction (PPI) network containing 21 nodes and 25 edges, the ten hub genes (S100A8, S100A9, IL1RN, CSTA, ANXA1, KRT4, TGM3, SCEL, PPL, and PSCA) were identified using the CytoHubba plugin of Cytoscape. The expression of the ten hub genes were all downregulated in HNSCC tissues compared with normal tissues. Based on survival analysis, the lower expression of CSTA was associated with worse overall survival (OS) in patients with HNSCC. Finally, the protein level of CSTA, which was validated by the Human Protein Atlas (HPA) database, was down-regulated consistently with mRNA levels in head and neck cancer samples. In summary, our study demonstrated that a survival-related gene is highly correlated with head and neck cancer development. Thus, CSTA may play important roles in the progression of head and neck cancer and serve as a potential biomarker for future diagnosis and treatment., (Copyright © 2020 Li, Cai, Tsai and Wang.)

The reactivation of telomerase in cancer cells remains incompletely understood. The catalytic component of telomerase, hTERT, is thought to be the limiting component in cancer cells for the formation of active enzymes. hTERT gene expression is regulated at several levels including chromatin, DNA methylation, transcription factors, and RNA processing events. Of these regulatory events, RNA processing has received little attention until recently. RNA processing and alternative splicing regulation have been explored to understand how hTERT is regulated in cancer cells. The cis- and trans-acting factors that regulate the alternative splicing choice of hTERT in the reverse transcriptase domain have been investigated. Further, it was discovered that the splicing factors that promote the production of full-length hTERT were also involved in cancer cell growth and survival. The goals are to review telomerase regulation via alternative splicing and the function of hTERT splicing variants and to point out how bioinformatics approaches are leading the way in elucidating the networks that regulate hTERT splicing choice and ultimately cancer growth. [ABSTRACT FROM AUTHOR]

Alternative splicing (AS) generates functionally distinct transcripts and is involved in multiple cellular processes, including stem cell differentiation. Several epithelial-to-mesenchymal transition-related splicing factors have also been associated with pluripotency. Concomitantly with the interest in studying AS in stem cell biology, the advent of next-generation sequencing of RNA (RNA-seq) has increased the public availability of transcriptomic data and enabled genome-wide AS studies. To facilitate performing such analyses in large publicly available or user-provided transcriptomics datasets, the psichomics R package provides an easy-to-use interface and efficient data visualization tools for AS quantification and integrative analyses of AS and gene expression data.psichomics is employed herein to study AS changes between human stem cells and fibroblasts, based on dimensionality reduction, and median- and variance-based differential AS and gene expression analyses. Putative RNA-binding protein regulators involved in those alterations are then identified based on correlation analyses in large cohorts of human tissue transcriptomes. We identified several alterations, both novel and previously reported, in alternative splicing events and in the expression of their candidate regulators that are associated with stem cell differentiation into fibroblasts.

Estimating fold changes of average mRNA and protein molecule counts per cell is the most common way to perform differential expression analysis. However, these gene expression data may be affected by cell division, an often-neglected phenomenon. Here, we develop a quantitative framework that links population-based mRNA and protein measurements to rates of gene expression in single cells undergoing cell division. The equations we derive are easy-to-use and widely robust against biological variability. They integrate multiple "omics" data into a coherent, quantitative description of single-cell gene expression and improve analysis when comparing systems or states with different cell division times. We explore these ideas in the context of resting versus activated B cells. Analyzing differences in protein synthesis rates enables to account for differences in cell division times. We demonstrate that this improves the resolution and hit rate of differential gene expression analysis when compared to analyzing population protein abundances alone., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Differential gene expression (DGE) studies often suffer from poor interpretability of their primary results, i.e., thousands of differentially expressed genes. This has led to the introduction of gene set analysis (GSA) methods that aim at identifying interpretable global effects by grouping genes into sets of common context, such as, molecular pathways, biological function or tissue localization. In practice, GSA often results in hundreds of differentially regulated gene sets. Similar to the genes they contain, gene sets are often regulated in a correlative fashion because they share many of their genes or they describe related processes. Using these kind of neighborhood information to construct networks of gene sets allows to identify highly connected sub-networks as well as poorly connected islands or singletons. We show here how topological information and other network features can be used to filter and prioritize gene sets in routine DGE studies. Community detection in combination with automatic labeling and the network representation of gene set clusters further constitute an appealing and intuitive visualization of GSA results. The RICHNET workflow described here does not require human intervention and can thus be conveniently incorporated in automated analysis pipelines., Competing Interests: No competing interests were disclosed.

High throughput techniques such as RNA-seq or microarray analysis have proven to be invaluable for the characterization of global transcriptional gene activity changes due to external stimuli or diseases. Differential gene expression analysis (DGEA) is the first step in the course of data interpretation, typically producing lists of dozens to thousands of differentially expressed genes. To further guide the interpretation of these lists, different pathway analysis approaches have been developed. These tools typically rely on the classification of genes into sets of genes, such as pathways, based on the interactions between the genes and their function in a common biological process. Regardless of technical differences, these methods do not properly account for cross talk between different pathways and rely on binary separation into differentially expressed gene and unaffected genes based on an arbitrarily set p-value cut-off. To overcome this limitation, we developed a novel approach to identify concertedly modulated sub-graphs in the global cell signaling network, based on the DGEA results of all genes tested. Thereby, expression patterns of genes are integrated according to the topology of their interactions and allow potentially to read the flow of information from the perturbation source to the effectors. The described software, named Modulated Sub-graph Finder (MSF) is freely available at https: //github.com/Modulated-Subgraph-Finder/MSF., Competing Interests: No competing interests were disclosed.