Differences in T cell-associated serum markers between ischemic cardiomyopathy and dilated cardiomyopathy.

Background: Ischemic cardiomyopathy (ICM) and dilated cardiomyopathy (DCM) have similar clinical manifestations but differ in pathogenesis. We aimed to identify T cell-associated serum markers that can be used to distinguish between ICM and DCM. Methods: We identified differentially expressed genes (DEGs) with transcriptome sequencing data in GSE116250, and then conducted enrichment analysis of DEGs in the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Protein-protein interaction (PPI) networks were used to analyze the relationship between T cells-related genes and identify hub genes. Enzyme-linked immunosorbent assay (ELISA) kits were used to detect T cell-associated proteins in serum, and receiver operating characteristic (ROC) curves were used to evaluate the diagnostic efficacy of these serum markers. Results: Using the limma package and Venn plots, we found that the non-failing donors (NFD) and DCM groups shared many of the same DEGs and DEGs-enriched functions compared to the ICM group, which were involved in T cell activation and differentiation, among other functions. Subsequently, the immune cell score showed no difference between NFD and DCM, but they were significantly different from ICM patients in CD8 T cells CD4 T cells memory resting and activated, T cells follicular helper, and M1 macrophage. After analyzing T cell-associated DEGs, it was found that 4 DEGs encoding secreted proteins were highly expressed in the ICM group compared with the NFD and DCM groups, namely chemokine (C-C motif) ligand 21 (CCL21), interleukin (IL)-1ß, lymphocyte-activation gene 3 (LAG3), and vascular cell adhesion molecule-1 (VCAM-1). Importantly, the serum levels of CCL21, IL-1ß, LAG3, and VCAM-1 in ICM patients were all significantly higher than those in DCM patients. The ROC curves showed that the area under the curve (AUC) values of serum CCL21, IL-1ß, LAG3, and VCAM-1 were 0.775, 0.868, 0.934, and 0.903, respectively. Conclusions: We have identified four T cell-associated serum markers, CCL21, IL-1ß, LAG3, and VCAM-1, as potential diagnostic serum markers that differentiate ICM from DCM.

Key genes and immune infiltration patterns and the clinical implications in psoriasis patients.

BACKGROUND: Psoriasis is an immune-mediated skin disease, closely related to immune regulation. The aim was to understand the pathogenesis of psoriasis further, reveal potential therapeutic targets, and provide new clues for its diagnosis, treatment, and prevention. MATERIALS AND METHODS: Expression profiling data were obtained from the Gene Expression Omnibus (GEO) database for skin tissues from healthy population and psoriasis patients. Differentially expressed genes (DEGs) were selected for Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) analysis separately. Machine learning algorithms were used to obtain characteristic genes closely associated with psoriasis. Receiver operating characteristic (ROC) curve was used to assess the diagnostic value of the characteristic genes for psoriasis. The Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) algorithm was used to calculate the proportion of immune cell infiltration. Correlation analysis was used to characterize the connection between gene expression and immune cell, Psoriasis Area and Severity Index (PASI). RESULTS: A total of 254 DEGs were identified in the psoriasis group, including 185 upregulated and 69 downregulated genes. GO was mainly enriched in cytokine-mediated signaling pathway, response to virus, and cytokine activity. KEGG was mainly focused on cytokine-cytokine receptor interaction and IL-17 signaling pathway. GSEA was mainly in chemokine signaling pathway and cytokine-cytokine receptor interaction. The machine learning algorithm screened nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9. In the validation set, the expressions of these nine genes increased in the psoriasis group, and the AUC values were all > 0.9, consistent with those of the training set. The immune infiltration results showed increased proportions of macrophages, T cells, and neutrophils in the psoriasis group. The characteristic genes were positively or negatively correlated to varying degrees with T cells and macrophages. Nine characteristic genes were highly expressed in the moderate to severe psoriasis group and positively correlated with PASI scores. CONCLUSION: High levels of nine characteristic genes C10orf99, GDA, FCHSD1, C12orf56, S100A7, INA, CHRNA9, IFI44, and CXCL9 were risk factors for psoriasis, the differential expression of which was related to the regulation of immune system activity and PASI scores, affecting the proportions of different immune cells and promoting the occurrence and development of psoriasis.

Comparative global profiling of Perilla leaf and stem via transcriptomics and metabolomics.

Perilla (Perilla frutescens L.) is a time-honored herbal plant with widespread applications in both medicine and culinary practices around the world. Profiling the essential organs and tissues with medicinal significance on a global scale offers valuable insights for enhancing the yield of desirable compounds in Perilla and other medicinal plants. In the present study, genome-wide RNA-sequencing (RNA-seq) and assessing the global spectrum of metabolites were carried out in the two major organs/tissues of stem (PfST) and leaf (PfLE) in Perilla. The results showed a total of 18,490 transcripts as the DEGs (differentially expressed genes) and 144 metabolites as the DAMs (differentially accumulated metabolites) through the comparative profiling of PfST vs PfLE, and all the DEGs and DAMs exhibited tissue-specific trends. An association analysis between the transcriptomics and metabolomics revealed 14 significantly enriched pathways for both DEGs and DAMs, among which the pathways of Glycine, serine and threonine metabolism (ko00260), Glyoxylate and dicarboxylate metabolism (ko00630), and Glucagon signaling pathway (ko04922) involved relatively more DEGs and DAMs. The results of qRT-PCR assays of 18 selected DEGs confirmed the distinct tissue-specific characteristics of all identified DEGs between PfST and PfLE. Notably, all eight genes associated with the flavonoid biosynthesis/metabolism pathways exhibited significantly elevated expression levels in PfLE compared to PfST. This observation suggests a heightened accumulation of metabolites related to flavonoids in Perilla leaves. The findings of this study offer a comprehensive overview of the organs and tissues in Perilla that have medicinal significance.

Identifying key pathogenic mechanisms and potential intervention targets for recurrence after laryngeal cancer treatment through bioinformatics screening.

Background: Laryngeal cancer (LC), a prevalent malignant tumor of the head and neck, is characterized by a high rate of postoperative recurrence and significant treatment challenges upon recurrence, severely impacting patients' quality of life. There is a pressing need for effective biomarkers in clinical practice to predict the risk of LC recurrence and guide the development of personalized treatment plans. This study uses bioinformatics methods to explore potential biomarkers for LC recurrence, focusing on key genes and exploring their functions and mechanisms of action in LC recurrence. The aim is to provide new perspectives and evidence for clinical diagnosis, prognostic evaluation, and targeted treatment of LC. Methods: Gene expression profiles from the GSE25727 data set in the Gene Expression Omnibus database were analyzed to detect the differentially expressed genes (DEGs) between the tumor tissues of postoperative recurrent and non-recurrent early stage LC patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were also conducted. A protein-protein interaction (PPI) network and transcription factor (TF)-DEG-microRNA (miRNA) network were developed using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, with key genes selected using the Molecular Complex Detection (MCODE) plugin. A Gene Set Enrichment Analysis (GSEA) was carried out to investigate the possible mechanisms of the key genes. A retrospective analysis was conducted using the clinical data of 83 LC patients. Immunohistochemical staining was used to examine the transcription level of the key genes in the LC tumor tissues and the factors affecting postoperative recurrence. Results: A total of 248 upregulated and 34 downregulated DEGs were identified in the GSE25727 data set. The PPI network analysis identified a significant module and five candidate genes (i.e., RRAGA, SLC38A9, WDR24, ATP6V1B1, and LAMTOR3). The construction of the TF-DEG-miRNA network indicated that ATP6V1B1 might be regulated by one TF and interact with 17 miRNAs. The KEGG and GSEA analyses suggested that ATP6V1B1 may influence LC recurrence through the involvement of pro-inflammatory and pro-fibrotic mediators, glutathione metabolism, matrix metalloproteinases, immune regulation, and lymphocyte interactions. The recurrence rate of the 83 LC patients included in the study was 19.3% (16/83). The immunohistochemistry results indicated that ATP6V1B1 was highly expressed in patients with recurrent LC. The univariate and multivariate logistic regression analyses revealed that tumor stage T3 (P=0.04), tumor stage T4 (P=0.01), and a high expression of ATP6V1B1 (P=0.02) were risk factors for recurrence after surgical treatment in LC patients. Conclusions: The key genes and signaling pathways identified through the bioinformatics screening provide insights into the potential mechanisms of the pathogenesis of LC. ATP6V1B1 may promote the recurrence of LC by weakening the immune phenotype. Our findings provide a theoretical basis for further research into clinical diagnostics and treatment strategies for LC.

Exploring Programmed Cell Death-Related Biomarkers and Disease Therapy Strategy in Nasopharyngeal Carcinoma Using Transcriptomics.

BACKGROUND: Uncontrolled cellular proliferation may result in the progression of diseases such as cancer that promote organism death. Programmed cell death (PCD) is an important mechanism that ensures the quality and quantity of cells, which could be developed as a potential biomarker for disease diagnosis and treatment. METHODS: RNA-seq data and clinical information of nasopharyngeal carcinoma (NPC) patients were downloaded from the Gene Expression Omnibus (GEO), and 1548 PCD-related genes were collected. We used the "limma" package to analyze differentially expressed genes (DEGs). The STRING database was used for protein interaction analysis, and the least absolute shrinkage and selection operator (Lasso) and support vector machines (SVMs) regression analyses were used to identify biomarkers. Then, the timeROC package was used for classifier efficiency assessment, and the "CIBERSORT" package was used for immune infiltration analysis. Wound healing and transwell migration assay were performed to evaluate migration and invasion. RESULTS: We identified 800 DEGs between our control and NPC patient groups, in which 59 genes appeared to be PCD-related DEGs, with their function closely associated with NPC progression, including activation of the PI3K-Akt, TGF-ß, and IL-17 signaling pathways. Furthermore, based on the STRING database, Cytoscape and six algorithms were employed to screen 16 important genes (GAPDH, FN1, IFNG, PTGS2, CXCL1, MYC, MUC1, LTF, S100A8, CAV1, CDK4, EZH2, AURKA, IL33, S100A9, and MIF). Subsequently, two reliably characterized biomarkers, FN1 and MUC1, were obtained from the Lasso and SVM analyses. The Receiver operating characteristic (ROC) curves showed that both biomarkers had area under the curve (AUC) values higher than 0.9. Meanwhile, the enrichment analysis showed that in NPC patients, the FN1 and MUC1 expression levels correlated with programmed cell death-related pathways. The enrichment analysis and cellular experimental results indicated that FN1 and MUC1 were overexpressed in NPC cells and associated with programmed cell death-related pathways. Importantly, FN1 and MUC1 severely affected the ability of NPC cells to migrate, invade, and undergo apoptosis. Finally, medroxyprogesterone acetate and 8-Bromo-cAMP acted as drug molecules for the docking of FN1 and MUC1 molecules, respectively, and had binding capacities of -9.17 and -7.27 kcal/mol, respectively. CONCLUSION: We examined the PCD-related phenotypes and screened FN1 and MUC1 as reliable biomarkers of NPC; our findings may promote the development of NPC treatment strategy.

Development and evaluation of a chronic kidney disease risk prediction model using random forest.

This research aims to advance the detection of Chronic Kidney Disease (CKD) through a novel gene-based predictive model, leveraging recent breakthroughs in gene sequencing. We sourced and merged gene expression profiles of CKD-affected renal tissues from the Gene Expression Omnibus (GEO) database, classifying them into two sets for training and validation in a 7:3 ratio. The training set included 141 CKD and 33 non-CKD specimens, while the validation set had 60 and 14, respectively. The disease risk prediction model was constructed using the training dataset, while the validation dataset confirmed the model's identification capabilities. The development of our predictive model began with evaluating differentially expressed genes (DEGs) between the two groups. We isolated six genes using Lasso and random forest (RF) methods-DUSP1, GADD45B, IFI44L, IFI30, ATF3, and LYZ-which are critical in differentiating CKD from non-CKD tissues. We refined our random forest (RF) model through 10-fold cross-validation, repeated five times, to optimize the mtry parameter. The performance of our model was robust, with an average AUC of 0.979 across the folds, translating to a 91.18% accuracy. Validation tests further confirmed its efficacy, with a 94.59% accuracy and an AUC of 0.990. External validation using dataset GSE180394 yielded an AUC of 0.913, 89.83% accuracy, and a sensitivity rate of 0.889, underscoring the model's reliability. In summary, the study identified critical genetic biomarkers and successfully developed a novel disease risk prediction model for CKD. This model can serve as a valuable tool for CKD disease risk assessment and contribute significantly to CKD identification.

Identification of Differentially Expressed Genes and Protein-Protein Interaction in Patients With COVID-19 and Diabetes Peripheral Neuropathy: A Bioinformatics and System Biology Approach.

The coronavirus disease 2019 (COVID-19) pandemic has had a significant impact globally, resulting in a higher death toll and persistent health issues for survivors, particularly those with pre-existing medical conditions. Numerous studies have demonstrated a strong correlation between catastrophic COVID-19 results and diabetes. To gain deeper insights, we analysed the transcriptome dataset from COVID-19 and diabetic peripheral neuropathic patients. Using the R programming language, differentially expressed genes (DEGs) were identified and classified based on up and down regulations. The overlaps of DEGs were then explored between these groups. Functional annotation of those common DEGs was performed using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), Bio-Planet, Reactome, and Wiki pathways. A protein-protein interaction (PPI) network was created with bioinformatics tools to understand molecular interactions. Through topological analysis of the PPI network, we determined hub gene modules and explored gene regulatory networks (GRN). Furthermore, the study extended to suggesting potential drug molecules for the identified mutual DEG based on the comprehensive analysis. These approaches may contribute to understanding the molecular intricacies of COVID-19 in diabetic peripheral neuropathy patients through insights into potential therapeutic interventions.

Advances in Inflammatory Bowel Disease Diagnostics: Machine Learning and Genomic Profiling Reveal Key Biomarkers for Early Detection.

This study, utilizing high-throughput technologies and Machine Learning (ML), has identified gene biomarkers and molecular signatures in Inflammatory Bowel Disease (IBD). We could identify significant upregulated or downregulated genes in IBD patients by comparing gene expression levels in colonic specimens from 172 IBD patients and 22 healthy individuals using the GSE75214 microarray dataset. Our ML techniques and feature selection methods revealed six Differentially Expressed Gene (DEG) biomarkers (VWF, IL1RL1, DENND2B, MMP14, NAAA, and PANK1) with strong diagnostic potential for IBD. The Random Forest (RF) model demonstrated exceptional performance, with accuracy, F1-score, and AUC values exceeding 0.98. Our findings were rigorously validated with independent datasets (GSE36807 and GSE10616), further bolstering their credibility and showing favorable performance metrics (accuracy: 0.841, F1-score: 0.734, AUC: 0.887). Our functional annotation and pathway enrichment analysis provided insights into crucial pathways associated with these dysregulated genes. DENND2B and PANK1 were identified as novel IBD biomarkers, advancing our understanding of the disease. The validation in independent cohorts enhances the reliability of these findings and underscores their potential for early detection and personalized treatment of IBD. Further exploration of these genes is necessary to fully comprehend their roles in IBD pathogenesis and develop improved diagnostic tools and therapies. This study significantly contributes to IBD research with valuable insights, potentially greatly enhancing patient care.

Integrative bioinformatics approach for identifying key genes and potential therapeutic targets in the concurrent manifestation of hypertrophic cardiomyopathy and pulmonary hypertension.

Background: Hypertrophic cardiomyopathy (HCM), identified as a primary cause of sudden cardiac death (SCD), intertwines with pulmonary hypertension (PH) to amplify cardiovascular morbidity. This complex synergy poses significant therapeutic challenges due to the absence of drugs specifically targeting their concurrent manifestation. This study seeks to unravel the molecular intricacies linking HCM and PH, aiming to lay the groundwork for targeted therapeutic interventions. Methods: Through the analysis of gene expression profiles from datasets GSE36961 (HCM) and GSE113439 (PH) within the public data repository of Gene Expression Omnibus (GEO), this research systematically identified differentially expressed genes (DEGs), conducted extensive functional annotations, and constructed detailed protein-protein interaction (PPI) networks to uncover crucial hub genes. Further, co-expression analyses, alongside drug prediction and molecular docking simulations, were employed to pinpoint potential therapeutic agents that could ameliorate the combined pathology of HCM and PH. Results: Our comprehensive analysis unearthed 79 DEGs shared between HCM and PH, highlighting fourteen as pivotal hub genes. Validation across three additional datasets (GSE35229, GSE32453, and GSE53408) from GEO accentuated secreted phosphoprotein 1 (SPP1) as a key gene of interest. Remarkably, the study identified tacrolimus, ponatinib, bosutinib, dasatinib, doxorubicin, and zanubrutinib as promising drugs for addressing the dual challenge of HCM and PH. Conclusions: The findings of this investigation shed light on the genetic underpinnings of HCM and PH's simultaneous occurrence, emphasizing the central role of SPP1 in their pathogenesis. The identification of six candidate drugs offers a hopeful vista for future therapeutic strategies targeting this complex cardiovascular interplay, marking a significant stride towards mitigating the compounded morbidity of HCM and PH. Future mechanistic and clinical studies are warranted for the investigation of this potential target and therapeutics.

Physiological and transcriptomic analyses reveal the regulatory mechanisms of Anoectochilus roxburghii in response to high-temperature stress.

BACKGROUND: High temperatures significantly affect the growth, development, and yield of plants. Anoectochilus roxburghii prefers a cool and humid environment, intolerant of high temperatures. It is necessary to enhance the heat tolerance of A. roxburghii and breed heat-tolerant varieties. Therefore, we studied the physiological indexes and transcriptome of A. roxburghii under different times of high-temperature stress treatments. RESULTS: Under high-temperature stress, proline (Pro), H2O2 content increased, then decreased, then increased again, catalase (CAT) activity increased continuously, peroxidase (POD) activity decreased rapidly, then increased, then decreased again, superoxide dismutase (SOD) activity, malondialdehyde (MDA), and soluble sugars (SS) content all decreased, then increased, and chlorophyll and soluble proteins (SP) content increased, then decreased. Transcriptomic investigation indicated that a total of 2740 DEGs were identified and numerous DEGs were notably enriched for "Plant-pathogen interaction" and "Plant hormone signal transduction". We identified a total of 32 genes in these two pathways that may be the key genes for resistance to high-temperature stress in A. roxburghii. CONCLUSIONS: To sum up, the results of this study provide a reference for the molecular regulation of A. roxburghii's tolerance to high temperatures, which is useful for further cultivation of high-temperature-tolerant A. roxburghii varieties.

Comparative transcriptome profile of embryos at different developmental stages derived from somatic cell nuclear transfer (SCNT) and in-vitro fertilization (IVF) in riverine buffalo (Bubalus bubalis).

Somatic cell nuclear transfer (SCNT) is a very important reproductive technology with many diverse applications, such as fast multiplication of elite animals, the production of transgenic animals and embryonic stem (ES) cells. However, low cloning efficiency, a low live birth rate and the abnormally high incidence of abnormalities in the offspring born are attributed to incomplete or aberrant nuclear reprogramming. In SCNT embryos, the aberrant expression pattern of the genes throughout embryonic development is responsible for the incomplete nuclear reprogramming. The present study was carried out to identify the differential gene expression (DEGs) profile and molecular pathways of the SCNT and IVF embryos at different developmental stages (2 cell, 8 cell and blastocyst stages). In the present study, 1164 (2 cell), 1004 (8 cell) and 530 (blastocyst stage) DEGs were identified in the SCNT embryos as compared to IVF embryos. In addition, several genes such as ZEB1, GDF1, HSF5, PDE3B, VIM, TNNC, HSD3B1, TAGLN, ITGA4 and AGMAT were affecting the development of SCNT embryos as compared to IVF embryos. Further, Gene Ontology (GO) and molecular pathways analysis suggested, SCNT embryos exhibit variations compared to their IVF counterparts and affected the development of embryos throughout the different developmental stages. Apart from this, q-PCR analysis of the GDF1, TMEM114, and IGSF22 genes were utilized to validate the RNA-seq data. These findings contribute valuable insights about the different genes and molecular pathways underlying SCNT embryo development and offer crucial information for improving SCNT efficiency.

Screening and identification of hub genes for ischemic cardiomyopathy and construction and validation of a clinical prognosis model using bioinformatics analysis.

Background: Myocardial ischemia and hypoxia may result in myocardial cell necrosis, scar formation, and hyperplasia. We aim to explore the differentially expressed genes (DEGs) in ischemic cardiomyopathy (ICM), construct and identify a clinical prognosis model using bioinformatics methods, so as to screen potential biomarkers of ICM to provide a basis for the early diagnosis and treatment of ICM. Methods: Based on the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) database, R language was used to screen DEGs in healthy myocardial (n=5) and ICM myocardial tissues (n=12). DEGs were analyzed by Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and protein-protein interaction (PPI). Receiver operating characteristic (ROC) curves were drawn to verify the target genes. Results: A total of 259 genes with significantly changed fold change (FC) values were obtained through conditional screening, including up-regulated genes and down-regulated genes. The first two hub genes [interleukin-6 (IL-6) and Ras homologous gene family member A (RHOA)] with the largest degree value among the above up-regulated and down-regulated genes were selected and their expression values were combined in the gene chip to draw the ROC curve based on the pROC package of R language. The area under the ROC curve (AUC) values of IL-6 and RHOA were 0.956 and 0.995, respectively. The expression levels of Sqstm1, Nos2, IL-6, RHOA, and Zfp36 genes in the ICM group are lower than those in the blank control group and the difference was statistically significant (P<0.05). RHOA and Stat3 were identified as the key genes controlling the occurrence and development of ICM. Conclusions: ICM is closely related to the changes of extracellular matrix (ECM) and oxidoreductase activity. The IL-6 and RHOA are expected to become potential targets for ICM treatment.

Identification of the Hub Genes Involved in Chikungunya Viral Infection.

Background Chikungunya virus (CHIKV) infection poses a significant global health threat, necessitating a deeper understanding of its molecular mechanisms for effective management and treatment. This study aimed to understand the molecular and genetic mechanisms of CHIKV infection by analyzing microarray expression data. Methodology National Center for Biotechnology Information (NCBI) GEO2R with an adjusted p-value cut-off of <0.05 and |log2FC ≥ 1.5| was used to identify the differentially expressed genes involved in CHIKV infection using microarray data from the Gene Expression Omnibus (GEO) database, followed by enrichment analysis, protein-protein interaction (PPI) network construction, and, finally, hub gene identification. Results Analysis of the microarray dataset revealed 25 highly significant differentially expressed genes (DEGs), including 21 upregulated and four downregulated genes. PPI network analysis elucidated interactions among these DEGs, with hub genes such as ACTB and CTNNB1 exhibiting central roles. Enrichment analysis identified crucial pathways, including leukocyte transendothelial migration, regulation of actin cytoskeleton, and thyroid hormone signaling, implicating their involvement in CHIKV infection. Furthermore, the study highlights potential therapeutic targets such as ACTB and CTNNB1, which showed significant upregulation in infected cells. Conclusions These findings underscore the complex interplay between viral infection and host cellular processes, shedding light on novel avenues for diagnostic marker discovery and advancing antiviral strategies. In this study, we shed light on the molecular and genetic mechanisms of CHIKV infection and the potential role of ACTB and CTNNB1 genes.

The Construction of a Multi-Gene Risk Model for Colon Cancer Prognosis and Drug Treatments Prediction.

In clinical practice, colon cancer is a prevalent malignant tumor of the digestive system, characterized by a complex and progressive process involving multiple genes and molecular pathways. Historically, research efforts have primarily focused on investigating individual genes; however, our current study aims to explore the collective impact of multiple genes on colon cancer and to identify potential therapeutic targets associated with these genes. For this research, we acquired the gene expression profiles and RNA sequencing data of colon cancer from TCGA. Subsequently, we conducted differential gene expression analysis using R, followed by GO and KEGG pathway enrichment analyses. To construct a protein-protein interaction (PPI) network, we selected survival-related genes using the log-rank test and single-factor Cox regression analysis. Additionally, we performed LASSO regression analysis, immune infiltration analysis, mutation analysis, and cMAP analysis, as well as an investigation into ferroptosis. Our differential expression and survival analyses identified 47 hub genes, and subsequent LASSO regression analysis refined the focus to 23 key genes. These genes are closely linked to cancer metastasis, proliferation, apoptosis, cell cycle regulation, signal transduction, cancer microenvironment, immunotherapy, and neurodevelopment. Overall, the hub genes discovered in our study are pivotal in colon cancer and are anticipated to serve as important biological markers for the diagnosis and treatment of the disease.

Boosting edgeR (Robust) by dealing with missing observations and gene-specific outliers in RNA-Seq profiles and its application to explore biomarker genes for diagnosis and therapies of ovarian cancer.

The edgeR (Robust) is a popular approach for identifying differentially expressed genes (DEGs) from RNA-Seq profiles. However, it shows weak performance against gene-specific outliers and is unable to handle missing observations. To address these issues, we proposed a pre-processing approach of RNA-Seq count data by combining the iLOO-based outlier detection and random forest-based missing imputation approach for boosting the performance of edgeR (Robust). Both simulation and real RNA-Seq count data analysis results showed that the proposed edgeR (Robust) outperformed than the conventional edgeR (Robust). To investigate the effectiveness of identified DEGs for diagnosis, and therapies of ovarian cancer (OC), we selected top-ranked 12 DEGs (IL6, XCL1, CXCL8, C1QC, C1QB, SNAI2, TYROBP, COL1A2, SNAP25, NTS, CXCL2, and AGT) and suggested hub-DEGs guided top-ranked 10 candidate drug-molecules for the treatment against OC. Hence, our proposed procedure might be an effective computational tool for exploring potential DEGs from RNA-Seq profiles for diagnosis and therapies of any disease.

Systems and Computational Screening identifies SRC and NKIRAS2 as Baseline Correlates of Risk (CoR) for Live Attenuated Oral Typhoid Vaccine (TY21a) associated Protection.

AIM: We investigated the molecular underpinnings of variation in immune responses to the live attenuated typhoid vaccine (Ty21a) by analyzing the baseline immunological profile. We utilized gene expression datasets obtained from the Gene Expression Omnibus (GEO) database (accession number: GSE100665) before and after immunization. We then employed two distinct computational approaches to identify potential baseline biomarkers associated with responsiveness to the Ty21a vaccine. MAIN METHODS: The first pipeline (knowledge-based) involved the retrieval of differentially expressed genes (DEGs), functional enrichment analysis, protein-protein interaction network construction, and topological network analysis of post-immunization datasets before gauging their pre-vaccination expression levels. The second pipeline utilized an unsupervised machine learning algorithm for data-driven feature selection on pre-immunization datasets. Supervised machine-learning classifiers were employed to computationally validate the identified biomarkers. KEY FINDINGS: Baseline activation of NKIRAS2 (a negative regulator of NF-kB signalling) and SRC (an adaptor for immune receptor activation) was negatively associated with Ty21a vaccine responsiveness, whereas LOC100134365 exhibited a positive association. The Stochastic Gradient Descent (SGD) algorithm accurately distinguished vaccine responders and non-responders, with 88.8%, 70.3%, and 85.1% accuracy for the three identified genes, respectively. SIGNIFICANCE: This dual-pronged novel analytical approach provides a comprehensive comparison between knowledge-based and data-driven methods for the prediction of baseline biomarkers associated with Ty21a vaccine responsiveness. The identified genes shed light on the intricate molecular mechanisms that influence vaccine efficacy from the host perspective while pushing the needle further towards the need for development of precise enteric vaccines and on the importance of pre-immunization screening.

Identification and sex expression profiles of candidate chemosensory genes from Atherigona orientalis via the antennae and leg transcriptome analysis.

Atherigona orientalis Schiner (1868) is an acknowledged agricultural pest owing to its feeding habits and breeding locations. This insect is a tropical and subtropical pest in fruits and vegetables, in which >50 varieties of fruits and vegetables in 26 families, such as Capsicum annuum, Lycopersicon esculentum, and Cucumis melo have been attacked. Moreover, A. orientalis may also develop in rotten crops and feces or insect carcasses, which are also considered one kind of sanitary pest and medical insect. At present, the invasion ranges of A. orientalis are still increasing and more preventive and management measures are to be processed. To gain a better understanding of the molecular mechanisms involved in olfactory reception in A. orientalis, the transcriptome of male and female antennae and legs was systematically analyzed. In total, 131 chemosensory-related genes, including 63 odorant receptors (ORs), 20 gustatory receptors (GRs), 18 ionotropic receptors (IRs), 27 odorant binding proteins (OBPs), 1 chemosensory protein (CSP), and 2 sensory neuron membrane proteins (SNMPs), were identified. The analysis focused on obtaining expression information of candidate olfactory genes at the transcriptomic level by examining the differentially expressed genes (DEGs) in all samples. Totally, 41 DEGs were identified between male antennae (MA) and female antennae (FA), including 32 ORs, 5 OBPs, 1 IR, 2 GRs and 1 SNMP. In MA versus male legs (ML), 78 DEGs were identified (45 ORs, 18 OBPs, 6 GRs, 6 IRs, 1 CSP and 2 SNMPs). In FA and female legs (FL), 96 DEGs were identified (51 ORs, 21 OBPs, 9 GRs, 12 IRs, 1 CSP and 2 SNMPs). For ML and FL, 3 DEGs were identified, including 2 ORs and 1 SNMP. Our results supplement valuable insights for future research on the chemoreception mechanisms in A. orientalis.

Bioinformatics identification of characteristic genes of cervical cancer via an artificial neural network.

BACKGROUND: Artificial neural networks (ANNs) have been extensively used in the field of medicine. The present hypothesis-free study sought to use an ANN to identify the characteristic genes of cervical cancer (CC). METHODS: RNA sequencing profiles were obtained from the GSE7410, GSE9750, GSE63514, and GSE52903 datasets. The differentially expressed genes (DEGs) were identified and compared between the normal and CC tissues. An ANN analysis was conducted to obtain the random-forest tree and to examine differences in gene filtering. A neural network model was established using the characteristic genes of CC, while the verification accuracy of the model was examined by Cox regression. The differences in the immune infiltrating cells between the normal cervical and CC tissues were compared by CIBERSORT (an analytical tool can provide an estimation of the abundances of member cell types in a mixed cell population). RESULTS: Nine genes' characteristics for CC were identified: cyclin-dependent kinase inhibitor 2A (CDKN2A), chromosome 1 open reading frame 112 (C1orf112), helicase, lymphoid-specific (HELLS), mini-chromosome maintenance protein 5 (MCM5), mini-chromosome maintenance protein 2 (MCM2), kinetochore associated 1 (KNTC1), cysteine-rich secretory protein 3 (CRISP3), phytanoyl-CoA 2-hydroxylase interacting protein (PHYHIP), and cornulin (CRNN). CONCLUSIONS: ANN is a robust neural network model that can be used to potentially predict CC based on the gene score. It can provide novel insights into the pathogenesis and molecular mechanisms of CC.

Blue-light irradiation induced partial nitrification.

The role of ray radiation from the sunlight acting on organisms has long-term been investigated. However, how the light with different wavelengths affects nitrification and the involved nitrifiers are still elusive. Here, we found more than 60 % of differentially expressed genes (DEGs) in nitrifiers were observed under irradiation of blue light with wavelengths of 440-480 nm, which were 13.4 % and 20.3 % under red light and white light irradiation respectively. Blue light was more helpful to achieve partial nitrification rather than white light or red light, where ammonium oxidization by ammonia-oxidizing archaea (AOA) with the increased relative abundance from 8.6 % to 14.2 % played a vital role. This was further evidenced by the enhanced TCA cycle, reactive oxygen species (ROS) scavenge and DNA repair capacity in AOA under blue-light irradiation. In contrast, nitrite-oxidizing bacteria (NOB) was inhibited severely to achieve partial nitrification, and the newly discovered encoded blue light photoreceptor proteins made them more sensitive to blue light and hindered cell activity. Ammonia-oxidizing bacteria (AOB) expressed genes for DNA repair capacity under blue-light irradiation, which ensured their tiny impact by light irradiation. This study provided valuable insights into the photosensitivity mechanism of nitrifiers and shed light on the diverse regulatory by light with different radiation wavelengths in artificial systems, broadening our comprehension of the nitrogen cycle on earth.

Elucidating the role of TWIST1 in ulcerative colitis: a comprehensive bioinformatics and machine learning approach.

Background: Ulcerative colitis (UC) is a common and progressive inflammatory bowel disease primarily affecting the colon and rectum. Prolonged inflammation can lead to colitis-associated colorectal cancer (CAC). While the exact cause of UC remains unknown, this study aims to investigate the role of the TWIST1 gene in UC. Methods: Second-generation sequencing data from adult UC patients were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified, and characteristic genes were selected using machine learning and Lasso regression. The Receiver Operating Characteristic (ROC) curve assessed TWIST1's potential as a diagnostic factor (AUC score). Enriched pathways were analyzed, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Variation Analysis (GSVA). Functional mechanisms of marker genes were predicted, considering immune cell infiltration and the competing endogenous RNA (ceRNA) network. Results: We found 530 DEGs, with 341 upregulated and 189 downregulated genes. TWIST1 emerged as one of four potential UC biomarkers via machine learning. TWIST1 expression significantly differed in two datasets, GSE193677 and GSE83687, suggesting its diagnostic potential (AUC = 0.717 in GSE193677, AUC = 0.897 in GSE83687). Enrichment analysis indicated DEGs associated with TWIST1 were involved in processes like leukocyte migration, humoral immune response, and cell chemotaxis. Immune cell infiltration analysis revealed higher rates of M0 macrophages and resting NK cells in the high TWIST1 expression group, while TWIST1 expression correlated positively with M2 macrophages and resting NK cell infiltration. We constructed a ceRNA regulatory network involving 1 mRNA, 7 miRNAs, and 32 long non-coding RNAs (lncRNAs) to explore TWIST1's regulatory mechanism. Conclusion: TWIST1 plays a significant role in UC and has potential as a diagnostic marker. This study sheds light on UC's molecular mechanisms and underscores TWIST1's importance in its progression. Further research is needed to validate these findings in diverse populations and investigate TWIST1 as a therapeutic target in UC.