Gene Functional Enrichment Research Articles

Gene expression analysis reveals mir-29 as a linker regulatory molecule among rheumatoid arthritis, inflammatory bowel disease, and dementia: Insights from systems biology approach

Background Rheumatoid arthritis (RA) is a degenerative autoimmune disease, often managed through symptomatic treatment. The co-occurrence of the reported extra-articular comorbidities such as inflammatory bowel disease (IBD), and dementia may complicate the pathology of the disease as well as the treatment strategies. Therefore, in our study, we aim to elucidate the key genes, and regulatory elements implicated in the progression and association of these diseases, thereby highlighting the linked potential therapeutic targets. Methodology Ten microarray datasets each for RA, and IBD, and nine datasets for dementia were obtained from Gene Expression Omnibus. We identified common differentially expressed genes (DEGs) and constructed a gene-gene interaction network. Subsequently, topology analysis for hub gene identification, cluster and functional enrichment, and regulatory network analysis were performed. The hub genes were then validated using independent microarray datasets from Gene Expression Omnibus. Results A total of 198 common DEGs were identified from which CD44, FN1, IGF1, COL1A2, and POSTN were identified as the hub genes in our study. These hub genes were mostly enriched in significant processes and pathways like tissue development, collagen binding, cell adhesion, regulation of ERK1/2 cascade, PI3K-AKT signaling, and cell surface receptor signaling. Key transcription factors TWIST2, CEBPA, EP300, HDAC1, HDAC2, NFKB1, RELA, TWIST1, and YY1 along with the miRNA hsa-miR-29 were found to regulate the expression of the hub genes significantly. Among these regulatory molecules, miR-29 emerged as a significant linker molecule, bridging the molecular mechanisms of RA, IBD, and dementia. Validation of our hub genes demonstrated a similar expression trend in the independent datasets used for our study. Conclusion Our study underscores the significant role of miR-29 in modulating the expression of hub genes and the associated transcription factors, which are crucial in the comorbidity status of RA, dementia, and IBD. This regulatory mechanism highlights miR-29 as a key player in the pathogenesis of these comorbid diseases.

The systematic analysis of genes related to butyrate metabolism suggests that CDKN3 could serve as a promising therapeutic target for lung adenocarcinoma treatment

BackgroundMetabolic pathways are known to significantly impact the development and advancement of lung cancer. This study sought to establish a signature related to butyrate metabolism that is specifically linked to lung adenocarcinoma (LUAD).MethodsFor the purpose of identifying butyrate metabolism-related differentially expressed genes (BMR-DEGs) in the TCGA-LUAD dataset, we introduced transcriptome data. This was followed by the implementation of the univariate Cox and LASSO analyses in order to construct a LUAD gene signature. We performed a comprehensive analysis of gene function enrichment between the two populations at risk, thoroughly examined their immune microenvironment characteristics, and assessed the effectiveness of immunotherapy. Finally, the function of CDKN3 in LUAD was verified by in vitro experiments.ResultsThrough a comprehensive analysis of the TCGA-LUAD dataset, 51 significant BMR-DEGs were confirmed. Subsequently, five characteristic genes, CPS1, ABCC2, CDKN3, SLC2A1, and IGFBP1 were identified to create prognostic features for butyrate metabolism related outcomes in LUAD. Cox regression analysis determined that the pathological T stage, tumor stage, and RiskScore could serve as independent prognostic indicators. Analysis of the abundance of 22 immune infiltrating cells revealed that 15 immune cell types exhibited substantial differences and were strongly associated with risk ratings and prognosis. An important correlation exists between risk ratings and immunological checkpoints, which can be utilized to forecast the efficacy of treatment. In the high-risk group, there was an upregulation of the expression of PD-L2, PD-L1, and PD-1. Additionally, the risk score showed a positive correlation with TIDE and Exclusion score, while showing a negative correlation with Dysfunction score. Furthermore, the IC50 values for cisplatin, paclitaxel, and docetaxel were notably elevated in the high-risk group, indicating that these medications could potentially provide therapeutic advantages for this particular group. Finally, we determined that knockdown CDKN3 inhibited the proliferation and metastasis of LUAD cells.ConclusionWe identify and validate a novel BMR-related prognostic signature comprising 5 DEGs for LUAD patients. Our data might provide a new molecular target for LUAD treatment.

Cuproptosis related lncRNA signature as a prognostic and therapeutic biomarker in osteosarcoma immunity

Osteosarcoma is one of the most common malignant bone tumours in children. In this study, we aimed to construct a cuproptosis-related lncRNAs signature to predict the prognosis and immune landscape of osteosarcoma patients. Databases from TARGET were used to acquire osteosarcoma patient datasets, which included clinical information and RNA sequencing data. Cuproptosis-related lncRNAs was obtained by correlation analysis. Through univariate Cox regression analysis, prognosis-related lncRNAs were obtained. We used nonnegative matrix factorization clustering to identify potential molecular subgroups with different cuproptosis-related lncRNA expression patterns. The least absolute shrinkage and selection operator algorithm and multivariate Cox regression analysis were used to construct the prognostic signature. The ESTIMATE algorithm, Gene Ontology and Kyoto Encyclopaedia of Genes and Genomes were applied to explore the underlying mechanisms in the immune landscape of osteosarcoma. We used gene set enrichment analysis to compare the different enrichments in the high-risk group and the low-risk group. Furthermore, we predicted the response to targeted drugs in patients with different risk groups. Using multivariable analysis, we developed a risk scoring model based on 7 long noncoding RNAs and calculated two molecular subgroups from osteosarcoma patients from the database. There is a better immune microenvironment in the low-risk group compared to the high-risk group. At the same time, the gene functional enrichment analysis based on the differently expressed genes obtained by grouping showed they were mainly related to immunity, indicating that cuproptosis-related lncRNAs may affect the prognosis of osteosarcoma by regulating immunity. Moreover, these patients in high-risk group were more susceptible to targeted drugs than the low-risk group. We identified a cuproptosis-related lncRNA prognostic signature for osteosarcoma and showed a close connection in terms of immunity. Moreover, we provided some potential targeted drugs for the treatment of osteosarcoma.

Identification of CENPM as a key gene driving adrenocortical carcinoma metastasis via physical interaction with immune checkpoint ligand FGL1.

Distant metastasis occurs in the majority of adrenocortical carcinoma (ACC), leading to an extremely poor prognosis. However, the key genes driving ACC metastasis remain unclear. Weighted gene co-expression network analysis (WGCNA) and functional enrichment analysis were conducted to identify ACC metastasis-related genes. Data from RNA-seq and microarray were analyzed to reveal correlations of the CENPM gene with cancer, metastasis, and survival in ACC. Immunohistochemistry was used to assess CENPM protein expression. The impact of CENPM on metastasis behaviour was verified in ACC (H295R and SW-13) cells and xenograft NPG mice. DIA quantitative proteomics analysis, western blot, immunofluorescence, and co-immunoprecipitation assay were performed to identify the downstream target of CENPM. Among the 12035 analyzed genes, 363 genes were related to ACC metastasis and CENPM was identified as the hub gene. CENPM was upregulated in ACC samples and associated with metastasis and poor prognosis. Knockdown of CENPM inhibited proliferation, invasion, and migration of ACC cells and suppressed liver metastasis in xenograft NPG mice. Collagen-containing extracellular matrix signalling was primarily downregulated when CENPM was knocked down. FGL1, important components of ECM signalling and immune checkpoint ligand of LAG3, were downregulated following CENPM silence, overexpressed in human advanced ACC samples, and colocalized with CENPM. Physical interaction between CENPM and FGL1 was identified. Overexpression of FGL1 rescued migration and invasion of CENPM knockdown ACC cells. CENPM is a key gene in driving ACC metastasis. CENPM promotes ACC metastasis through physical interaction with the immune checkpoint ligand FGL1. CENPM can be used as a new prognostic biomarker and therapeutic target for metastatic ACC. CENPM is the key gene that drives ACC metastasis, and a robust biomarker for ACC prognosis. Silencing CENPM impedes ACC metastasis in vitro and in vivo by physical interaction with immune checkpoint ligand FGL1. FGL1 is overexpressed in ACC and promotes ACC metastasis.

Whole-Transcriptome Analysis Reveals the Regulatory Network of Immune Response in Dapulian Pig.

There is a consensus that indigenous pigs in China are more resistant than modern commercial pigs in terms of disease resistance. Generally, the immune response is an important part of anti-disease capability; however, the related mechanism in pigs is largely puzzling. Here, the public transcriptome data of peripheral blood mononuclear cells (PBMCs) from Dapulian (Chinese local breed) and Landrace (Commercial breed) pigs after stimulation with polyinosinic-polycytidylic acid (poly I:C, a conventional reagent used for simulation of the viral infection) were reanalyzed, and the immune response mechanism in different pig breeds was investigated from a transcriptomic perspective. Of note, through comparative analyses of Dapulian and Landrace pigs, the candidate genes involved in swine broad-spectrum resistance were identified, such as TIMD4, RNF128 and VCAM1. In addition, after differential gene expression, target gene identification and functional enrichment analyses, a potential regulatory network of miRNA genes associated with immune response was obtained in Dapulian pigs, including five miRNAs and 12 genes (such as ssc-miR-181a, ssc-miR-486, IL1R1 and NFKB2). This work provides new insights into the immune response regulation of antiviral responses in indigenous and modern commercial pigs.

MiR-142-3p Regulates Airway Inflammation Through PTEN/AKT in Children and Mice with Asthma

ABSTRACT Background Asthma is the most common chronic pulmonary disease in children. MicroRNAs (miRNAs) play a regulatory role in the occurrence and development of asthma. We aimed to explore the differential expression of miRNAs in the peripheral blood of children with asthma and identify a miRNA that can alleviate asthma inflammation. Methods We used high-throughput sequencing to analyze differences in peripheral blood miRNA between children with acute asthma and healthy children, followed by target gene prediction and functional enrichment analysis. We inhibited miR-142-3p’s expression in asthmatic mice to observe asthma symptoms. Inflammatory changes in lung tissue were assessed using hematoxylin and eosin staining and ELISA. Subsequently, the target gene of miR-142-3p was identified through a dual-luciferase reporter assay, and PTEN and AKT expression levels in mice lung tissue were determined using qPCR and western blot. Results Fifty one differentially expressed miRNAs were identified. Inhibition of miR-142-3p expression in asthmatic mice reversed the downregulation of PTEN and activation of AKT in lung tissue, while also significantly alleviating symptoms and pulmonary inflammation in the asthmatic mice. Conclusion miRNAs were differentially expressed in the peripheral blood of children with asthma. miR-142-3p regulates airway inflammation via the PTEN/AKT pathway.

Genome-wide methylome-based molecular pathologies associated with depression and suicide.

Major depressive disorder (MDD) is a debilitating disorder. Suicide attempts are 5-times higher in MDD patients than in the general population. Interestingly, not all MDD patients develop suicidal thoughts or complete suicide. Thus, it is important to study the risk factors that can distinguish suicidality among MDD patients. The present study examined if DNA methylation changes can distinguish suicidal behavior among depressed subjects. Genome-wide DNA methylation was examined in the dorsolateral prefrontal cortex of depressed suicide (MDD+S; n = 15), depressed non-suicide (MDD-S; n = 17), and nonpsychiatric control (C; n = 16) subjects using 850 K Infinium Methylation EPIC BeadChip. The significantly differentially methylated genes were used to determine the functional enrichment of genes for ontological clustering and pathway analysis. Based on the number of CpG content and their relative distribution from specific landmark regions of genes, 32,958 methylation sites were identified across 12,574 genes in C vs. MDD+/-S subjects, 30,852 methylation sites across 12,019 genes in C vs. MDD-S, 41,648 methylation sites across 13,941 genes in C vs. MDD+S, and 49,848 methylation sites across 15,015 genes in MDD-S vs. MDD+S groups. A comparison of methylation sites showed 33,129 unique methylation sites and 5451 genes in the MDD-S group compared to the MDD+S group. Functional analysis suggested oxytocin, GABA, VGFA, TNFA, and mTOR pathways associated with suicide in the MDD group. Altogether, our data show a distinct pattern of DNA methylation, the genomic distribution of differentially methylated sites, gene enrichment, and pathways in MDD suicide compared to non-suicide MDD subjects.

SNRPB and CEP290, predicting the prognosis of diffuse large B cell lymphoma and associated with tumour immune microenvironment

Background Diffuse large B-cell lymphoma (DLBCL), the most prevalent type of non-Hodgkin’s lymphoma, exhibits significant correlations with efferocytosis-related molecules (ERMs) concerning invasion, metastasis, and clinical outcomes. This study aims to establish an efferocytosis-related gene signature specifically linked to DLBCL. Methods Key module genes linked to DLBCL were identified via weighted gene co-expression network analysis (WGCNA) in GSE32018. Univariate Cox analysis of GSE31312 revealed ERMs associated with DLBCL survival. Differential expression analysis identified differentially expressed genes (DEGs) between DLBCL subtypes and normal samples. Venn diagram analysis identified common DEGs and key module genes. A DLBCL gene signature was built by using univariate Cox and least absolute shrinkage and selection operator (LASSO) analysis. Gene functional enrichment, immune microenvironment, and immunotherapy analyses compared two risk subgroups. Prognostic gene expression was validated at the single-cell level. Results In the GSE32018 dataset, 1760 key module genes related to DLBCL were identified. Using GSE31312, 14 ERMs associated with DLBCL prognosis were determined.Then, an ERMs-related prognostic signature, including small nuclear ribonucleoprotein polypeptides B (SNRPB) and centrosomal protein 290 (CEP290), was established. Independent prognostic analysis showed that the RiskScore derived from this signature was a prognostic factor. Significant immune microenvironment differences were observed between two risk subgroups. Additionally, chemotherapeutic drug sensitivity results indicated the signature could predict therapeutic response. Eventually, expression of SNRPB and CEP290 was confirmed in B cells. Conclusion The prognostic signature comprised of SNRPB and CEP290 based on ERMs-DEGs was established, providing a theoretical basis and reference value for DLBCL research.

Investigation of the correlation between AGRN expression and perineural invasion in colon cancer.

Colon cancer is one of the most common gastrointestinal malignancies. According to the traditional view, the primary modes of transmission include direct dissemination, hematogenous metastasis, and lymph node metastasis. In recent years, the role of perineural invasion (PNI) in the spread and metastasis of tumors has received immense attention. However, there are still relatively few reports on the potential mechanisms and biomarkers of PNI occurrence and development in colon cancer. We identified genes linked to the onset and progression of PNI in colon cancer using bioinformatics tools and extensive databases. Gene function enrichment analysis was used to explore the potential roles of these genes in tumor proliferation, invasion, and PNI. A collection of postoperative pathological specimens from colon cancer patients who underwent surgery, related clinicopathological data, and immunohistochemistry were used to validate AGRN expression in PNI tissues. Bioinformatics analysis revealed that AGRN is overexpressed in colon cancer tissues and correlates with poor patient prognosis. The findings from gene association and enrichment studies indicate that AGRN and its associated genes may play a role in PNI development and progression in colon cancer by simultaneously enhancing tumor cell invasion and neural cell growth. Immunohistochemical analysis of clinical samples confirmed that AGRN expression is elevated in colon cancer tissues with PNI. We found that AGRN is significantly overexpressed in colon cancer tissues exhibiting PNI and is linked to poor patient survival. AGRN and its related genes may contribute to PNI by promoting tumor cell invasion and neural cell growth. Hence, AGRN may play a crucial role in the initiation and progression of PNI in colon cancer.

Multi-omics joint screening of biomarkers related to M2 macrophages in gastric cancer

BackgroundDue to high mortality rate and limited treatments in gastric cancer (GC), call for deeper exploration of M2 macrophages as biomarkers is needed.MethodsThe data for this study were obtained from the Gene Expression Omnibus (GEO) and Genomic Data Commons (GDC). The Seurat package was utilized for single-cell RNA sequencing (scRNA-seq) analysis. FindAllMarkers was used to identify genes highly expressed among different cell subsets. DESeq2 package was leveraged to screen differentially expressed genes (DEGs), while limma package was utilized for identifying differentially expressed proteins (DEPs). Enrichment analyses of the genes were conducted using KOBAS-i database. MultipleROC was applied to evaluate the diagnostic potential of biomarkers, and rms package was utilized to construct diagnostic models. hTFtarget database was utilized to predict potential transcription factors (TFs). Finally, cell-based assays were performed to validate the expression and potential biological functions of the screened key markers.ResultsThis study found that M2 macrophages were enriched in protein, endoplasmic reticulum, and virus-related pathways. A total of 4146 DEGs and 1946 DEPs were obtained through screening, with 254 common DEGs/DEPs. The results of gene function enrichment analysis suggested that it may affect the occurrence and development of GC through DNA replication and cell cycle. This study identified three biomarkers, HSPH1, HSPD1, and IFI30, and constructed a diagnostic model based on these three genes. The AUC value greater than 0.8 proved the reliability of the model. Through screening TFs, SPI1 and KLF5 were found to be the common TFs for the three biomarkers. The expression of the three genes IFI30, HSPD1 and HSPH1 was up-regulated in GC cells, and IFI30 may play a facilitating role in the migration and invasion of GC cells.ConclusionThis study identified three biomarkers and constructed a diagnostic model, providing a new perspective for the research and treatment of GC.

The Rice Online Expression Profiles Array Database Version 2 (ROADv2): An Interactive Atlas for Rice Functional Genomics

The Rice Online expression profiles Array Database version 2 (ROADv2; https://roadv2.khu.ac.kr), an enhanced database for rice gene expression analysis, transitions from the previous microarray platforms to RNA-Seq data for improved accuracy. It encompasses 328 datasets from diverse experimental series, including anatomy, abiotic and biotic stress, hormone response, and nutrient starvation. Key updates include gene annotation (upgraded to RGAP version 7) and functional enrichment data (utilizing recent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) versions). ROADv2 debuts protein–protein interaction (PPI) network analysis and broadens interactive visualization across all features. Gene expression data are segmented into anatomy, biotic, abiotic, nutrient, and hormone categories, with user-interactive heatmaps displaying normalized log2 expression and log2 fold change data. Coexpression correlation analysis identifies genes with similar patterns, visualized through interactive network graphs. Functional enrichment tools display GO and KEGG analyses with significant terms emphasized in various formats. PPI network analysis integrates coexpression data to enhance prediction accuracy. Validation studies affirm the database’s reliability, demonstrating reproducible tissue/organ-specific expression patterns. ROADv2 provides a comprehensive resource for rice functional genomics studies.

Identification of Ferroptosis-Related Gene in Age-Related Macular Degeneration Using Machine Learning.

Age-related macular degeneration (AMD) is a major cause of irreversible visual impairment, with dry AMD being the most prevalent form. Programmed cell death of retinal pigment epithelium (RPE) cells is a central mechanism in the pathogenesis of dry AMD. Ferroptosis, a recently identified form of programmed cell death, is characterized by iron accumulation-induced lipid peroxidation. This study aimed to investigate the involvement of ferroptosis in the progression of AMD. A total of 41 samples of AMD and 50 normal samples were obtained from the data set GSE29801 for differential gene expression analysis and functional enrichment. Differentially expressed genes (DEGs) were selected and intersected with genes from the ferroptosis database to obtain differentially expressed ferroptosis-associated genes (DEFGs). Machine learning algorithms were employed to screen diagnostic genes. The diagnostic genes were subjected to Gene Set Enrichment Analysis (GSEA). Expression differences of diagnostic genes were validated in in vivo and in vitro models. We identified 462 DEGs when comparing normal and AMD samples. The GO enrichment analysis indicated significant involvement in key biological processes like collagen-containing extracellular matrix composition, positive cell adhesion regulation, and extracellular matrix organization. Through the intersection with ferroptosis gene sets, we pinpointed 10 DEFGs. Leveraging machine learning algorithms, we pinpointed five ferroptosis feature diagnostic genes: VEGFA, SLC2A1, HAMP, HSPB1, and FADS2. The subsequent experiments validated the increased expression of SLC2A1 and FADS2 in the AMD ferroptosis model. The occurrence of ferroptosis could potentially contribute to the advancement of AMD. SLC2A1 and FADS2 have demonstrated promise as emerging diagnostic biomarkers and plausible therapeutic targets for AMD.

Genetic variation analysis of Guanling cattle based on whole-genome resequencing.

The objective of this study was to unravel the genetic traits of Guanling cattle, pinpoint genes advantageous for muscle growth, and lay a foundation for the preservation of genetic diversity and further analysis of regulation mechanism of important economic traits in local cattle breed. In this study, we sequenced the whole genome of 3 Guanling cattle in Guizhou province using the Illumina HiSeq cBo sequencing platform. And, high- multiplex polymerase chain reaction technology was employed to detect high-quality single nucleotide polymorphism (SNP) sites of other 55 Guanling cattle. Our study identified 166,411 non-synonymous SNPs (nsSNPs) and 42,423 insertions and deletions (indels). Through SNP annotation, gene function enrichment analysis, and comparing with Simmental, Angus, and Limousin cattle, we identified six genes (LEPR, AKAP9, SIX4, SPIDR, PRG4, FASN) which are potentially influential on meat quality traits, playing crucial roles in muscle growth, fat metabolism, and bodily support. We also examined polymorphisms at seven SNP sites in Guanling cattle and found that all seven were in Hardy-Weinberg equilibrium. These findings suggested that these gene sites are stable and widespread in the Guanling cattle population. Our research lays the groundwork for future genetic enhancement and variety identification of Guanling cattle.

Integrated Single-Cell and Spatial Transcriptome Reveal Metabolic Gene SLC16A3 as a Key Regulator of Immune Suppression in Hepatocellular Carcinoma.

Hepatocellular carcinoma (HCC) is one of the most lethal cancers, usually diagnosed at an advanced stage. Metabolic reprogramming plays a significant role in HCC progression, probably related to immune evasion, yet the key gene is unclear. In this study, six metabolism-related genes with prognostic implications were screened. Correlation analysis between the key genes and immune cell subtypes was conducted, and a prominent gene strongly associated with immunosuppression, SLC16A3, was identified. Overexpression of SLC16A3 is associated with the loss of T-cell function and might lead to the upregulation of several immunosuppressive proteins. Gene function enrichment analysis showed genes correlated with SLC16A3 primarily involved in cell adhesion. Single-cell analysis showed that the SLC16A3 gene was mainly expressed in macrophages, especially some tumour-promoting macrophages. Further analysis of spatial transcriptome data indicated that SLC16A3 was enriched at the tumour invasion front. The mIHC revealed that patients with high SLC16A3 expression exhibited significantly reduced infiltration of GZMB+ cells. And SLC16A3 inhibitors significantly suppressed the proliferation of HCC, while simultaneously enhancing T-cell cytotoxicity and reducing exhaustion. These results reveal the phenomenon of immune escape mediated by metabolic reprogramming and suggest that SLC16A3 may serve as a novel target for intervention.

Medical Treatment of Hypercortisolism with Relacorilant: Final Results of the Phase 3 GRACE Study

Diabetic cardiomyopathy (DCM) is a cardiometabolic syndrome, manifested by ventricular diastolic or systolic dysfunction which occurs in approximately 12% of diabetic patients. Diabetes perpetuates the development of microvascular complications that can exacerbate cardiovascular pathologies. Understanding the common molecular targets underlying these two conditions is important for designing effective interventions that can address diabetes and related complications simultaneously. Herein, we aim to investigate the underlying network of key modules associated with diabetic cardiomyopathy. In current study, microarray gene expression profiles of diabetic and cardiomyopathy patients possessing accession number (GSE30122 and GSE20966) and (GSE89714, GSE3586, and GSE1145) were retrieved from Gene Expression Omnibus (GEO) database to explore key modulators. The differentially expressed genes (DEGs) were analyzed via GEO2R tool, based on the cut off criteria (p ≤ 0.05) and log2(FC)>|±1|. 37 overlapped DEGs were identified which then subjected to miRNA target prediction, protein-protein interaction (PPI) network construction and module screening via TargetScan, ShinyGO, STRING, and Cytoscape respectively, to screen hub genes as potential targets. Functional gene enrichment analysis identified enrichment of DEGs in heart contraction, apoptosis, cytokines signaling, beta adrenergic signaling and GPCR neurotransmitter receptor activity. The miRNA-mRNA regulatory network identified 9 hub genes, including ADRB1, TGFB2, RAP2A, CALD1, WIPF1, ATP10a, AKAP1, NR4A3, EXT1 and 4 hub miRNAs including miR-199-5, miR-200a, mir141-3, and miR-19-3p based on their degree of connectivity and centrality within the network. Conclusively, our analysis unveiled hub genes and miRNAs associated with diabetes-linked cardiomyopathy that might considered as biomarkers and offer a reliable tool for developing effective therapeutic strategies in future.

<b>Computational Analysis to Identify Novel Drug Targets for Esophageal Cancer</b>

Cancer is a multigene and widespread disease. Increasing drug resistance leads to the development of new therapeutic targets. Recent research indicates that various cellular components called Stress Granules (SGs) are engaged in the cancer-related signaling pathway. The phosphoinositol-3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) signaling, considered a master regulator in cancer, has been shown through genomic profiling studies to play a key role in Esophageal Cancer (EC). In this study, we performed the in-silico analysis of an RNA sequencing dataset to investigate the effects of omipalisib, a PI3K/mTOR inhibitor, on EC cell lines. Our objective was to identify novel molecular targets, particularly stress granule-related proteins, that contribute to drug resistance in EC. Using computational approaches including differential gene expression analysis, pathway analysis, and functional enrichment, we examined the transcriptomic changes in response to omipalisib treatment. Our analysis revealed downregulation of the PI3K/mTOR signaling pathway and upregulation of compensatory pathways such as FOXO and JAK-STAT signaling in response to omipalisib. Notably, we identified 16 stress granule-related proteins that were significantly upregulated, suggesting their potential role in drug resistance mechanisms. These findings provide new insights into the molecular mechanisms underlying drug resistance in EC and highlight potential novel targets for therapeutic intervention. Currently, EC is limited by the number of potential drugs for treatment, poor prognosis, and is prone to chemotherapeutic resistance to existing clinically proven drugs. Our computational analysis offers valuable insights into targeting stress granules for cancer drug discovery, potentially enhancing the development of new therapeutic strategies for EC. These results provide a strong foundation for future experimental validation and drug development efforts aimed at overcoming resistance to EC treatment. Received: 1 July 2024 | Revised: 20 September 2024 | Accepted: 20 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in the NCBI Gene Expression Omnibus (GEO) database under accession number GSE143462 at https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE143462. Author Contribution Statement Vinod Jangid: Conceptualization, Methodology, Software, Validation, Formal analysis, Data curation, Writing - original draft, Visualization. Chandrasekar Narayanan Rahul: Conceptualization, Methodology, Validation, Investigation,Writing - original draft. Aarthi Rashmi B: Validation, Writing - review & editing. Kanagaraj Sekar:Validation, Writing - review & editing, Supervision, Project administration.

Identification of gastric cancer biomarkers through in-silico analysis of microarray based datasets

Gastric cancer is among the most prevalent cancers worldwide including in Pakistan. Late diagnosis of gastric cancer leads to reduced survival. The present study aimed to investigate biomarkers for early diagnosis and prognosis of gastric cancer. For this purpose, the ten microarray-based gene expression datasets (GSE54129, GSE79973, GSE161533, GSE103236, GSE33651, GSE19826, GSE118916, GSE112369, GSE13911, and GSE81948) were retrieved from GEO database and analyzed by GEO2R to identify differentially expressed genes. Datasets were arranged in subsets of different dataset combinations to identify common DEGs. The gene ontology and functional pathway enrichment analysis of common DEGs was performed by DAVID tool. Pan-cancer analysis was conducted by UALCAN database. Survival analysis of common DEGs was done by Kaplan-Meier plotter. A total of 71 common DEGs were identified in different combinations of datasets. Among them, only 5 DEGs namely ATP4B, ATP4A, CCKBR, KCNJ15, and KCNJ16 were detected to be common in all the datasets. The GO and pathway analysis represented that the identified DEGs are involved in gastric acid secretion and collecting duct acid secretion pathways. Further expression validation of these five genes using three additional datasets (GSE31811, GSE26899, and GSE26272) confirmed their differential expression in gastric cancer samples. The pan-cancer analysis also revealed aberrant expression of DEGs in various cancers. The survival analysis showed the association of these 5 DEGs with poor survival of gastric cancer patients. To conclude, this study revealed a panel of 5 genes, which can be employed as diagnostic and prognostic biomarkers of gastric cancer patients.

A machine learning model revealed that exosome small RNAs may participate in the development of breast cancer through the chemokine signaling pathway

BackgroundExosome small RNAs are believed to be involved in the pathogenesis of cancer, but their role in breast cancer is still unclear. This study utilized machine learning models to screen for key exosome small RNAs and analyzed and validated them.MethodPeripheral blood samples from breast cancer screening positive and negative people were used for small RNA sequencing of plasma exosomes. The differences in the expression of small RNAs between the two groups were compared. We used machine learning algorithms to analyze small RNAs with significant differences between the two groups, fit the model through training sets, and optimize the model through testing sets. We recruited new research subjects as validation samples and used PCR-based quantitative detection to validate the key small RNAs screened by the machine learning model. Finally, target gene prediction and functional enrichment analysis were performed on these key RNAs.ResultsThe machine learning model incorporates six small RNAs: piR-36,340, piR-33,161, miR-484, miR-548ah-5p, miR-4282, and miR-6853-3p. The area under the ROC curve (AUC) of the machine learning model in the training set was 0.985 (95% CI = 0.948-1), while the AUC in the test set was 0.972 (95% CI = 0.882–0.995). RT-qPCR was used to detect the expression levels of these key small RNAs in the validation samples, and the results revealed that their expression levels were significantly different between the two groups (P < 0.05). Through target gene prediction and functional enrichment analysis, it was found that the functions of the target genes were enriched mainly in the chemokine signaling pathway.ConclusionThe combination of six plasma exosome small RNAs has good prognostic value for women with positive breast cancer by imaging screening. The chemokine signaling pathway may be involved in the early stage of breast cancer. It is worth further exploring whether small RNAs mediate chemokine signaling pathways in the pathogenesis of breast cancer through the delivery of exosomes.

Transcriptomic and Metabolomics Joint Analyses Reveal the Influence of Gene and Metabolite Expression in Blood on the Lactation Performance of Dual-Purpose Cattle (Bos taurus).

Blood is an important component for maintaining animal lives and synthesizing sugars, lipids, and proteins in organs. Revealing the relationship between genes and metabolite expression and milk somatic cell count (SCC), milk fat percentage, milk protein percentage, and lactose percentage in blood is helpful for understanding the molecular regulation mechanism of milk formation. Therefore, we separated the buffy coat and plasma from the blood of Xinjiang Brown cattle (XJBC) and Chinese Simmental cattle (CSC), which exhibit high and low SCC/milk fat percentage/milk protein percentage/lactose percentages, respectively. The expression of genes in blood and the metabolites in plasma was detected via RNA-Seq and LC-MS/MS, respectively. Based on the weighted gene coexpression network analysis (WGCNA) and functional enrichment analysis of differentially expressed genes (DEGs), we further found that the expression of genes in the blood mainly affected the SCC and milk fat percentage. Immune or inflammatory-response-related pathways were involved in the regulation of SCC, milk fat percentage, milk protein percentage, and lactose percentage. The joint analysis of the metabolome and transcriptome further indicated that, in blood, the metabolism pathways of purine, glutathione, glycerophospholipid, glycine, arginine, and proline are also associated with SCC, while lipid metabolism and amino-acid-related metabolism pathways are associated with milk fat percentage and milk protein percentage, respectively. Finally, related SCC, milk fat percentage, and milk protein percentage DEGs and DEMs were mainly identified in the blood.

Association of B cells and the risk of Esophageal cancer: a bidirectional two-sample mendelian randomization study

Background and objectivesCurrently, research on the role of B cells in esophageal cancer (EC) is limited, and existing studies on their impact are controversial. Therefore, this study was conducted to elucidate the complex causal relationship between B cells and EC, expand the understanding of esophageal cancer immunology.MethodsBidirectional two-sample Mendelian randomization (MR) was performed to assess the causal relationships between 190 B cell phenotypes and EC. To complement the MR analysis, Bayesian Weighted Mendelian Randomization (BWMR) was employed, and sensitivity analyses were conducted to evaluate the robustness of the findings. Positive results were further validated in independent cohorts of esophageal cancer studies. In addition, RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) were utilized for validation, incorporating B cell-related gene expression analysis and functional enrichment analysis to support the MR findings.ResultsIn the primary analysis, significant causal relationships were observed between 5 B cell types and the risk of EC; the onset of EC was causally linked to 3 B cell phenotypes. Validation in other cohorts revealed that 4 outcomes aligned with the primary analysis, included were CD19 on IgD + CD38-, CD20 on IgD- CD27-, CD20 on IgD- CD38br, and CD38 on PB/PC. Further validation using RNA-seq data showed that CD38 mRNA was significantly overexpressed in EC tissues, whereas CD19 and MS4A1 mRNA levels did not differ significantly between tumor and normal tissues. Functional enrichment analysis revealed that CD19, MS4A1, and CD38 are involved in multiple tumor-related immune pathways, suggesting their pivotal role in regulating the tumor immune microenvironment.ConclusionsOur study suggests a potential connection between B cell phenotypes and EC through bidirectional two-sample MR combined with BWMR analysis, providing a preliminary basis for future research.