Integrative multi-omics characterization of hepatocellular carcinoma in hispanic patients.

BACKGROUND: The incidence and mortality rates of hepatocellular carcinoma (HCC) among Hispanic individuals in the United States are much higher than in non-Hispanic white people. We conducted multi-omics analyses to elucidate molecular alterations in HCC among Hispanic patients. METHODS: Paired tumor and adjacent non-tumor samples were collected from 31 Hispanic HCCs in South Texas (STX-Hispanic) for genomic, transcriptomic, proteomic, and metabolomic profiling. Serum lipids were profiled in 40 Hispanic and non-Hispanic patients with or without clinically diagnosed HCC. RESULTS: Exome sequencing revealed high mutation frequencies of AXIN2 and CTNNB1 in STX Hispanic HCCs, suggesting a predominant activation of the Wnt/ß-catenin pathway. TERT promoter mutations were also significantly more frequent in the Hispanic cohort (Fisher's exact test, p < .05). Cell cycles and liver function were positively and negatively enriched, respectively, with gene set enrichment analysis. Gene sets representing specific liver metabolic pathways were associated with dysregulation of corresponding metabolites. Negative enrichment of liver adipogenesis and lipid metabolism corroborated with a significant reduction in most lipids in serum samples of HCC patients (paired t-test, p < .0001). Two HCC subtypes from our Hispanic cohort were identified and validated with the TCGA liver cancer cohort. Patients with better overall survival showed higher activity of immune and angiogenesis signatures, and lower activity of liver function-related gene signatures. They also had higher levels of immune checkpoint and immune exhaustion markers. CONCLUSIONS: Our study revealed specific molecular features of Hispanic HCC and potential biomarkers for therapeutic management. It provides a unique resource for studying Hispanic HCC.

Identification of molecular targets and small drug candidates for Huntington's disease via bioinformatics and a network-based screening approach.

Huntington's disease (HD) is a gradually severe neurodegenerative ailment characterised by an increase of a specific trinucleotide repeat sequence (cytosine-adenine-guanine, CAG). It is passed down as a dominant characteristic that worsens over time, creating a significant risk. Despite being monogenetic, the underlying mechanisms as well as biomarkers remain poorly understood. Furthermore, early detection of HD is challenging, and the available diagnostic procedures have low precision and accuracy. The research was conducted to provide knowledge of the biomarkers, pathways and therapeutic targets involved in the molecular processes of HD using informatic based analysis and applying network-based systems biology approaches. The gene expression profile datasets GSE97100 and GSE74201 relevant to HD were studied. As a consequence, 46 differentially expressed genes (DEGs) were identified. 10 hub genes (TPM1, EIF2S3, CCN2, ACTN1, ACTG2, CCN1, CSRP1, EIF1AX, BEX2 and TCEAL5) were further differentiated in the protein-protein interaction (PPI) network. These hub genes were typically down-regulated. Additionally, DEGs-transcription factors (TFs) connections (e.g. GATA2, YY1 and FOXC1), DEG-microRNA (miRNA) interactions (e.g. hsa-miR-124-3p and has-miR-26b-5p) were also comprehensively forecast. Additionally, related gene ontology concepts (e.g. sequence-specific DNA binding and TF activity) connected to DEGs in HD were identified using gene set enrichment analysis (GSEA). Finally, in silico drug design was employed to find candidate drugs for the treatment HD, and while the possible modest therapeutic compounds (e.g. cortistatin A, 13,16-Epoxy-25-hydroxy-17-cheilanthen-19,25-olide, Hecogenin) against HD were expected. Consequently, the results from this study may give researchers useful resources for the experimental validation of Huntington's diagnosis and therapeutic approaches.

Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma.

BACKGROUND: Kidney renal clear cell carcinoma (KIRC) represents a significant proportion of renal cell carcinomas and is characterized by high aggressiveness and poor prognosis despite advancements in immunotherapy. Disulfidptosis, a novel cell death pathway, has emerged as a critical mechanism in various cellular processes, including cancer. This study leverages machine learning to identify disulfidptosis-related long noncoding RNAs (DRlncRNAs) as potential prognostic biomarkers in KIRC, offering new insights into tumor pathogenesis and treatment avenues. RESULTS: Our analysis of data from The Cancer Genome Atlas (TCGA) led to the identification of 431 DRlncRNAs correlated with disulfidptosis-related genes. Five key DRlncRNAs (SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3, and AC108673.3) were used to develop a prognostic model that effectively distinguished between low- and high-risk patients with significant differences in overall survival and progression-free survival. The low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. Conversely, the high-risk group displayed aggressive tumor features and poor immunotherapy outcomes. Validation through qRT‒PCR confirmed the differential expression of these DRlncRNAs in KIRC cells compared to normal kidney cells, underscoring their potential functional significance in tumor biology. CONCLUSIONS: This study established a robust link between disulfidptosis-related lncRNAs and patient prognosis in KIRC, underscoring their potential as prognostic biomarkers and therapeutic targets. The differential expression of these lncRNAs in tumor versus normal tissue further highlights their relevance in KIRC pathogenesis. The predictive model not only enhances our understanding of KIRC biology but also provides a novel stratification tool for precision medicine approaches, improving treatment personalization and outcomes in KIRC patients.

Resource: A curated database of brain-related functional gene sets (Brain.GMT).

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation. •We compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT").•Brain.GMT can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret neuroscience transcriptional profiling results from three species (rat, mouse, human).•Although Brain.GMT is still undergoing development, it substantially improved the interpretation of differential expression results within our initial use cases.

Comprehensive proteomic profiling of lung adenocarcinoma: development and validation of an innovative prognostic model.

Background: Lung adenocarcinoma (LUAD), a global leading cause of cancer deaths, remains inadequately addressed by current protein biomarkers. Our study focuses on developing a protein-based risk signature for improved prognosis of LUAD. Methods: We employed the least absolute shrinkage and selection operator (LASSO)-COX algorithm on The Cancer Genome Atlas database to construct a prognostic model incorporating six proteins (CD49B, UQCRC2, SMAD1, FOXM1, CD38, and KAP1). The model's performance was assessed using principal component, Kaplan-Meier (KM), and receiver operating characteristic (ROC) analysis, indicating strong predictive capability. The model stratifies LUAD patients into distinct risk groups, with further analysis revealing its potential as an independent prognostic factor. Additionally, we developed a predictive nomogram integrating clinicopathologic factors, aimed at assisting clinicians in survival prediction. Gene set enrichment analysis (GSEA) and examination of the tumor immune microenvironment were conducted, highlighting metabolic pathways in high-risk genes and immune-related pathways in low-risk genes, indicating varied immunotherapy sensitivity. Validation through immunohistochemistry from the Human Protein Atlas (HPA) database and immunofluorescence staining of clinical samples was performed, particularly focusing on CD38 expression. Results: Our six-protein model (CD49B, UQCRC2, SMAD1, FOXM1, CD38, KAP1) effectively categorized LUAD patients into high and low-risk groups, confirmed by principal component, KM, and ROC analyses. The model showed high predictive accuracy, with distinct survival differences between risk groups. Notably, CD38, traditionally seen as protective, was paradoxically associated with poor prognosis in LUAD, a finding supported by immunohistochemistry and immunofluorescence data. GSEA revealed that high-risk genes are enriched in metabolic pathways, while low-risk genes align with immune-related pathways, suggesting better immunotherapy response in the latter group. Conclusions: This study presented a novel prognostic protein model for LUAD, highlighting the CD38 expression paradox and enhancing our understanding of protein roles in lung cancer progression. It offered new clinical tools for prognosis prediction and provided assistance for future lung cancer pathogenesis research.

Resource: A Curated Database of Brain-Related Functional Gene Sets (Brain.GMT).

Transcriptional profiling has become a common tool for investigating the nervous system. During analysis, differential expression results are often compared to functional ontology databases, which contain curated gene sets representing well-studied pathways. This dependence can cause neuroscience studies to be interpreted in terms of functional pathways documented in better studied tissues (e.g., liver) and topics (e.g., cancer), and systematically emphasizes well-studied genes, leaving other findings in the obscurity of the brain "ignorome". To address this issue, we compiled a curated database of 918 gene sets related to nervous system function, tissue, and cell types ("Brain.GMT") that can be used within common analysis pipelines (GSEA, limma, edgeR) to interpret results from three species (rat, mouse, human). Brain.GMT includes brain-related gene sets curated from the Molecular Signatures Database (MSigDB) and extracted from public databases (GeneWeaver, Gemma, DropViz, BrainInABlender, HippoSeq) and published studies containing differential expression results. Although Brain.GMT is still undergoing development and currently only represents a fraction of available brain gene sets, "brain ignorome" genes are already better represented than in traditional Gene Ontology databases. Moreover, Brain.GMT substantially improves the quantity and quality of gene sets identified as enriched with differential expression in neuroscience studies, enhancing interpretation.

Integrated Multi-Omics Analysis and Validation in Yeast Model of Amyotrophic Lateral Sclerosis.

Transcriptomics is a complex process that involves raw data extraction, normalization, differential gene expression, and analysis. The Gene Expression Omnibus (GEO) database at the National Center for Biotechnology Information (NCBI) is a repository of experimental datasets. Amyotrophic lateral sclerosis (ALS) datasets are deposited by various scientists and research investigators to expand the horizon of scientific knowledge. R-statistical tools are the most common ways for conducting these kinds of studies. The first step is the identification of appropriate datasets. Since the raw data is available in a variety of formats, a large array of software is used for extraction and analysis. Normalization is conducted for the datasets using NetworkAnalyst. Differential analysis is further conducted on the normalized data to identify significantly enriched genes. The significant genes are then grouped into pathways. The results were validated using yeast model of ALS in which the yeast is transformed with ALS plasmids encoding genes associated with ALS. The resulting GFP-tagged protein aggregates are imaged using fluorescence microscopy and subsequently validated using filter retardation assay and quantified using ImageJ software. Functional role of different genes is studied using metabolite treatment and knockout studies.

A Cyclic Permutation Approach to Removing Spatial Dependency between Clustered Gene Ontology Terms.

Traditional gene set enrichment analysis falters when applied to large genomic domains, where neighboring genes often share functions. This spatial dependency creates misleading enrichments, mistaking mere physical proximity for genuine biological connections. Here we present Spatial Adjusted Gene Ontology (SAGO), a novel cyclic permutation-based approach, to tackle this challenge. SAGO separates enrichments due to spatial proximity from genuine biological links by incorporating the genes' spatial arrangement into the analysis. We applied SAGO to various datasets in which the identified genomic intervals are large, including replication timing domains, large H3K9me3 and H3K27me3 domains, HiC compartments and lamina-associated domains (LADs). Intriguingly, applying SAGO to prostate cancer samples with large copy number alteration (CNA) domains eliminated most of the enriched GO terms, thus helping to accurately identify biologically relevant gene sets linked to oncogenic processes, free from spatial bias.

Hormetic effect of a short-chain PFBS on Microcystis aeruginosa and its molecular mechanism.

Short-chain Perfluorinated compounds (PFCs), used as substitutes for highly toxic long-chain PFCs, are increasingly entering the aquatic environment. However, the toxicity of short-chain PFCs in the environment is still controversial. This study investigated the effects of short-chain perfluorobutanesulfonic acid (PFBS) at different concentrations (2.5, 6, 14.4, 36, and 90 mg/L) on M. aeruginosa growth under 12-day exposure and explored the molecular mechanism of toxicity using transcriptomics. The results showed that M. aeruginosa exhibited hormetic effects after exposure to PFBS. Low PFBS concentrations stimulated algal growth, whereas high PFBS concentrations inhibited it, and this inhibitory effect became progressively more pronounced with increasing PFBS exposure concentrations. Transcriptomics showed that PFBS promoted the pathways of photosynthesis, glycolysis, energy metabolism and peptidoglycan synthesis, providing the energy required for cell growth and maintaining cellular morphology. PFBS, on the other hand, caused growth inhibition in algae mainly through oxidative stress, streptomycin synthesis, and genetic damage. Our findings provide new insights into the toxicity and underlying mechanism of short-chain PFCs on algae and inform the understanding of the hormetic effect of short-chain PFCs, which are crucial for assessing their ecological risks in aquatic environments.

TRIP 13-dependent pathways promote the development of gastric cancer.

TRIP13 is highly expressed in various human tumors and promotes tumorigenesis. We aimed to explore the biological effect of TRIP13 on gastric cancer. The RNA sequence data were retrieved from TCGA to evaluate TRIP13 mRNA expression in gastric cancer. Paired formalin-fixed paraffin-embedded blocks were further analyzed to verify the relationship between TRIP13 expression and carcinogenic status. The functions of TRIP13 on the proliferation of gastric malignancy were investigated by MTT, flow cytometry, colony formation experiment, and nude mouse tumor formation experiment. Finally, microarray analysis of TRIP13-related pathways was performed to identify the potential underlying mechanism of TRIP13 in gastric cancer. TRIP13 was found to have high expression in tumor samples. TRIP13 expression status was significantly subjective to tumor-node-metastasis (TNM) staging and poor survival. The downregulation of TRIP13 promoted apoptosis and inhibited tumor growth. TRIP13-dependent JAK/STAT and NF-κB signaling cascade were found as two key pathways in the carcinogenesis of GC. In conclusion, TRIP13 participates in the carcinogenesis of stomach cancer, and its overexpression in the cancerous tissues dovetail with advanced stage and survival. Moreover, TRIP13 functions as an upstream regulator of the JAK/STAT and p53 signaling pathways, which play critical roles in developing various malignancies.

The Landscapes of Gluten Regulatory Network in Elite Wheat Cultivars Contrasting in Gluten Strength.

Yangmai-13 (YM13) is a wheat cultivar with weak gluten fractions. In contrast, Zhenmai-168 (ZM168) is an elite wheat cultivar known for its strong gluten fractions and has been widely used in a number of breeding programs. However, the genetic mechanisms underlying the gluten signatures of ZM168 remain largely unclear. To address this, we combined RNA-seq and PacBio full-length sequencing technology to unveil the potential mechanisms of ZM168 grain quality. A total of 44,709 transcripts were identified in Y13N (YM13 treated with nitrogen) and 51,942 transcripts in Z168N (ZM168 treated with nitrogen), including 28,016 and 28,626 novel isoforms in Y13N and Z168N, respectively. Five hundred and eighty-four differential alternative splicing (AS) events and 491 long noncoding RNAs (lncRNAs) were discovered. Incorporating the sodium-dodecyl-sulfate (SDS) sedimentation volume (SSV) trait, both weighted gene coexpression network analysis (WGCNA) and multiscale embedded gene coexpression network analysis (MEGENA) were employed for network construction and prediction of key drivers. Fifteen new candidates have emerged in association with SSV, including 4 transcription factors (TFs) and 11 transcripts that partake in the post-translational modification pathway. The transcriptome atlas provides new perspectives on wheat grain quality and would be beneficial for developing promising strategies for breeding programs.

Prediction of target genes in community-acquired pneumonia based on the bioinformatics method.

Background: To screen the related genes of community-acquired pneumonia (CAP) by bioinformatics technology, and to analyze the clinical value of key genes. Methods: Gene chip data sets containing CAP patients and normal controls were screened from the Gene Expression Omnibus (GEO) database. The downregulated differentially expressed genes (DEGs) were screened using a gene expression analysis tool (GEO2R). Simultaneously, gene set enrichment analysis (GSEA) was used to explore the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and core genes related to CAP. The candidate genes were then intersected with the genes reported in Online Mendelian Inheritance in Man (OMIM), and the clinical value of these candidate genes was examined based on a literature search. Finally, the clinical data of the CAP patients were retrospectively analyzed. Detect the type of pathogenic bacteria in bronchial-alveolar lavage fluid (BALF) using metagenomics next-generation sequencing (mNGS) high throughput sequencing technology, and detect the expression of key genes through liquid based cell immunohistochemistry to analyze the correlation between pathogenic bacteria and key genes. Results: Through the intersection of Venn diagrams, 175 co-expressed downregulated DEGs related to CAP were identified. A total of 4 candidate genes, including ICOS, IL7R, ITK, and ZAP70, were obtained by constructing the protein mutual aid network and conducting a module analysis of the common differentially expressed genes. The core genes in the GSEA enrichment pathways were intersected with the CAP-related genes reported in the relevant literature retrieved from the OMIM database. In the Venn diagram, two genes that coexist with OMIM include IL7R and PIK3R1. After considering our findings and the relevant literature, we determined that the key gene related to the occurrence and development of CAP was IL7R. The mNGS detected 13 kinds of bacteria, 4 kinds of fungi, and 2 kinds of viruses. Based on immunohistochemical results, it was found that there were relatively more bacteria detected in the IL7R high expression group. Conclusions: The identification of the key gene IL7R and the related signaling pathways extend understanding of the pathogenesis of CAP and provide a theoretical basis for clinical targeted therapy research.

Profiles of immune infiltration in seasonal allergic rhinitis and related genes and pathways.

BACKGROUND: Seasonal allergic rhinitis (SAR) is a chronic inflammatory disease for which the molecular mechanism is unclear. METHODS: Whole blood, CD4+ T cells in peripheral blood mononuclear cells (PBMCs), and CD4+ T cells in nasal mucosa from SAR-related datasets (GSE43497, GSE50223, and GSE49782) were downloaded from the Gene Expression Omnibus (GEO) database. Differences in SAR-associated immune cell infiltration in the PBMCs were analyzed using the CIBERSORT algorithm. Differential gene expression analysis was conducted between different groups. Gene set enrichment analysis (GSEA) was performed using the clusterProfiler package to explore functional changes in signaling pathways. RESULTS: There was a significant increase in the proportion of CD8+ T cells and a significant decrease in the proportion of neutrophils in the whole blood of SAR patients after allergen challenge compared to SAR patients after diluent challenge. This pattern was also found in SAR patients compared to healthy controls (HCs) by flow cytometry. The NF-κB and Toll-like receptor signaling pathways were enriched in SAR patients following allergen challenge. The expression of CD4+ T cell marker genes and associated cytokines significantly differed between allergen-treated SAR patients, diluent-treated SAR patients and HCs. We also observed heightened CD4+ T cell related genes, cytokines and pathways activation in the nasal mucosa region of SAR patients after allergen challenge. CONCLUSION: Our analysis revealed that T cell receptor signaling pathways, T helper 1 (Th1) /T helper 2 (Th2) cell differentiation may contribute to the development of SAR. The present study is the first bioinformatic analysis to quantify immune cell infiltration and identify underlying SAR mechanisms from combined microarray data and provides insight for further research into the molecular mechanisms of SAR.

Cinnamomi ramulus inhibits cancer cells growth by inducing G2/M arrest.

Introduction: Cinnamomi ramulus (CR) is one of the most widely used traditional Chinese medicine (TCM) with anti-cancer effects. Analyzing transcriptomic responses of different human cell lines to TCM treatment is a promising approach to understand the unbiased mechanism of TCM. Methods: This study treated ten cancer cell lines with different CR concentrations, followed by mRNA sequencing. Differential expression (DE) analysis and gene set enrichment analysis (GSEA) were utilized to analyze transcriptomic data. Finally, the in silico screening results were verified by in vitro experiments. Results: Both DE and GSEA analysis suggested the Cell cycle pathway was the most perturbated pathway by CR across these cell lines. By analyzing the clinical significance and prognosis of G2/M related genes (PLK1, CDK1, CCNB1, and CCNB2) in various cancer tissues, we found that they were up-regulated in most cancer types, and their down-regulation showed better overall survival rates in cancer patients. Finally, in vitro experiments validation on A549, Hep G2, and HeLa cells suggested that CR can inhibit cell growth by suppressing the PLK1/CDK1/ Cyclin B axis. Discussion: This is the first study to apply transcriptomic analysis to investigate the cancer cell growth inhibition of CR on various human cancer cell lines. The core effect of CR on ten cancer cell lines is to induce G2/M arrest by inhibiting the PLK1/CDK1/Cyclin B axis.

Repurposing Approved Drugs for Sarcopenia Based on Transcriptomics Data in Humans.

Sarcopenia, characterized by age-related loss of muscle mass, strength, and decreased physical performance, is a growing public health challenge amid the rapidly ageing population. As there are no approved drugs that target sarcopenia, it has become increasingly urgent to identify promising pharmacological interventions. In this study, we conducted an integrative drug repurposing analysis utilizing three distinct approaches. Firstly, we analyzed skeletal muscle transcriptomic sequencing data in humans and mice using gene differential expression analysis, weighted gene co-expression analysis, and gene set enrichment analysis. Subsequently, we employed gene expression profile similarity assessment, hub gene expression reversal, and disease-related pathway enrichment to identify and repurpose candidate drugs, followed by the integration of findings with rank aggregation algorithms. Vorinostat, the top-ranking drug, was also validated in an in vitro study, which demonstrated its efficacy in promoting muscle fiber formation. Although still requiring further validation in animal models and human clinical trials, these results suggest a promising drug repurposing prospect in the treatment and prevention of sarcopenia.

Identification of glycolysis genes signature for predicting prognosis in malignant pleural mesothelioma by bioinformatics and machine learning.

Background: Glycolysis-related genes as prognostic markers in malignant pleural mesothelioma (MPM) is still unclear. We hope to explore the relationship between glycolytic pathway genes and MPM prognosis by constructing prognostic risk models through bioinformatics and machine learning. Methods: The authors screened the dataset GSE51024 from the GEO database for Gene set enrichment analysis (GSEA), and performed differentially expressed genes (DEGs) of glycolytic pathway gene sets. Then, Cox regression analysis was used to identify prognosis-associated glycolytic genes and establish a risk model. Further, the validity of the risk model was evaluated using the dataset GSE67487 in GEO database, and finally, a specimen classification model was constructed by support vector machine (SVM) and random forest (RF) to further screen prognostic genes. Results: By DEGs, five glycolysis-related pathway gene sets (17 glycolytic genes) were identified to be highly expressed in MPM tumor tissues. Also 11 genes associated with MPM prognosis were identified in TCGA-MPM patients, and 6 (COL5A1, ALDH2, KIF20A, ADH1B, SDC1, VCAN) of them were included by Multi-factor COX analysis to construct a prognostic risk model for MPM patients, with Area under the ROC curve (AUC) was 0.830. Further, dataset GSE67487 also confirmed the validity of the risk model, with a significant difference in overall survival (OS) between the low-risk and high-risk groups (P < 0.05). The final machine learning screened the five prognostic genes with the highest risk of MPM, in order of importance, were ALDH2, KIF20A, COL5A1, ADH1B and SDC1. Conclusions: A risk model based on six glycolytic genes (ALDH2, KIF20A, COL5A1, ADH1B, SDC1, VCAN) can effectively predict the prognosis of MPM patients.

Elevated expression of protein-L-isoaspartate O-methyltransferase-1 (PCMT1) in cervical cancer.

Background: Protein-L-isoaspartate O-methyltransferase-1 (PCMT1) is a protein carboxyl methyltransferase enzyme, which has been found to play roles in cancers. However, no clinical information about the correlation between cervical cancer and PCMT1 expression has been reported. Methods: We used immunohistochemistry (IHC) to characterize the protein level of PCMT1 in human cervical intraepithelial neoplasia and cervical cancer specimens. The mRNA expression profile of PCMT1 in cervical cancer was also analyzed by using Gene Expression Omnibus (GEO) databases. The prognostic value of PCMT1 in patients with cervical cancer was evaluated by using the Kaplan-Meier plotter. Gene set enrichment analysis (GSEA) was conducted by using The Cancer Genome Atlas (TCGA) cervical cancer dataset. Results: The protein level of PCMT1 was increased in cervical high-grade squamous intraepithelial lesion (HSIL) (7.40±0.42) and cervical cancer tissues (10.70±0.54), compared to normal cervix (5.00±0.86) and low-grade squamous intraepithelial lesion (LSIL) (6.22±0.57) (P<0.05). the immunoreactivity score (IRS) of PCMT1 was also higher in cervical cancer tissues than in paired adjacent non-cancerous cervical tissues (9.03±0.52 vs. 6.32±0.46) (P<0.05). High expression of PCMT1 was associated with decreased overall survival (OS) of patients with cervical cancer (P=0.0022). GSEA demonstrated that cervical cancer patients with high expression of PCMT1 were enriched in the various cancer-related signaling pathways. Conclusions: These results suggest that PCMT1 might be a diagnostic and prognostic biomarker for cervical cancer, and further validation studies should be performed.

MAP kinase and mammalian target of rapamycin are main pathways of gallbladder carcinogenesis: results from bioinformatic analysis of next generation sequencing data from a hospital-based cohort (NCT05404347).

BACKGROUND: Gallbladder Cancer (GBC) is one of the most common cancers of the biliary tract and the third commonest gastrointestinal (GI) malignancy worldwide. The disease is characterized by the late presentation and poor outcome despite treatment, and hence, newer therapies and targets need to be identified. METHODS: The current study investigated various functionally enriched pathways in GBC pathogenesis involving the genes identified through Next Generation Sequencing (NGS) in a hospital-based cohort. The Pathway enrichment analysis and Gene Ontology (GO) were carried out after NGS, followed by the construction of the protein-protein interaction (PPI) network to discover associations among the genes. RESULTS: Of the thirty-three patients with GBC who were screened through next-generation sequencing (NGS), 27somatic mutations were identified. These mutations involved a total of 14 genes. The p53 and KRAS were commonly found to be mutated, while mutations in other genes were seen in one case each, the mean number of mutations were 1.2, and maximum mutation in a single case (eight) was seen in one case. The bioinformatics analysis identified MAP kinase, PI3K-AKT, EGF/EGFR, and Focal Adhesion PI3K-AKT-mTOR signaling pathways and cross-talk between these. CONCLUSION: The results suggest that the complex crosstalk between the mTOR, MAPK, and multiple interacting cell signaling cascades can promote GBC progression, and hence, mTOR-MAPK targeted treatment will be an attractive option.

Gene Set Enrichment Analysis Reveals That Fucoidan Induces Type I IFN Pathways in BMDC.

Fucoidan, a sulfated polysaccharide extracted from brown seaweed, has been proposed to effectively treat and prevent various viral infections. However, the mechanisms behind its antiviral activity are not completely understood. We investigate here the global transcriptional changes in bone marrow-derived dendritic cells (BMDCs) using RNA-Seq technology. Through both analysis of differentially expressed genes (DEG) and gene set enrichment analysis (GSEA), we found that fucoidan-treated BMDCs were enriched in virus-specific response pathways, including that of SARS-CoV-2, as well as pathways associated with nucleic acid-sensing receptors (RLR, TLR, NLR, STING), and type I interferon (IFN) production. We show that these transcriptome changes are driven by well-known regulators of the inflammatory response against viruses, including IRF, NF-κB, and STAT family transcription factors. Furthermore, 435 of the 950 upregulated DEGs are classified as type I IFN-stimulated genes (ISGs). Flow cytometric analysis additionally showed that fucoidan increased MHCII, CD80, and CD40 surface markers in BMDCs, indicative of greater antigen presentation and co-stimulation functionality. Our current study suggests that fucoidan transcriptionally activates PRR signaling, type I IFN production and signaling, ISGs production, and DC maturation, highlighting a potential mechanism of fucoidan-induced antiviral activity.

Development and Validation of an Immune-Related Prognostic Signature in Cervical Cancer.

Background: Cervical cancer is the fourth most frequent gynecological malignancy across the world. Immunotherapies have proved to improve prognosis of cervical cancer. However, few studies on immune-related prognostic signature had been reported in cervical cancer. Methods: Raw data and clinical information of cervical cancer samples were downloaded from TCGA and UCSC Xena website. Immunophenoscore of immune infiltration cells in cervical cancer samples was calculated through the ssGSEA method using GSVA package. WGCNA, Cox regression analysis, LASSO analysis, and GSEA analysis were performed to classify cervical cancer prognosis and explore the biological signaling pathway. Results: There were eight immune infiltration cells associated with prognosis of cervical cancer. Through WGCNA, 153 genes from 402 immune-related genes were significantly correlated with prognosis of cervical cancer. A 15-gene signature demonstrated powerful predictive ability in prognosis of cervical cancer. GSEA analysis showed multiple signaling pathways containing Programmed cell death ligand-1 (PD-L1) expression and PD-1 checkpoint pathway differences between high-risk and low-risk groups. Furthermore, the 15-gene signature was associated with multiple immune cells and immune infiltration in tumor microenvironment. Conclusion: The 15-gene signature is an effective potential prognostic classifier in the immunotherapies and surveillance of cervical cancer.