Comparative and Functional Genomics最新文献

Background: CD8⁺ T cells play pivotal roles in antitumor immunity, where infiltration levels often correlate with favorable prognosis. However, the functional heterogeneity of CD8⁺ T cell subsets within the gastric cancer (GC) tumor microenvironment (TME)—particularly their divergent impacts on tumor progression, immunotherapy response, and clinical outcomes—remains poorly characterized.

Methods: We integrated single-cell RNA sequencing (scRNA-seq) data from 23 GC tissues (GEO: GSE150290) with bulk transcriptomic profiles from TCGA-STAD to dissect CD8⁺ T cell heterogeneity. Analytical pipelines included unsupervised clustering, pseudotime trajectory analysis, and protein–protein interaction (PPI) network construction to identify survival-associated hub genes. Differential gene expression, functional enrichment, and experimental validation were performed to confirm clinical relevance.

Results: scRNA-seq resolved CD8⁺ T cells into five functionally distinct subsets: naïve/memory, exhausted, and three cytotoxic subpopulations. Among these, cytotoxic CD8⁺ T1 cells exhibited the strongest prognostic relevance, with high infiltration correlating to improved survival and enrichment in G2-grade tumors. Pseudotime analysis revealed differentiation trajectories from naïve to exhausted subsets, accompanied by metabolic and immune checkpoint pathway alterations. PPI network analysis identified SELL, CD79B, and RAMP2 as hub genes, all significantly linked to survival and differentially expressed across tumor grades/stages. Experimental validation confirmed that SELL, CD79B, and RAMP2 knockdown suppressed GC cell proliferation, underscoring their functional roles.

Conclusion: Our study unveils the landscape of CD8⁺ T cell heterogeneity in GC and proposes a three-gene signature (SELL/CD79B/RAMP2) with dual prognostic and therapeutic potential. These findings provide actionable insights for stratifying patients, tailoring immunotherapy regimens, and developing novel targets to enhance antitumor immunity in GC.

Aims: The aim of this study was to systematically analyze the role of Brucea javanica in the treatment of cervical cancer (CC) and its underlying mechanisms by means of network pharmacology and molecular docking.

Background:Brucea javanica is a traditional Chinese herbal medicine used for the treatment of malaria and cancers, but its mechanism of action in CC is unknown.

Objective: The objective of the study is screening of active chemical constituents of Brucea javanica by Traditional Chinese Medicine Systems Pharmacology (TCMSP) database and investigating their potential targets involved in CC therapy.

Methods: The GeneCards database was used for the disease targets of CC, the drug–compound–disease target network was constructed by using the Cytoscape 3.8.0 software. Then, the key targets in the protein–protein interaction (PPI) network were identified, and the “clusterProfiler” was used for the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. The qRT-PCR, CCK-8, and flow cytometry were used to assess the expression levels of specific target genes in CC cells, as well as their effects on cell proliferation, apoptosis, and reactive oxygen species (ROS) levels, respectively. Protein–compound complex analysis was performed using molecular dynamics simulation.

Results: A total of 15 active compounds and their 86 treatment targets were obtained from the Brucea javanica analysis, in which 51 target genes were associated with the CC-related disease targets. Then, a PPI analysis identified 12 key genes (including EGFR, TP53, BCL2, AKT1, JUN, TNF, CASP3, IL6, MMP9, ERBB2, CCND1, and PTGS2) that were related to oxidative stress, PI3K-Akt, IL-17, p53, and JAK-STAT pathways, inflammatory response, and apoptosis pathways. In addition, AKT1 showed upregulation at the mRNA level in SiHa cells, and the knockdown of AKT1 significantly reduced the proliferation of CC cells and increased apoptosis and ROS levels. Molecular docking and dynamics simulations revealed a close binding between the active compounds and targets.

Conclusions: The present research comprehensively examined the active compounds, potential targets, and pathways of Brucea javanica in CC treatment, providing a novel insight for CC treatment.

This study investigates the promoter region and regulatory elements of chicken insulin-like growth factor (IGF) and vasoactive intestinal polypeptide (VIP) genes associated with reproductive traits. Several in silico tools, such as Neural Network Promoter Prediction (NNPP), Multiple Expectation maximizations for Motif Elicitation (MEME-Suite), GC-Profiles, microsatellite prediction (MISA-web), CLC Genomics, Multiple Association Network Integration Algorithm (GeneMANIA), and Gene Ontology for Motifs (GOMO), were used to characterize the promoter regions and regulatory elements of IGF and VIP genes. The in silico analysis showed that the highest promoter prediction scores (1.0) for TSS were obtained for three gene sequences (IGFP4, VIP, and VIPR1), while the lowest promoter prediction score (0.8) was obtained for IGF1. The present analysis revealed that the best common motif, Motif II, resembles three major transcription factor families: zinc finger family, homeobox transcription factor family, and high-mobility group factor family, accounting for about 79.17%. This study found that 62.5% of the candidate transcription factors have interaction with the Wnt signalling pathway to regulate transcription. Key regulatory elements identified in this study, such as CPEB1, MAFB, SOX15, TCF7L2, TCF3, and TCF7, play critical roles in activating and repressing transcription, with significant implications for embryonic and nervous system development. In the current study, very rich CpG islands were identified in the gene body and promoter regions of IGF and VIP genes. Generally, in silico analysis of gene promoter regions and regulatory elements in IGF and VIP genes can be helpful for comprehending regulatory networks and gene expression patterns in promoter regions, which will guide new experimental studies in gene expression assays.

Background: Glioblastoma (GBM) represents an aggressive malignancy in the central nervous system, with a poor prognosis. Despite ongoing research efforts, there is still a lack of effective treatments, leading to the need for new therapeutic targets. Collagen plays a crucial role in the extracellular matrix and can impact the progression of cancer. Yet the potential involvement of COL22A1 (Collagen Type XXII Alpha 1 chain) in GBM has not been investigated.

Materials and Methods: The expression of COL22A1 was evaluated in both clinical GBM samples and the Gene Expression Profiling Interactive Analysis (GEPIA) database. Following COL22A1 knockdown in GBM cells, functional assays were conducted to assess proliferation, migration, and invasion. The influence of COL22A1 on oncogenic signaling pathways was analyzed through luciferase reporter assays and interventions with pharmacological agents. In vivo experiments were performed using a nude mouse xenograft model.

Results: COL22A1 expression was significantly higher in GBM tissues and was linked with a poor prognosis. Silencing COL22A1 suppressed proliferation, migration, and invasion of GBM cells and impeded tumorigenesis in vivo. On a mechanistic level, COL22A1 impacted the PI3K/AKT signaling cascade, demonstrated by decreased FOXO transcriptional activity and lower levels of phosphorylated PI3K (p-PI3K) and phosphorylated AKT (p-AKT). Furthermore, stimulating the PI3K/AKT pathway partially mitigated the impact of COL22A1 silencing.

Conclusion: COL22A1 plays a crucial role in dictating the malignancy of GBM through regulating the PI3K/AKT signaling pathway. Targeting COL22A1 could present a novel approach for GBM management.

Background: Sepsis is an infection-induced dysregulated cellular response that leads to multiorgan dysfunction. As a time-sensitive condition, sepsis requires prompt diagnosis and standardized treatment. This study investigated the impact of biomarkers identified in peripheral whole blood from sepsis patients (24-h post-onset) on sepsis-induced acute lung injury (ALI) using bioinformatics and machine learning approaches.

Methods: Gene Expression Omnibus (GEO) datasets were analyzed for functional and differential gene expression. Critical genetic markers were identified and evaluated using multiple machine learning algorithms. Single-cell RNA sequencing (scRNA-seq) and cell-type identification by estimating relative subsets of RNA transcript (CIBERSORT) were conducted to explore associations between biomarkers and immune cells. Biomarker expression was further validated through animal experiments.

Result: A total of 611 overlapping differentially expressed genes (DEGs) were identified in GSE54514, including 361 upregulated and 250 downregulated genes. From GSE95233, 1150 DEGs were detected, with 703 upregulated and 447 downregulated genes. Enrichment analysis revealed DEGs associated with immune cell activity, immune cell activation, and inflammatory signaling pathways. Component 3a receptor 1 (C3AR1) and secretory leukocyte peptidase inhibitor (SLPI) were identified as critical biomarkers through multiple machine learning approaches. CIBERSORT analysis revealed significant associations between immune cell types and C3AR1/SLPI. Moreover, the scRNA-seq analysis demonstrated that the SLPI expression was significantly elevated in immunological organ cells during the early stages of sepsis, a finding further validated in sepsis-induced ALI models.

Conclusion: This study employed machine learning techniques to identify sepsis-associated genes and confirmed the importance of SLPI as a biomarker within 24 h of sepsis onset. SLPI also played a significant role in sepsis-induced ALI, suggesting its potential as a novel target for personalized medical interventions, targeted prevention, and patient screening.

Backgrounds and Aims: Lung squamous cell carcinoma (LUSC) represents a significant challenge in oncology, necessitating the identification of novel prognostic markers and therapeutic targets. This study is aimed at investigating the oncogenic role of MXD3 (MAX Dimerization Protein 3) in LUSC and its implications for patient prognosis.

Methods: A retrospective cohort of 199 LUSC patients from the 905th Hospital of People’s Liberation Army Navy was analyzed to evaluate MXD3 expression levels and their association with clinicopathological characteristics and survival outcomes. Immunohistochemistry (IHC) staining was performed to assess MXD3 expression in LUSC tissue samples. Survival analyses, including the Kaplan–Meier curves and multivariate Cox regression, were conducted to determine the prognostic significance of MXD3 expression and other clinicopathological factors. Additionally, the methylation status of MXD3 was examined using data from the TCGA database to assess its role in regulating MXD3 expression and survival outcomes.

Results: MXD3 expression exhibited significant heterogeneity among LUSC patients, with high MXD3 expression correlating with advanced tumor differentiation grade, larger tumor size, and advanced T and N stages. The Kaplan–Meier survival analyses revealed that high MXD3 expression was associated with shorter cancer-specific survival. Multivariate Cox regression identified MXD3 expression level and lymph node involvement (N stage) as independent prognostic factors for cancer-specific survival in LUSC patients. Additionally, analysis of MXD3 methylation revealed significantly lower methylation levels in LUSC tissues, and reduced methylation correlated with poorer survival outcomes.

Conclusions: Our findings highlight MXD3 as a promising prognostic biomarker for LUSC, with high MXD3 expression predicting poorer survival outcomes. MXD3 expression level, along with lymph node involvement and methylation status, could serve as independent prognostic indicators for risk stratification and treatment decision-making in LUSC patients. Further research is warranted to elucidate the underlying mechanisms of MXD3-mediated tumorigenesis and its potential as a therapeutic target in LUSC management.

Background: Cervical cancer is a complex disease with considerable cellular heterogeneity, which hampers our understanding of its progression and the development of effective treatments. Single-cell RNA sequencing (scRNA-seq)—a technology that enables gene expression analysis at the cellular level—has emerged as an important tool to explore this heterogeneity on a cell-to-cell basis. We perform an analysis on data quality and differential gene expression in cervical cancer via scRNA-seq, giving insights into the tumor microenvironment and likely therapeutic targets.

Methods: scRNA-seq for cervical cancer sample and advanced bioinformatics tool for data analysis were utilized. Scatter plots were generated to assess quality control metrics based on mitochondrial gene expression and total RNA count. Cell clustering differential expression analysis identified significant genes in each cell cluster. Gene coexpression networks and modules were performed network analysis. We utilized pseudotime analysis to model the experience of cell state transitions to infer a trajectory and functional enrichment analysis to understand the biological processes involved.

Results: scRNA-seq data revealed distinct cluster pattern of high quality gene expression profile. Ultimately, differential expression analysis suggested significant genes: TP53, GNG4, and CCL5 had high degrees of differential expression and potential roles in tumor progression. Some of these gene modules have unique biological functions identified by network analysis, while dynamic changes in gene expression across the trajectory of the pseudotime reveal the differences in gene expression during cell state transition. We next performed functional enrichment analysis which revealed that immune response and metabolic processes play a pivotal role in cervical cancer.

Conclusion: Our large scale scRNA-seq of cervical cancer provide insights into cellular heterogeneity and gene expression dynamics within the tumor microenvironment.

Background: This study aimed to assess the pathogenicity of newly identified tuberous sclerosis Complex 1 (TSC1) and TSC2 variants, contributing definitive evidence for the diagnosis of TSC.

Methods: A total of 103 TSC patients underwent TSC genetic testing using standardized protocols, and genetic testing was extended to their respective families. Analysis of genetic testing results considered clinical phenotype and gene pathogenicity based on the 2012 revision of the International Society of TSC.

Results: Among participants, 12 exhibited previously unreported variants of TSC1 or TSC2 gene absent in relevant databases. All 12 clinically diagnosed TSC patients presented typical phenotypes, such as brain lesions and skin changes. Notably, there were 2 variants of TSC1 gene and 10 variants of TSC2 gene, encompassing 8 frameshift variants, 2 nonsense variants, and 2 missense variants.

Conclusions: This study broadens the spectrum of variants of TSC1 and TSC2 genes, reaffirming the clinical diagnosis of patients through genetic testing.

Background: Triple-negative breast cancer (TNBC) is an aggressive subtype with high malignancy, rapid progression, and a poor 5-year survival rate of ~77%. Due to the lack of targeted therapies, treatment options are limited, highlighting the urgent need for novel therapeutic strategies. Myoepithelial cells (MECs) in the tumor microenvironment may significantly influence TNBC development and progression.

Methods: This study used single-cell RNA sequencing to compare the MEC gene expression in the normal versus TNBC tissues. TNBC-associated MECs showed increased expression of proliferation- and immune-related genes (e.g., FDCSP, KRT14, and KRT17) and decreased expression of inflammatory and extracellular matrix-related genes (e.g., CXCL8, SRGN, and DCN). Copy number variation and pseudotime analyses revealed genomic alterations and phenotypic dynamics in MECs. A CoxBoost-based prognostic model was developed and validated across 20 survival cohorts, integrating immune profiling, pathway enrichment, and drug sensitivity analyses. Mendelian randomization identified TPD52 as a TNBC risk–associated gene. siRNA knockdown of TPD52 was performed in TNBC cell lines to evaluate its effects on proliferation and migration.

Results: TNBC MECs displayed significant changes in the gene expression and genomic integrity, impacting immune responses and tumor invasion. The prognostic model effectively predicted 1-, 3-, and 5-year survival outcomes, stratifying high-risk patients with enriched cell cycle and DNA replication pathways, reduced immune checkpoint expression, and chemotherapy resistance. TPD52 was identified as a tumor-promoting gene, and its knockdown suppressed TNBC cell proliferation and migration.

Conclusion: This study highlights MECs’ role in TNBC progression, provides a CoxBoost prognostic model for personalized treatment, and identifies TPD52 as a potential therapeutic target for TNBC intervention.

Background: Phenylketonuria (PKU) is an autosomal recessive metabolic disorder caused by mutations in the PAH gene, resulting in deficient phenylalanine hydroxylase (PAH) enzyme activity and neurotoxic phenylalanine accumulation. Untreated PKU results in progressive neurodegeneration and severe intellectual disability. Neonatal screening has evolved from the Guthrie test to advanced techniques like HPLC, tandem mass spectrometry, and next-generation sequencing (NGS) for molecular confirmation. This study aimed to develop a rapid, scalable PAH genetic assay using Oxford Nanopore Technologies (ONTs) to enhance neonatal screening in high-prevalence regions like Türkiye, through accelerated, cost-effective genetic diagnostics.

Methods: An in-house panel was designed, implemented, and benchmarked against results obtained from the Illumina sequencing platform. A cohort of 40 PKU patients, previously diagnosed using Illumina platform, was selected for this study. Gene-specific primers were strategically designed to amplify exonic regions, untranslated segments, and exon–intron junctions of the PAH gene. Sequencing libraries were then prepared and processed using the MinION Mk1c instrument, with subsequent data analysis conducted through the Guppy software and complementary bioinformatics tools.

Results: The findings showed complete agreement between the ONT and Illumina platforms, corroborating the high fidelity and reliability of the ONT-based assay. All pathogenic variants previously identified through Illumina sequencing were accurately detected, albeit with varying observed allele frequencies. Notably, the most prevalent variants identified in the patient cohort were NC_000012.12(NM_000277.3):c.1066-11G > A with a frequency of 37.5% and NC_000012.12(NM_000277.3):c.782G > A, at 15%.

Conclusion: The ONT-based single-gene testing for PKU demonstrated complete concordance with Illumina sequencing, validating its accuracy and reliability. This method effectively detects pathogenic variants and offers a faster, cost-effective solution for neonatal screening, particularly beneficial in high-prevalence regions like Türkiye.