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PubMed2026

Pan-cancer analysis identifies KANSL2 as a cell-cycle-associated regulator of tumor progression and immunity in liver hepatocellular carcinoma.

KANSL2, a core component of the NSL histone acetyltransferase complex, has been implicated in tumorigenesis. However, its pan-cancer relevance and functional role in liver hepatocellular carcinoma (LIHC) remain unclear. Multi-omics data from TCGA, GEO, and HPA were integrated to systematically evaluate KANSL2 expression, clinical significance, genomic alterations, and immune associations across cancers. Functional enrichment, immune infiltration analyses, and single-cell transcriptomics were performed. In vitro assays were conducted to validate the biological effects of KANSL2 in LIHC cells. KANSL2 is broadly upregulated across cancers and exhibits strong diagnostic performance. Elevated KANSL2 expression correlates with unfavorable prognosis, particularly in LIHC. Mechanistically, KANSL2 and its co-expressed genes are enriched in cell-cycle progression. KANSL2 expression is also closely associated with immune infiltration and immunoregulatory signaling within the tumor microenvironment, with single-cell data indicating preferential expression in proliferative T-cell subsets. Functional experiments demonstrate that KANSL2 silencing suppresses proliferation, migration, and invasion, and induces G2/M phase arrest in LIHC cells. Notably, its effects on apoptosis are limited, suggesting that KANSL2 primarily drives tumor progression through cell-cycle-dependent mechanisms. This study identifies KANSL2 as a key regulator of tumor progression and immune remodeling in LIHC. By promoting malignancy predominantly via cell-cycle control, KANSL2 represents a promising biomarker for diagnosis and prognosis, and a potential therapeutic target.

Carcinoma, Hepatocellular↗

PubMed2006

Distribution of nitrogen species in groundwater aquifers of an industrial area in alluvial Indo-Gangetic Plains--a case study.

The groundwater samples collected from the shallow and deep groundwater aquifers of an industrial area of the Kanpur city (Uttar Pradesh, India) were analyzed for the concentration levels and distribution pattern of nitrogenous species, such as nitrate-nitrogen (NO(3)-N), nitrite-nitrogen (NO(2)-N), ammonical-nitrogen (NH(4)-N), organic-nitrogen (Org-N) and total Kjeldahl-nitrogen (TKN) to identify the possible contamination source. Geo-statistical approach was adopted to determine the distribution and extent of the contaminant plume. In the groundwater aquifers NO(3)-N, NO(2)-N, NH(4)-N, TKN, Org-N and Total-N ranged from 0.10 to 64.10, BDL (below detection limit)-6.57, BDL-39.00, 7.84-202.16, 1.39-198.97 and 8.89-219.43 mg l(-1), respectively. About 42% and 26% of the groundwater samples of the shallow and deep groundwater aquifers, respectively, exceeded the BIS (Bureau of Indian Standards) guideline value of 10 mg l(-1) for NO(3)-N and may pose serious health hazards to the people of the area. The results of the study revealed that the groundwater aquifers of the study area are highly contaminated with the nitrate and indicates point source pollution of nitrate in the study area.

Environmental Exposure↗

PubMed2006

Analysis of the spatio-temporal and semantic aspects of land-cover/use change dynamics 1991-2001 in Albania at national and district levels.

In the turmoil of a rapidly changing economy the Albanian government needs accurate and timely information for management of their natural resources and formulation of land-use policies. The transformation of the forestry sector has required major changes in the legal, regulatory and management framework. The World Bank financed Albanian National Forest Inventory project provides an analysis of spatially explicit land-cover/use change dynamics in the period 1991-2001 using the FAO/UNEP Land Cover Classification System for codification of classes, satellite remote sensing and field survey for data collection and elements of the object-oriented geo-database approach to handle changes as an evolution of land-cover/use objects, i.e. polygons, over time to facilitate change dynamics analysis. Analysis results at national level show the trend of natural resources depletion in the form of modifications and conversions that lead to a gradual shift from land-cover/use types with a tree cover to less dense tree covers or even a complete removal of trees. Policy failure (e.g., corruption, lack of law enforcement) is seen as the underlying cause. Another major trend is urbanisation of areas near large urban centres that change urban-rural linkages. Furthermore, after privatisation agricultural areas increased in the hills where environmental effects may be detrimental, while prime agricultural land in the plains is lost to urbanisation. At district level, the local variability of spatially explicit land-cover/use changes shows different types of natural resources depletion. The distribution of changes indicates a regional prevalence, thus a decentralised approach to the natural resources management could be advocated.

Albania↗

PubMed2006

Spatial relationships between water quality and pesticide application rates in agricultural watersheds.

Pesticide applications to agricultural lands in California, USA, are reported to a central data base, while data on water and sediment quality are collected by a number of monitoring programs. Data from both sources are geo-referenced, allowing spatial analysis of relationships between pesticide application rates and the chemical and biological condition of water bodies. This study collected data from 12 watersheds, selected to represent a range of pesticide usage. Water quality parameters were measured during six surveys of stream sites receiving runoff from the selected watershed areas. This study had three objectives: to evaluate the usefulness of pesticide application data in selecting regional monitoring sites, to provide information for generating and testing hypotheses about pesticide fate and effects, and to determine whether in-stream nitrate concentration was a useful surrogate indicator for regional monitoring of toxic substances. Significant correlations were observed between pesticide application rates and in-stream pesticide concentrations (p < 0.05) and toxicity (p < 0.10). In-stream nitrate concentrations were not significantly correlated with either the amount of pesticides applied, in-stream pesticide concentrations, or in-stream toxicity (all p > 0.30). Neither total watershed area nor the area in which pesticide usage was reported correlated significantly with the amount of pesticides applied, in-stream pesticide concentrations, or in-stream toxicity (all p > 0.14). In-stream pesticide concentrations and effects were more closely related to the intensity of pesticide use than to the area under cultivation.

Agriculture↗

PubMed2006

Neural network based automated interpretation algorithm for combined geophysical soundings in coastal zones.

The fresh water availability in coastal aquifers is an important problem faced by a major part of world's population dwelling there. For in situ and dynamic characterization of seawater encroachment into coastal aquifers, electrical geophysical methods are better suited. However, the resolving power of such data in distinguishing saline sands from moist clays in the subsurface is very poor. To meet this aspect and also the problem of analyzing voluminous data sets, we propose a feed forward back-propagation neural network (BPNN) based approach for the analysis of combined vertical electrical and induced polarization soundings. Our method is tested on synthetic data computed from available geo-electric sections and prevailing subsurface geological information of coastal aquifers of East Coast of India. The synthetic data are comprised of 18 combined soundings spread over five profiles. 15 out of 18 are used for training the BPNN, while 3 are used for testing. The trained BPNN (one node each in each of the input and output layers and 18 hidden nodes) showed 84.85% accuracy in testing phase for distinguishing clays from saline sands. Our method is also tested on real data concerning a shaly aquifer in Bahia, Brazil yielding an overall accuracy of 84.9%, comparable to that of synthetic case; thereby validating our approach.

Algorithms↗

PubMed2006

Landscape based identification of human disturbance gradients and reference conditions for Michigan streams.

Identification of reference streams and human disturbance gradients are crucial steps in assessing the effects of human disturbances on stream health. We describe a process for identifying reference stream reaches and assessing disturbance gradients using readily available, geo-referenced stream and human disturbance databases. We demonstrate the utility of this process by applying it to wadeable streams in Michigan, USA, and use it to identify which human disturbances have the greatest impact on streams. Approximately 38% of cold-water and 16% of warm-water streams in Michigan were identified as being in least-disturbed condition. Conversely, approximately 3% of cold-water and 4% of warm-water streams were moderately to severely disturbed by landscape human disturbances. Anthropogenic disturbances that had the greatest impact on moderately to severely disturbed streams were nutrient loading and percent urban land use within network watersheds. Our process for assessing stream health represents a significant advantage over other routinely used methods. It uses inter-confluence stream reaches as an assessment unit, permits the evaluation of stream health across large regions, and yields an overall disturbance index that is a weighted sum of multiple disturbance factors. The robustness of our approach is linked to the scale of disturbances that affect a stream; it will be less robust for identifying less degraded or reference streams with localized human disturbances. With improved availability of high-resolution disturbance datasets, this approach will provide a more complete picture of reference stream reaches and factors contributing to degradation of stream health.

Ecosystem↗

PubMed2005

Molecular analysis of divergence in tachinid Uzi (Exorista sorbillans) populations in India.

Exorista sorbillans is a tachinid endoparasitoid of silkworm, Bombyx mori, and is globally known as uzi. It causes economic injury to the cocoon crop in silkworm cultivating areas of India, except those above 400 m above mean sea level (AMSL) in the foothills of the Himalayas (Darjeeling). It is reported that the sericulture tract of south India became infected with this pest only since 1980 through an accidental transportation of cocoons from West Bengal. To ascertain whether the genome of this parasitoid is differentiating into discrete gene pools in contrasting geo-climatic conditions, molecular profiling of four populations (Es (Annatapur), Es(Ramanagaram), Es (Channapatna) and Es(Kodathi) from south India and Es(Murshidabad) from Murshidabad, West Bengal was undertaken with 13 ISSR, 3 RAPD and six non-random primers designed from various repeat sequences of B. mori . MANOVA indicated significance for the Roy's largest root estimate (55.4; F =18.47; p = 0.002) for the variability contributed by the replication. Further, hierarchical clustering done on the basis of Euclidean distance matrix and Nei's unbiased Phylip clustering put Es(Murshidabad) at the maximum distance from those of south India and 29 markers could also be identified which significantly differentiateEs(Murshidabad) from others. However, Nei's statistics for gene diversity in sub-populations reveal considerably high gene-flow (3.44 and 2.51) among the populations around Bangalore. The gene-flow between Es(Murshidabad) and other population is lowest but cannot be ignored. The comparison of endosymbiont specific 16SrRNA and fts Z gene (partial) sequences through clustalW (gcgMSF) revealed a closer relationship of Es(Murshidabad) with Es(Annatapur) and Es (Ramanagaram) and is not congruent with the relationships discussed above. The significance of this maiden study with a tachinid fly-pest is discussed in the context of understanding the diversification of Uzi fly-pest and also establishing this pest as a relevant biological material for studying microevolution in future.

Animals↗

PubMed2005

Cloning of cDNAs with PDCD2(C) domain and their expressions during apoptosis of HEK293T cells.

There are only two isoforms of PDCD2 and MGC13096 containing PDCD2(C) domain in human genome. To study the role of PDCD2_C domain in apoptosis, the cDNAs of two isoforms of PDCD2 and MGC13096 were cloned. The RT-PCR products (AY948416, AY948417) of PDCD2 from RNA of human embryonic kidney 293T (HEK293T) and gastric cancer AGS cell line lost common 99 bp when compared with the sequences of NCBI database (NM_002598, NM_144781). The data of expression of PDCD2 and MGC13096 genes in HEK293T cells which induced to undergo apoptosis by various treatments suggested that there was no significant over-regulation of MGC13096 gene and the over-expression of PDCD2 gene did not occur universally. We searched GEO (Gene Expression Omnibus) about PDCD2 and MGC13096. PDCD2 (NM_002598) was over expressed when endothelial cells treated with leukotriene D4 or natural killer cells were activated by IL-2. Perhaps PDCD2_C domain is not universally associated with apoptosis, the function of PDCD2_C domain needs to be studied further.

Apoptosis↗

PubMed2025

The correlation of DPM1 overexpression with immune infiltration and poor prognosis in hepatocellular carcinoma.

BACKGROUND: The DPM1 gene, crucial for glycosylation processes, has shown abnormal expression in various cancers, raising interest in its potential oncogenic role and as a biomarker in hepatocellular carcinoma (HCC). METHODS: Transcriptomic data were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. DPM1 expression levels were compared between HCC tissues and adjacent normal tissues. Clinical correlations were assessed using statistical analyses, including survival analysis and multivariate Cox regression. Immune microenvironment profiling was conducted to evaluate associations between DPM1 expression and immune cell infiltration patterns. RESULTS: Elevated DPM1 levels were associated with advanced tumor stages (P < 0.001), higher pathologic T stage (P < 0.001), increased histologic grade (P < 0.001), tumor positivity (P < 0.001), tissue inflammation (P < 0.001), and elevated alpha-fetoprotein levels (AFP > 400 ng/mL, P < 0.05). Multivariate Cox regression analysis identified DPM1 as an independent prognostic factor for reduced overall survival (HR = 1.990, 95% CI 1.390-2.848). Immunological analysis revealed that DPM1 expression was positively correlated with T helper cells (R = 0.268, P < 0.001) and Th2 cells (R = 0.295, P < 0.001), and negatively correlated with plasmacytoid dendritic cells (R=-0.291, P < 0.001) and cytotoxic cells (R=-0.284, P < 0.001). CONCLUSIONS: DPM1 serves as a promising prognostic biomarker in HCC, with its expression correlating with unfavorable clinical outcomes and immune landscape alterations. Future studies should further validate DPM1's impact on ferroptosis and immune evasion in HCC, and explore its potential as a therapeutic target.

DPM1↗

PubMed2025

Expression and prognosis of CXCL13 in uterine corpus endometrial carcinoma based on bioinformatics analysis.

OBJECTIVE: The biological significance of the chemokine ligand C-X-C motif chemokine ligand 13 (CXCL13) may play a significant role in the pathogenesis of uterine corpus endometrial carcinoma (UCEC). This study aims to identify and verify CXCL13 with predictive value for prognosis in UCEC. METHODS: CXCL13 mRNA expression differences were analyzed using R software in three independent datasets: one each from The Cancer Genome Atlas (TCGA) and two from the Gene Expression Omnibus (GEO), namely GSE17025 and GSE106191. The correlation between CXCL13 expression and prognosis was evaluated by Kaplan-Meier analysis. Univariate and multivariate Cox analyses were utilized to construct a prognostic nomogram. Tumor Immune Estimation Resource (TIMER) and the Tumor and Immune System Interaction Database (TISIDB) were employed to assess the relationship between CXCL13 and tumor immune infiltration. Coexpressed genes with CXCL13 were identified by the Spearman correlation analysis. A CXCL13 protein-protein interaction (PPI) network was constructed with the STRING website tool and hub genes were screened out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses were performed with the "clusterProfiler" R package. Gene set enrichment analysis (GSEA) was used to identify underlying biological mechanisms. A drug-gene interaction network was constructed in the Comparative Toxicogenomics Database (CTD). RESULTS: High CXCL13 mRNA expression were validated in UCEC in the above three independent datasets. High CXCL13 expression was associated with favorable prognosis in UCEC. A nomogram for predicting the 1-, 3-, and 5-year survival probability in UCEC was construct based on CXCL13 expression and other clinical parameters. The use of Spearman correlation indicated certain correlation between CXCL13 and immune cells and immune checkpoint (ICP) genes. Seven hub genes were upregulated in UCEC, namely CXCL9, IFNG, CXCL10, CXCL11, GBP5, CCL18, and GZMB. The expression and prognostic relevance of CXCL9, IFNG, GBP5, and GZMB were in accordance with CXCL13. The main biological processes enriched were cytokine-cytokine receptor interaction and chemokine signaling pathway. CONCLUSIONS: The above comprehensive analyses suggest that CXCL13 may serve as a potential prognostic biomarker for UCEC, specifically for early-stage UCEC.

CXCL13↗

PubMed2026

Multi-cohort integration and machine learning identify CPVL as a novel oncogenic driver in gastric cancer.

BACKGROUND: Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide, and the prognosis of advanced GC remains poor. Systematic identification of robust biomarkers through multi-cohort integration and computational prioritization may facilitate the discovery of novel therapeutic targets. AIM: To identify key genes associated with gastric cancer progression through integrative multi-omics analysis and to elucidate the biological functions and molecular mechanisms of the top-prioritized candidate gene. METHODS: Comprehensive bioinformatics analyses integrating The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) datasets were performed using differential expression analysis, weighted gene co-expression network analysis (WGCNA), Cox regression, and eight machine-learning algorithms to systematically identify and prioritize GC-associated hub genes. Among the identified candidates, CPVL was selected for further validation based on its diagnostic and prognostic performance. CPVL expression and clinical relevance were validated by independent datasets and immunohistochemistry. Lentiviral constructs were used to overexpress or silence CPVL in GC cell lines. Functional assays were performed, including CCK-8, colony formation, EdU incorporation, and flow cytometry, to assess cell proliferation and cell-cycle distribution. Western blotting and JAK2 inhibitor (AZD1480) rescue experiments were performed to elucidate the underlying mechanisms, and a nude mouse xenograft model was used to evaluate tumorigenicity in vivo. RESULTS: Multi-cohort screening identified five hub genes (CPVL, AADAC, BCAT1, CPXM1, and FBN1). Among them, CPVL exhibited the highest diagnostic accuracy (AUC = 0.895) and the strongest correlation with poor overall survival, and was therefore selected for mechanistic investigation. CPVL expression was markedly upregulated in GC tissues and cell lines. Functional assays demonstrated that CPVL promotes GC cell proliferation and accelerates G1/S-phase transition. Mechanistically, CPVL activated the JAK2/STAT3 signaling pathway, upregulating Cyclin D1 and CDK4 while downregulating p27. Treatment with the JAK2 inhibitor AZD1480 partially reversed these effects. In vivo, CPVL knockdown significantly inhibited tumor growth. CONCLUSION: Through systematic multi-cohort integration and machine-learning prioritization, CPVL was identified as a novel oncogenic driver in gastric cancer. CPVL promotes tumor growth via activation of the JAK2/STAT3 pathway and regulation of the Cyclin D1/CDK4/p27 axis, highlighting its potential as a diagnostic biomarker and therapeutic target.

Biomarker↗

PubMed2026

A novel lactylation-related gene signature deciphers the immunosuppressive microenvironment and stratifies precision therapy in colorectal cancer.

BACKGROUND: Colorectal cancer (CRC) remains a leading cause of cancer mortality, largely due to the heterogeneity of the tumor microenvironment (TME) and the limited efficacy of immunotherapy in microsatellite stable (MSS) tumors. Histone lactylation, a post-translational modification derived from the Warburg effect, serves as a critical bridge linking metabolic reprogramming to gene regulation and immune evasion; however, its specific prognostic value and clinical implications in CRC remain to be fully elucidated. METHODS: In this study, we systematically analyzed transcriptome profiling data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts, supplemented by single-cell RNA sequencing (scRNA-seq) analysis and Human Protein Atlas (HPA) protein-level validation. By integrating univariate Cox regression, Least Absolute Shrinkage and Selection Operator (LASSO) analysis, and multivariate Cox regression, we constructed a novel lactylation-related gene (LRG) risk signature. We extensively evaluated the association between this risk signature and patient prognosis, immune infiltration patterns, somatic mutations, and therapeutic sensitivity. RESULTS: A robust 9-gene prognostic signature (DHRS7, SPR, MBD2, RBM17, CSRP2, S100A4, TMSB4X, TKT, COPS4) was identified and corroborated at the protein level. Patients with high risk scores exhibited significantly worse overall survival (OS) across the training and two independent validation cohorts. Immunogenomic and scRNA-seq analyses revealed that high-risk tumors were characterized by an immunosuppressive and stromal-dense microenvironment-with stromal cells exhibiting the highest lactylation risk scores-enriched with regulatory T cells (Tregs), and frequently harbored PIK3CA mutations. Differential expression analysis indicated that this immune exclusion is structurally maintained by enriched extracellular matrix (ECM) organization and TGF-β signaling. Conversely, low-risk tumors displayed an inflamed phenotype with active antitumor immunity. Pharmacogenomic prediction identified distinct therapeutic stratifications: low-risk patients exhibited significant sensitivity to standard chemotherapeutics (fluorouracil, oxaliplatin) and EGFR/HER2 inhibitors (e.g., lapatinib, erlotinib). In contrast, high-risk patients showed specific vulnerabilities to novel targeted agents, including PI3K pathway inhibitors (TG-100-115, XL765), microenvironment-modulating agents (sildenafil, GANT-61), and epigenetic inhibitors (UNC0638). CONCLUSION: We established a novel lactylation-related risk signature that effectively stratifies CRC patients by prognosis and TME characteristics. By elucidating the crosstalk between metabolic dysregulation, stromal barriers, and immune exclusion, this study provides potential biomarkers and stratified therapeutic strategies-ranging from standard chemotherapy to targeted metabolic and stromal interventions-to optimize precision medicine for CRC patients.

Colorectal cancer↗

PubMed2026

Screening and identification of key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods.

OBJECTIVE: Radiotherapy (RT) plays a crucial role in the comprehensive treatment of rectal cancer. However, the impact of radiotherapy on the tumor microenvironment (TME), especially its effect on immune cell infiltration and immune-related gene expression, has not been fully studied. This study aims to screen and analyze key genes related to the immune microenvironment of rectal cancer influenced by radiotherapy based on bioinformatics methods for the purpose of identifying potential biomarkers and providing new insights for the personalized therapy of rectal cancer. METHODS: Using data from the Public Gene Expression Database (GEO) and the Cancer Genomics Database (TCGA), the impact of radiotherapy on the immune microenvironment of rectal cancer was explored using bioinformatics tools. Through screening differentially expressed genes (DEGs), correlation analysis, TIMER database analysis, immune infiltration score, and correlation analysis between key genes and prognosis, the effects of radiotherapy on the immune microenvironment of rectal cancer were investigated. RESULTS: Totally 7 upregulated and 4 downregulated differentially expressed genes were identified, among which MASP1, LTK, SLC9A3R2 were negatively correlated with myeloid suppressor cell infiltration (MDSCs), while ZP2 was positively correlated. The expression of MASP1 and SLC9A3R2 was closely related to the level of immune cell infiltration and played significant roles in the immune microenvironment. High expression of MASP1 was significantly correlated with survival benefits from immune checkpoint inhibitor therapy, while SLC9A3R2 was closely related to the efficacy of PD-L1 inhibitors and CTLA4 inhibitors. CONCLUSIONS: MASP1 and SLC9A3R2, as two key genes that may be related to the immune microenvironment of rectal cancer radiotherapy, deserve further exploration of their roles in the mechanism. The combination of radiotherapy and immunotherapy holds promising prospects in the treatment of rectal cancer, and exploration of related mechanisms will provide new strategies and targets for the treatment of various tumors and rectal cancer.

Bioinformatics↗

PubMed2026

Integrative analysis of the roles and prognostic value of RNA-binding proteins in papillary renal cell carcinoma.

RNA-binding proteins (RBPs) serve essential roles in various cancer types, but their functions in papillary renal cell carcinoma (pRCC) have not been elucidated to date. In our work, differentially expressed RBPs in pRCC were identified after acquisition of RNA-sequencing and clinical data related to pRCC from The Cancer Genome Atlas database(TCGA). Functional enrichment analysis and protein interaction network analysis, along with univariate and multivariate Cox regression analyses, were performed to uncover potential biological effects of the identified RBPs and screen the hub RBPs for pRCC prognosis. We identified 251 up-regulated and 129 down-regulated RBPs, and filtered out seven hub RBPs, namely, SRSF8, CD3EAP, HBS1L, ELAC2, MRPL34, NOP2 and IGF2BP2, for their prognostic relevance. A prognostic risk score model for overall survival of pRCC patients was constructed based on the seven hub RBPs. Further analysis showed that the low-risk group had higher survival rate than the high-risk group in both training and validation cohorts. The predictive accuracy was verified in the Human Protein Atlas database.In addition, we introduced the GSE15641 dataset from the Gene Expression Omnibus (GEO) database for independent external validation, and confirmed the expression levels of HBS1L, MRPL34 and IGF2BP2 through real-time quantitative PCR (RT-qPCR) and Western blotting (WB) using human renal tubular epithelial cell line HK-2 and human papillary renal cell carcinoma cell line Caki-2. In pRCC, CD3EAP was significantly elevated, while ELAC2, IGF2BP2, MRPL34, SRSF8 and HBS1L were significantly reduced. There was no significant difference between tumor and normal tissues in NOP2 expression. Risk score and tumor grade were independent prognostic factors associated with overall survival. In addition, we established a nomogram based on the seven prognostic RBPs to help predict overall survival at 1-3 years. In conclusion, seven differentially expressed hub RBPs were identified as potential prognostic biomarkers for pRCC. Our prognostic model might serve as a support for better treatment decision-making. Our work could provide potential new ideas for diagnosis and research on targeted drugs for pRCC.

Bioinformatics↗

PubMed2026

Expression of PIEZO1 in lung adenocarcinoma correlates with PD-L1 expression, cell migration, and poor prognosis: an exploratory study.

BACKGROUND AND AIMS: Lung adenocarcinoma (LUAD) treatment is challenging process. and the function of PIEZO1, a mechanically sensitive ion channel has not been systematically determined. In this study we aimed to explore the expression, potential associations, and clinical significance of PIEZO1 in LUAD. METHODS AND AIMS: A comprehensive bioinformatics analysis was performed using data from the Cancer Genome Atlas (TCGA) database, the Gene Expression Omnibus (GEO) database and other databases. Experimental validation was performed to confirm the expression patterns and preliminarily examine the associations of PIEZO1 in LUAD cell lines. RESULTS: PIEZO1 expression was significantly lower in LUAD tissues than in normal lung tissues (P < 0.05). Its expression was correlated with advanced pathological stage, lymph node involvement, and distant metastasis. High PIEZO1 expression was associated with a distinct immune-related tumor microenvironment, characterized by correlations with the expression levels of multiple immune checkpoint molecules, and was identified as an independent factor associated with poor overall survival (HR = 1.49; 95% CI 1.11-2; P < 0.007). In vitro experiments confirmed the downregulated PIEZO1 expression in LUAD cell lines, and functional knockdown experiments revealed its association with cell migration and PD-L1 expression. CONCLUSION: This exploratory study revealed that PIEZO1 expression in LUAD cell lines correlated with the expression of immune-related features and EMT-related genes, as well as poor prognosis. In vitro, PIEZO1 knockdown is associated with reduced cell migration and decreased PD-L1 expression. These findings provide a basis for future investigations into the potential role of PIEZO1 in LUAD.

Gene expression↗

PubMed2026

Exploring shared biomarkers and their mechanisms in thyroid cancer and systemic lupus erythematosus via bioinformatics analysis.

BACKGROUND: Systemic lupus erythematosus (SLE), an autoimmune disorder, is linked to a heightened risk of multiple malignancies, including thyroid cancer. Thyroid cancer is the most prevalent malignancy of the endocrine system, and its autoimmune-related pathological features render it an optimal subject for investigating the mechanisms of their comorbidity. The molecular mechanisms underlying this comorbidity are still ambiguous. The accurate diagnosis and treatment of thyroid cancer urgently necessitate innovative molecular targets that extend beyond conventional pathological characteristics. This study seeks to employ integrated bioinformatics approaches to elucidate potential shared molecular mechanisms and immunological features between thyroid cancer and systemic lupus erythematosus (SLE), aiming to enhance understanding of their comorbidity and identify novel intervention targets. METHODS: This study initially acquired gene expression data for TC and SLE from the GEO database and subsequently screened and identified differentially expressed genes (DEGs) shared by both diseases. Subsequently, we conducted Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Reactome functional enrichment analyses on these 46 shared differentially expressed genes (DEGs) and further assessed the activation status of pertinent pathways using Gene Set Enrichment Analysis (GSEA). Subsequently, we employed CIBERSORTx to examine immune infiltration patterns and developed protein-protein interaction networks utilising the STRING database. We identified hub genes utilising the MCODE and cytoHubba plugins and visualised the findings with Cytoscape software. We additionally assessed the diagnostic efficacy of these core hub genes in an independent dataset utilising ROC curves and investigated their prognostic relevance in thyroid cancer through Kaplan-Meier survival analysis and multivariate Cox proportional hazards regression. Ultimately, we employed the Network Analyst platform to forecast transcription factor-gene and miRNA-gene regulatory networks and identified potential targeted therapeutic compounds utilising the DSigDB database. RESULTS: This study identified 46 differentially expressed genes (DEGs) commonly linked to thyroid cancer and systemic lupus erythematosus (SLE), which were significantly enriched in signalling pathways associated with immune-inflammatory activation, type I interferon responses, and complement pathway activation. Moreover, GSEA findings validated that immune-inflammatory and autoimmune-related pathways are markedly activated in both conditions. Twelve hub genes were discerned through protein-protein interaction networks. Analysis of immune infiltration indicated that thyroid cancer and systemic lupus erythematosus exhibit a shared characteristic of innate immune dysregulation, marked by the infiltration of myeloid cells (neutrophils, M0/M2 macrophages). Receiver operating characteristic (ROC) curve analysis identified six significant core hub genes with substantial diagnostic value: C1QB, LCN2, C1QC, LTF, VSIG4, and C3AR1. Univariate survival analysis indicated that elevated expression of C1QC and C3AR1 significantly enhances overall survival in thyroid cancer patients; however, multivariate COX regression analysis revealed that their independent prognostic significance necessitates further validation. This study predicted the interaction networks of transcription factors and miRNAs regulating key genes, with LCN2 demonstrating the highest connectivity to miRNAs, and identified candidate therapeutic compounds linked to it. CONCLUSION: This study employed bioinformatics analysis to identify critical shared hub genes and molecular pathways connecting thyroid cancer and systemic lupus erythematosus, offering novel insights into their shared pathogenesis and the advancement of targeted biomarkers and therapeutic strategies.

Bioinformatics analysis↗

PubMed2026

Quantum computing-assisted validation of a conserved macrophage suppression module shared by ASFV and PEDV.

BACKGROUND: African swine fever virus (ASFV) and porcine epidemic diarrhea virus (PEDV) differ in viral biology and cellular tropism, yet both pathogens suppress macrophage-mediated immune responses in pigs. OBJECTIVE: To identify a conserved macrophage suppression module shared by ASFV and PEDV and evaluate quantum computing as an independent framework for biological network validation. METHODS: Integrated analysis of publicly available GEO datasets (GSE231435 for ASFV and GSE306895) identified 471 shared downregulated genes. A network- and multi-omics-informed 20-gene core was selected and encoded as a 20-qubit modularity-based Quadratic Unconstrained Binary Optimization (QUBO) problem. Community detection was benchmarked using the Quantum Approximate Optimization Algorithm (QAOA) on both the IBM Quantum Aer simulator and the 156-qubit IBM Fez (Heron r2) quantum processor and compared with brute-force enumeration and simulated annealing. RESULTS: A conserved macrophage suppression module shared by ASFV and PEDV was identified. For the STRING protein-protein interaction network, QAOA at circuit depth p = 3 reproduced the brute-force optimum with an approximation ratio of 1.000. In contrast, performance progressively declined in the denser co-expression network with increasing circuit depth, consistent with noise accumulation under current Noisy Intermediate-Scale Quantum (NISQ) conditions. Multi-run consensus analysis identified stable hub genes, including MMP9 and SLA-DOA, as well as genes exhibiting variable community assignments. CONCLUSION: These findings reveal a conserved macrophage suppression module shared between ASFV and PEDV and demonstrate that quantum computing can serve as an independent validation framework for biologically meaningful host-response networks. Network topology emerged as a key determinant of QAOA performance on real NISQ hardware.

Animals↗

PubMed1995

Spatial analysis of Honolulu motor vehicle crashes: I. Spatial patterns.

This study describes spatial patterns in Honolulu motor vehicle accidents for 1990. A method for geo-coding accident locations is utilized with approximately 98% of the crash locations being identified. Spatial software tools are developed for describing the degree of spatial concentration. The spatial patterns of different types of accidents and accidents for every hour of the day, weekdays and weekends separately, are analyzed. Accidents spatially fluctuate dynamically, as a response to changing traffic patterns and volume. Generally, most accidents are closer to employment centers than to residential areas. In the suburban and rural areas, however, accidents are more likely to involve fatalities or serious injuries and be related to night-time driving and alcohol. It is shown that these conditions spatially correlate with single-vehicle crashes and crashes with opposite direction vehicles. The spatial patterns point to the limits of "blackspot" analysis.

Accidents, Traffic↗