Integrated Proteogenomic Characterization of Clear Cell Renal Cell Carcinoma.

To elucidate the deregulated functional modules that drive clear cell renal cell carcinoma (ccRCC), we performed comprehensive genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic characterization of treatment-naive ccRCC and paired normal adjacent tissue samples. Genomic analyses identified a distinct molecular subgroup associated with genomic instability. Integration of proteogenomic measurements uniquely identified protein dysregulation of cellular mechanisms impacted by genomic alterations, including oxidative phosphorylation-related metabolism, protein translation processes, and phospho-signaling modules. To assess the degree of immune infiltration in individual tumors, we identified microenvironment cell signatures that delineated four immune-based ccRCC subtypes characterized by distinct cellular pathways. This study reports a large-scale proteogenomic analysis of ccRCC to discern the functional impact of genomic alterations and provides evidence for rational treatment selection stemming from ccRCC pathobiology.

Comprehensive and Integrated Genomic Characterization of Adult Soft Tissue Sarcomas.

Sarcomas are a broad family of mesenchymal malignancies exhibiting remarkable histologic diversity. We describe the multi-platform molecular landscape of 206 adult soft tissue sarcomas representing 6 major types. Along with novel insights into the biology of individual sarcoma types, we report three overarching findings: (1) unlike most epithelial malignancies, these sarcomas (excepting synovial sarcoma) are characterized predominantly by copy-number changes, with low mutational loads and only a few genes (TP53, ATRX, RB1) highly recurrently mutated across sarcoma types; (2) within sarcoma types, genomic and regulomic diversity of driver pathways defines molecular subtypes associated with patient outcome; and (3) the immune microenvironment, inferred from DNA methylation and mRNA profiles, associates with outcome and may inform clinical trials of immune checkpoint inhibitors. Overall, this large-scale analysis reveals previously unappreciated sarcoma-type-specific changes in copy number, methylation, RNA, and protein, providing insights into refining sarcoma therapy and relationships to other cancer types.

Germline polygenic risk scores are associated with immune gene expression signature and immune cell infiltration in breast cancer.

The tumor immune microenvironment (TIME) plays key roles in tumor progression and response to immunotherapy. Previous studies have identified individual germline variants associated with differences in TIME. Here, we hypothesize that common variants associated with breast cancer risk or cancer-related traits, represented by polygenic risk scores (PRSs), may jointly influence immune features in TIME. We derived 154 immune traits from bulk gene expression profiles of 764 breast tumors and 598 adjacent normal tissue samples from 825 individuals with breast cancer in the Nurses' Health Study (NHS) and NHSII. Immunohistochemical staining of four immune cell markers were available for a subset of 205 individuals. Germline PRSs were calculated for 16 different traits including breast cancer, autoimmune diseases, type 2 diabetes, ages at menarche and menopause, body mass index (BMI), BMI-adjusted waist-to-hip ratio, alcohol intake, and tobacco smoking. Overall, we identified 44 associations between germline PRSs and immune traits at false discovery rate q < 0.25, including 3 associations with q < 0.05. We observed consistent inverse associations of inflammatory bowel disease (IBD) and Crohn disease (CD) PRSs with interferon signaling and STAT1 scores in breast tumor and adjacent normal tissue; these associations were replicated in a Norwegian cohort. Inverse associations were also consistently observed for IBD PRS and B cell abundance in normal tissue. We also observed positive associations between CD PRS and endothelial cell abundance in tumor. Our findings suggest that the genetic mechanisms that influence immune-related diseases are also associated with TIME in breast cancer.

SpatialCells: automated profiling of tumor microenvironments with spatially resolved multiplexed single-cell data.

Cancer is a complex cellular ecosystem where malignant cells coexist and interact with immune, stromal and other cells within the tumor microenvironment (TME). Recent technological advancements in spatially resolved multiplexed imaging at single-cell resolution have led to the generation of large-scale and high-dimensional datasets from biological specimens. This underscores the necessity for automated methodologies that can effectively characterize molecular, cellular and spatial properties of TMEs for various malignancies. This study introduces SpatialCells, an open-source software package designed for region-based exploratory analysis and comprehensive characterization of TMEs using multiplexed single-cell data. The source code and tutorials are available at https://semenovlab.github.io/SpatialCells. SpatialCells efficiently streamlines the automated extraction of features from multiplexed single-cell data and can process samples containing millions of cells. Thus, SpatialCells facilitates subsequent association analyses and machine learning predictions, making it an essential tool in advancing our understanding of tumor growth, invasion and metastasis.

Development of a TMErisk model based on immune infiltration in tumour microenvironment to predict prognosis of immune checkpoint inhibitor treatment in hepatocellular carcinoma.

Immune checkpoint inhibitor (ICI) treatment has created the opportunity of improved outcome for patients with hepatocellular carcinoma (HCC). However, only a minority of HCC patients benefit from ICI treatment owing to poor treatment efficacy and safety concerns. There are few predictive factors that precisely stratify HCC responders to immunotherapy. In this study, we developed a tumour microenvironment risk (TMErisk) model to divide HCC patients into different immune subtypes and evaluated their prognosis. Our results indicated that virally mediated HCC patients who had more common tumour protein P53 (TP53) alterations with lower TMErisk scores were appropriate for ICI treatment. HCC patients with alcoholic hepatitis who more commonly harboured catenin beta 1 (CTNNB1) alterations with higher TMErisk scores could benefit from treatment with multi-tyrosine kinase inhibitors. The developed TMErisk model represents the first attempt to anticipate tumour tolerance of ICIs in the TME through the degree of immune infiltration in HCCs.

MrGPS: an m6A-related gene pair signature to predict the prognosis and immunological impact of glioma patients.

N6-methyladenosine (m6A) RNA methylation is the predominant epigenetic modification for mRNAs that regulates various cancer-related pathways. However, the prognostic significance of m6A modification regulators remains unclear in glioma. By integrating the TCGA lower-grade glioma (LGG) and glioblastoma multiforme (GBM) gene expression data, we demonstrated that both the m6A regulators and m6A-target genes were associated with glioma prognosis and activated various cancer-related pathways. Then, we paired m6A regulators and their target genes as m6A-related gene pairs (MGPs) using the iPAGE algorithm, among which 122 MGPs were significantly reversed in expression between LGG and GBM. Subsequently, we employed LASSO Cox regression analysis to construct an MGP signature (MrGPS) to evaluate glioma prognosis. MrGPS was independently validated in CGGA and GEO glioma cohorts with high accuracy in predicting overall survival. The average area under the receiver operating characteristic curve (AUC) at 1-, 3- and 5-year intervals were 0.752, 0.853 and 0.831, respectively. Combining clinical factors of age and radiotherapy, the AUC of MrGPS was much improved to around 0.90. Furthermore, CIBERSORT and TIDE algorithms revealed that MrGPS is indicative for the immune infiltration level and the response to immune checkpoint inhibitor therapy in glioma patients. In conclusion, our study demonstrated that m6A methylation is a prognostic factor for glioma and the developed prognostic model MrGPS holds potential as a valuable tool for enhancing patient management and facilitating accurate prognosis assessment in cases of glioma.

Identification of immune-related gene signatures for chronic obstructive pulmonary disease with metabolic syndrome: evidence from integrated bulk and single-cell RNA sequencing data.

Chronic obstructive pulmonary disease (COPD) is closely related to innate and adaptive inflammatory immune responses. It is increasingly becoming evident that metabolic syndrome (MetS) affects a significant portion of COPD patients. Through this investigation, we identify shared immune-related candidate biological markers. The Weighted Gene Co-Expression Network Analysis (WGCNA) was utilized to reveal the co-expression modules linked to COPD and MetS. The commonly expressed genes in the COPD and MetS were utilized to conduct an enrichment analysis. We adopted machine-learning to screen and validate hub genes. We also assessed the relationship between hub genes and immune cell infiltration in COPD and MetS, respectively. Moreover, associations across hub genes and metabolic pathways were also explored. Finally, we chose a single-cell RNA sequencing (scRNA-seq) dataset to investigate the hub genes and shared mechanisms at the level of the cells. We also applied cell trajectory analysis and cell-cell communication analysis to focus on the vital immune cell we were interested in. As a result, we selected and validated 13 shared hub genes for COPD and MetS. The enrichment analysis and immune infiltration analysis illustrated strong associations between hub genes and immunology. Additionally, we applied metabolic pathway enrichment analysis, indicating the significant role of reactive oxygen species (ROS) in COPD with MetS. Through scRNA-seq analysis, we found that ROS might accumulate the most in the alveolar macrophages. In conclusion, the 13 hub genes related to the immune response and metabolism may serve as diagnostic biomarkers and treatment targets of COPD with MetS.

Comprehensive bioinformatics analysis of human cytomegalovirus pathway genes in pan-cancer.

BACKGROUND: Human cytomegalovirus (HCMV) is a herpesvirus that can infect various cell types and modulate host gene expression and immune response. It has been associated with the pathogenesis of various cancers, but its molecular mechanisms remain elusive. METHODS: We comprehensively analyzed the expression of HCMV pathway genes across 26 cancer types using the Cancer Genome Atlas (TCGA) and The Genotype-Tissue Expression (GTEx) databases. We also used bioinformatics tools to study immune invasion and tumor microenvironment in pan-cancer. Cox regression and machine learning were used to analyze prognostic genes and their relationship with drug sensitivity. RESULTS: We found that HCMV pathway genes are widely expressed in various cancers. Immune infiltration and the tumor microenvironment revealed that HCMV is involved in complex immune processes. We obtained prognostic genes for 25 cancers and significantly found 23 key genes in the HCMV pathway, which are significantly enriched in cellular chemotaxis and synaptic function and may be involved in disease progression. Notably, CaM family genes were up-regulated and AC family genes were down-regulated in most tumors. These hub genes correlate with sensitivity or resistance to various drugs, suggesting their potential as therapeutic targets. CONCLUSIONS: Our study has revealed the role of the HCMV pathway in various cancers and provided insights into its molecular mechanism and therapeutic significance. It is worth noting that the key genes of the HCMV pathway may open up new doors for cancer prevention and treatment.

Comprehensive analysis of zinc and ring finger 3 in prognostic value and pan-cancer immunity.

Zinc and ring finger 3 (ZNRF3) is a negative suppressor of Wnt signal and newly identified as an important regulator in tumorigenesis and development. However, the pan-cancer analysis of ZNRF3 has not been reported. We found that ZNRF3 was significantly decreased in six tumors including CESC, KIRP, KIRC, SKCM, OV, and ACC, but increased in twelve tumors, namely LGG, ESCA, STES, COAD, STAD, LUSC, LIHC, THCA, READ, PAAD, TGCT, and LAML. Clinical outcomes of cancer patients were closely related to ZNRF3 expression in ESCA, GBM, KIRC, LUAD, STAD, UCEC, LGG, and SARC. The highest genetic alteration frequency of ZNRF3 occurred in ACC. Abnormal expression of ZNRF3 could be attributed to the differences of copy number variation (CNV) and DNA methylation as well as ZNRF3-interacting proteins. Besides, ZNRF3 were strongly associated with tumor heterogeneity, tumor stemness, immune score, stromal score and ESTIMATE score in certain cancers. In terms of immune cell infiltration, ZNRF3 was positively correlated to infiltration of cancer-associated fibroblasts in CESC, HNSC, OV, PAAD, PRAD, and THYM, but negatively associated with infiltration of CD8 T cells in HNSC, KIRC, KIRP and THYM. Moreover, ZNRF3 expression was correlated with most immune checkpoint genes in SARC, LUSC, LUAD, PRAD, THCA, UVM, TGCT, and OV, and associated with overwhelming majority of immunoregulatory genes in almost all cancers. Most RNA modification genes were also remarkably related to ZNRF3 level in KIRP, LUAD, LUSC, THYM, UVM, PRAD, and UCEC, indicating that ZNRF3 might have an important effect on cancer epigenetic regulation. Finally, we verified the expression and role of ZNRF3 in clinical specimens and cell lines of renal cancer and liver cancer. This study provides a comprehensive pan-cancer analysis of ZNRF3 and reveals the complexity of its carcinogenic effect.

Systematic pan-cancer analysis insights into ICAM1 as an immunological and prognostic biomarker.

Intercellular adhesion molecule 1 (ICAM1) is a cell surface adhesion glycoprotein in the immunoglobulin supergene family. It is associated with several epithelial tumorigenesis processes, as well as with inflammation. However, the function of ICAM1 in the prognosis of tumor immunity is still unclear. This study aimed to examine the immune function of ICAM1 in 33 tumor types and to investigate the prognostic value of tumors. Using datasets from the Cancer Genome Atlas (TCGA), Genotype Tissue Expression (GTEx), Cancer Cell Lines Encyclopedia (CCLE), Human Protein Atlas (HPA), and cBioPortal, we investigated the role of ICAM1 in tumors. We explored the potential correlation between ICAM1 expression and tumor prognosis, gene mutations, microsatellite instability, and tumor immune cell levels in various cancers. We observed that ICAM1 is highly expressed in multiple malignant tumors. Furthermore, ICAM1 is negatively or positively associated with different malignant tumor prognoses. The expression levels of ICAM1 were correlated with the tumor mutation burden (TMB) in 11 tumors and with MSI in eight tumors. ICAM1 is a gene associated with immune infiltrating cells, such as M1 macrophages and CD8+ T cells in gastric and colon cancer. Meanwhile, the expression of ICAM1 is associated with several immune-related functions and immune-regulation-related signaling pathways, such as the chemokine signaling pathway. Our study shows that ICAM1 can be used as a prognostic biomarker in many cancer types because of its function in tumorigenesis and malignant tumor immunity.

Novel ferroptosis gene biomarkers and immune infiltration profiles in diabetic kidney disease via bioinformatics.

Diabetic kidney disease (DKD) is the primary cause of end-stage renal disease, exhibiting high disability and mortality rates. Ferroptosis is vital for the progression of DKD, but the exact mechanism remains unclear. This study aimed to explore the potential mechanism of ferroptosis-related genes in DKD and their relationship with the immune and to identify new diagnostic biomarkers to help treat and diagnose DKD. GSE30122 and GSE47185 were obtained from the Gene Expression Omnibus database and were integrated into a merged dataset, followed by functional enrichment analysis. Then potential differentially expressed genes were screened. Ferroptosis-related differentially-expressed genes were identified, followed by gene ontology analysis. Protein-protein interaction networks were constructed and hub genes were screened. The immune cell-infiltrating state in the dataset was assessed using appropriate algorithms. Immune signature subtypes were constructed using the consensus clustering analysis. Hub gene expression was validated using qRT-PCR and immunohistochemistry. A total of Eleven screened ferroptosis-related differentially expressed genes were screened. Six potentially diagnostically favorable ferroptosis-related hub genes were identified. Significantly increased expression of γδT cells, resting mast cells, and macrophages infiltration was observed in the DKD group. Additionally, two distinct immune signature subgroups were identified. Ferroptosis-related hub genes were significantly correlated with differentially infiltrated immune cells. Six hub genes were significantly upregulated in HK-2 cells following high glucose treatment and in human kidney tissues of patients with DKD. Six ferroptosis-related hub genes were identified as potential biomarkers of diabetic kidney disease, but further validation is needed.

Dissecting the immune infiltrate of primary luminal B-like breast carcinomas in relation to age.

The impact of aging on the immune landscape of luminal breast cancer (Lum-BC) is poorly characterized. Understanding the age-related dynamics of immune editing in Lum-BC is anticipated to improve the therapeutic benefit of immunotherapy in older patients. To this end, here we applied the 'multiple iterative labeling by antibody neo-deposition' (MILAN) technique, a spatially resolved single-cell multiplex immunohistochemistry method. We created tissue microarrays by sampling both the tumor center and invasive front of luminal breast tumors collected from a cohort of treatment-naïve patients enrolled in the prospective monocentric IMAGE (IMmune system and AGEing) study. Patients were subdivided into three nonoverlapping age categories (35-45 = 'young', n = 12; 55-65 = 'middle', n = 15; ≥70 = 'old', n = 26). Additionally, depending on localization and amount of cytotoxic T lymphocytes, the tumor immune types 'desert' (n = 22), 'excluded' (n = 19), and 'inflamed' (n = 12) were identified. For the MILAN technique we used 58 markers comprising phenotypic and functional markers allowing in-depth characterization of T and B lymphocytes (T&B-lym). These were compared between age groups and tumor immune types using Wilcoxon's test and Pearson's correlation. Cytometric analysis revealed a decline of the immune cell compartment with aging. T&B-lym were numerically less abundant in tumors from middle-aged and old compared to young patients, regardless of the geographical tumor zone. Likewise, desert-type tumors showed the smallest immune-cell compartment and were not represented in the group of young patients. Analysis of immune checkpoint molecules revealed a heterogeneous geographical pattern of expression, indicating higher numbers of PD-L1 and OX40-positive T&B-lym in young compared to old patients. Despite the numerical decline of immune infiltration, old patients retained higher expression levels of OX40 in T helper cells located near cancer cells, compared to middle-aged and young patients. Aging is associated with important numerical and functional changes of the immune landscape in Lum-BC. © 2024 The Author(s). The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Identification of target genes co-regulated by four key histone modifications of five key regions in hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is a cancer with high morbidity and mortality. Studies have shown that histone modification plays an important regulatory role in the occurrence and development of HCC. However, the specific regulatory effects of histone modifications on gene expression in HCC are still unclear. This study focuses on HepG2 cell lines and hepatocyte cell lines. First, the distribution of histone modification signals in the two cell lines was calculated and analyzed. Then, using the random forest algorithm, we analyzed the effects of different histone modifications and their modified regions on gene expression in the two cell lines, four key histone modifications (H3K36me3, H3K4me3, H3K79me2, and H3K9ac) and five key regions that co-regulate gene expression were obtained. Subsequently, target genes regulated by key histone modifications in key regions were screened. Combined with clinical data, Cox regression analysis and Kaplan-Meier survival analysis were performed on the target genes, and four key target genes (CBX2, CEBPZOS, LDHA, and UMPS) related to prognosis were identified. Finally, through immune infiltration analysis and drug sensitivity analysis of key target genes, the potential role of key target genes in HCC was confirmed. Our results provide a theoretical basis for exploring the occurrence of HCC and propose potential biomarkers associated with histone modifications, which may be potential drug targets for the clinical treatment of HCC.

Microfluidics guided by deep learning for cancer immunotherapy screening.

Immunocyte infiltration and cytotoxicity play critical roles in both inflammation and immunotherapy. However, current cancer immunotherapy screening methods overlook the capacity of the T cells to penetrate the tumor stroma, thereby significantly limiting the development of effective treatments for solid tumors. Here, we present an automated high-throughput microfluidic platform for simultaneous tracking of the dynamics of T cell infiltration and cytotoxicity within the 3D tumor cultures with a tunable stromal makeup. By recourse to a clinical tumor-infiltrating lymphocyte (TIL) score analyzer, which is based on a clinical data-driven deep learning method, our platform can evaluate the efficacy of each treatment based on the scoring of T cell infiltration patterns. By screening a drug library using this technology, we identified an epigenetic drug (lysine-specific histone demethylase 1 inhibitor, LSD1i) that effectively promoted T cell tumor infiltration and enhanced treatment efficacy in combination with an immune checkpoint inhibitor (anti-PD1) in vivo. We demonstrated an automated system and strategy for screening immunocyte-solid tumor interactions, enabling the discovery of immuno- and combination therapies.

Identification and validation of CRLF1 and NRG1 as immune-related signatures in hypertrophic scar.

BACKGROUND: Hypertrophic scar (HTS) is a prevalent chronic inflammatory skin disorder characterized by abnormal proliferation and extracellular matrix deposition and the precise mechanisms underlying HTS remain elusive. This study aimed to identify and validate potential immune-related genes associated with hypertrophic scar formation. METHODS: Skin samples from normal (n = 12) and hypertrophic scar tissues (n = 12) were subjected to RNA-seq analysis. Differentially expressed genes (DEGs) and significant modular genes in Weighted gene Co-expression Network Analysis (WGCNA) were identified. Subsequently, functional enrichment analysis was performed on the intersecting genes. Additionally, eight immune-related genes were matched from the ImmPort database. Validation of NRG1 and CRLF1 was carried out using an external cohort (GSE136906). Furthermore, the association between these two genes and immune cells was assessed by Spearman correlation analysis. Finally, RNA was extracted from normal and hypertrophic scar samples, and RT-qPCR, Immunohistochemistry staining and Western Blot were employed to validate the expression of characteristic genes. RESULTS: A total of 940 DEGs were identified between HTS and normal samples, and 288 key module genes were uncovered via WGCNA. Enrichment analysis in key module revealed involvement in many immune-related pathways, such as Th17 cell differentiation, antigen processing and presentation and B cell receptor signaling pathway. The eight immune-related genes (IFI30, NR2F2, NRG1, ESM1, NFATC2, CRLF1, COLEC12 and IL6) were identified by matching from the ImmPort database. Notably, we observed that activated mast cell positively correlated with CRLF1 expression, while CD8 T cells exhibited a positive correlation with NRG1. The expression of NRG1 and CRLF1 was further validated in clinical samples. CONCLUSION: In this study, two key immune-related genes (CRLF1 and NRG1) were identified as characteristic genes associated with HTS. These findings provide valuable insights into the immune-related mechanisms underlying hypertrophic scar formation.

Pan-cancer analysis of the tumorigenic role of Fanconi anemia complementation group D2 (FANCD2) in human tumors.

Monoubiquitination of FANCD2 is a central step in the activation of the Fanconi anemia (FA) pathway after DNA damage. Defects in the FA pathway centered around FANCD2 not only lead to genomic instability but also induce tumorigenesis. At present, few studies have investigated FANCD2 in tumors, and no pan-cancer research on FANCD2 has been conducted. We conducted a comprehensive analysis of the role of FANCD2 in cancer using public databases and other published studies. Moreover, we evaluated the role of FANCD2 in the proliferation, migration and invasion of lung adenocarcinoma cells through in vitro and in vivo experiments, and explored the role of FANCD2 in cisplatin chemoresistance. We investigated the regulatory effect of FANCD2 on the cell cycle of lung adenocarcinoma cells by flow cytometry, and verified this effect by western blotting. FANCD2 expression is elevated in most TCGA tumors and shows a strong positive correlation with poor prognosis in tumor patients. In addition, FANCD2 expression shows strong correlations with immune infiltration, immune checkpoints, the tumor mutation burden (TMB), and microsatellite instability (MSI), which are immune-related features, suggesting that it may be a potential target of tumor immunotherapy. We further found that FANCD2 significantly promotes the proliferation, invasion, and migration abilities of lung adenocarcinoma cells and that its ability to promote cancer cell proliferation may be achieved by modulating the cell cycle. The findings indicate that FANCD2 is a potential biomarker and therapeutic target in cancer treatment by analyzing the oncogenic role of FANCD2 in different tumors.

Multi-dataset identification of innovative feature genes and molecular mechanisms in keratoconus.

This study aimed to identify feature genes and explore the molecular mechanisms of keratoconus (KC). We downloaded data files from NCBI GEO public database. The Limma package was used for differential expression analysis of gene profiles. Lasso regression was used to identify the feature genes. The CIBERSORT algorithm was used to infer the proportion of immune-infiltrating cells and analyse the correlation between gene expression levels and immune cells. Related transcription factors and miRNAs of key genes were predicted using the Cistrome DB and Mircode databases. Analysis of expression differences in disease genes was based on the GeneCards database. The CMap was used to analyse targeted therapeutic drugs. IHC was performed to verify the expression levels of ATOH7 and MYRF in corneas. Exactly 593 upregulated and 473 downregulated genes were identified. Lasso regression analysis identified ATOH7, DBNDD1, RNF217-AS1, ARL11, MYRF and SNORA74B as feature genes for KC. All key genes were correlated with immune infiltration and the levels of activated memory CD4+ T cells and plasma cells were significantly increased. miRNA, IRF and STAT families were correlated to feature genes. The expression levels of key genes were significantly correlated to KC-related genes. Entinostat, ochratoxin-a, diphencyprone and GSK-3-inhibitor-II were predicted as potential KC medications. The expression of MYRF was significantly higher in the KC samples, contrary to the expression of ATOH7. KC is related to both immune infiltration and genetic factors. MYRF and ATOH7 were newly identified and verified feature genes of KC.

Identification of diagnostic biomarkers and immune cell profiles associated with COPD integrated bioinformatics and machine learning.

This retrospective transcriptomic study leveraged bioinformatics and machine learning algorithms to identify novel gene biomarkers and explore immune cell infiltration profiles associated with chronic obstructive pulmonary disease (COPD). Utilizing an integrated analysis of metadata encompassing six gene expression omnibus (GEO) microarray datasets, 987 differentially expressed genes were identified. Further gene ontology and pathway enrichment analyses revealed the enrichment of these genes across various biological processes and pathways. Moreover, a systematic integration of two machine learning algorithms along with pathway-gene correlations identified six candidate biomarkers, which were validated in a separate cohort comprising six additional microarray datasets, ultimately identifying ADD3 and GNAS as diagnostic biomarkers for COPD. Subsequently, the diagnostic efficacy of ADD3 and GNAS was assessed, and the impact of their expression levels on overall survival was further evaluated and quantified in the validation cohort. Examination of immune cell subtype infiltration found increased proportions of cytotoxic CD8+ T cells, resting and activated NK cells, along with decreased M0 and M2 macrophages, in COPD versus control samples. Correlation analyses also uncovered significant associations between ADD3 and GNAS expression and infiltration of various immune cell types. In conclusion, this study elucidates crucial COPD diagnostic biomarkers and immune cell profiles which may illuminate the immunopathological drivers of COPD progression, representing personalized therapeutic targets warranting further investigation.

Machine learning and experimental validation of novel biomarkers for hypertrophic cardiomyopathy and cancers.

Hypertrophic cardiomyopathy (HCM) is a hereditary cardiac disorder marked by anomalous thickening of the myocardium, representing a significant contributor to mortality. While the involvement of immune inflammation in the development of cardiac ailments is well-documented, its specific impact on HCM pathogenesis remains uncertain. Five distinct machine learning algorithms, namely LASSO, SVM, RF, Boruta and XGBoost, were utilized to discover new biomarkers associated with HCM. A unique nomogram was developed using two newly identified biomarkers and subsequently validated. Furthermore, samples of HCM and normal heart tissues were gathered from our institution to confirm the variance in expression levels and prognostic significance of GATM and MGST1. Five novel biomarkers (DARS2, GATM, MGST1, SDSL and ARG2) associated with HCM were identified. Subsequent validation revealed that GATM and MGST1 exhibited significant diagnostic utility for HCM in both the training and test cohorts, with all AUC values exceeding 0.8. Furthermore, a novel risk assessment model for HCM patients based on the expression levels of GATM and MGST1 demonstrated favourable performance in both the training (AUC = 0.88) and test cohorts (AUC = 0.9). Furthermore, our study revealed that GATM and MGST1 exhibited elevated expression levels in HCM tissues, demonstrating strong discriminatory ability between HCM and normal cardiac tissues (AUC of GATM = 0.79; MGST1 = 0.86). Our findings suggest that two specific cell types, monocytes and multipotent progenitors (MPP), may play crucial roles in the pathogenesis of HCM. Notably, GATM and MGST1 were found to be highly expressed in various tumours and showed significant prognostic implications. Functionally, GATM and MGST1 are likely involved in xenobiotic metabolism and epithelial mesenchymal transition in a wide range of cancer types. GATM and MGST1 have been identified as novel biomarkers implicated in the progression of both HCM and cancer. Additionally, monocytes and MPP may also play a role in facilitating the progression of HCM.

Identification of crosstalk genes relating to ECM-receptor interaction genes in MASH and DN using bioinformatics and machine learning.

This study aimed to identify genes shared by metabolic dysfunction-associated fatty liver disease (MASH) and diabetic nephropathy (DN) and the effect of extracellular matrix (ECM) receptor interaction genes on them. Datasets with MASH and DN were downloaded from the Gene Expression Omnibus (GEO) database. Pearson's coefficients assessed the correlation between ECM-receptor interaction genes and cross talk genes. The coexpression network of co-expression pairs (CP) genes was integrated with its protein-protein interaction (PPI) network, and machine learning was employed to identify essential disease-representing genes. Finally, immuno-penetration analysis was performed on the MASH and DN gene datasets using the CIBERSORT algorithm to evaluate the plausibility of these genes in diseases. We found 19 key CP genes. Fos proto-oncogene (FOS), belonging to the IL-17 signalling pathway, showed greater centrality PPI network; Hyaluronan Mediated Motility Receptor (HMMR), belonging to ECM-receptor interaction genes, showed most critical in the co-expression network map of 19 CP genes; Forkhead Box C1 (FOXC1), like FOS, showed a high ability to predict disease in XGBoost analysis. Further immune infiltration showed a clear positive correlation between FOS/FOXC1 and mast cells that secrete IL-17 during inflammation. Combining the results of previous studies, we suggest a FOS/FOXC1/HMMR regulatory axis in MASH and DN may be associated with mast cells in the acting IL-17 signalling pathway. Extracellular HMMR may regulate the IL-17 pathway represented by FOS through the Mitogen-Activated Protein Kinase 1 (ERK) or PI3K-Akt-mTOR pathway. HMMR may serve as a signalling carrier between MASH and DN and could be targeted for therapeutic development.