Independent Gene Expression Omnibus Research Articles

Constructing and Evaluating a Mitophagy-Related Gene Prognostic Model: Implications for Immune Landscape and Tumor Biology in Lung Adenocarcinoma.

Mitophagy, a conserved cellular mechanism, is crucial for cellular homeostasis through the selective clearance of impaired mitochondria. Its emerging role in cancer development has sparked interest, particularly in lung adenocarcinoma (LUAD). Our study aimed to construct a risk model based on mitophagy-related genes (MRGs) to predict survival outcomes, immune response, and chemotherapy sensitivity in LUAD patients. We mined the GeneCards database to identify MRGs and applied LASSO/Cox regression to formulate a prognostic model. Validation was performed using two independent Gene Expression Omnibus (GEO) cohorts. Patients were divided into high- and low-risk categories according to the median risk score. The high-risk group demonstrated significantly reduced survival. Multivariate Cox analysis confirmed the risk score as an independent predictor of prognosis, and a corresponding nomogram was developed to facilitate clinical assessments. Intriguingly, the risk score correlated with immune infiltration levels, oncogenic expression profiles, and sensitivity to anticancer agents. Enrichment analyses linked the risk score with key oncological pathways and biological processes. Within the model, MTERF3 emerged as a critical regulator of lung cancer progression. Functional studies indicated that the MTERF3 knockdown suppressed the lung cancer cell proliferation and migration, enhanced mitophagy, and increased the mitochondrial superoxide production. Our novel prognostic model, grounded in MRGs, promises to refine therapeutic strategies and prognostication in lung cancer management.

A novel gene signature related to fatty acid metabolism predicts prognosis, immune landscape, and drug sensitivity in early-stage lung squamous cell carcinoma.

Dysregulation of fatty acid metabolism (FAM) represents a significant metabolic alteration in tumorigenesis. However, the role of FAM-related genes (FAMRGs) in early-stage lung squamous cell carcinoma (LUSC) remains incompletely understood. A series of bioinformatic analyses and machine learning strategies were performed to construct a FAMRGs-based signature to predict prognosis and guide personalized treatment for early-stage LUSC patients. FAMRGs were screened through the Kyoto Encyclopedia of Genes and Genomes (KEGG) database and the Molecular Signature Database (MSigDB). Prognosis FAMRGs were identified using univariate Cox regression, and unsupervised clustering analysis facilitated the division of the cohort into different clusters. The least absolute shrinkage and selection operator (LASSO)-Cox regression and multivariate regression analysis were employed to develop a FAMRGs-based signature for predicting overall survival (OS). A nomogram was subsequently constructed to facilitate risk assessment for individual patients. Comprehensive analyses of metabolic pathways, immune infiltration, immunomodulators, and potentially applicable drugs were conducted across different FAMRGs-related risk groups. The FAMRGs-based signature, comprising nine genes (ACOT11, APOH, BMX, CYP2R1, DPEP3, FABP6, FADS2, GLYATL2, and THRSP), demonstrated robust predictive capabilities for prognosis in The Cancer Genome Atlas (TCGA)-LUSC dataset and validated across six independent Gene Expression Omnibus (GEO)-LUSC datasets. Notably, the FAMRGs-base signature exhibited superior prognostic capacity and accurate survival prediction compared to conventional clinicopathological features. Furthermore, the signature was closely associated with immune cell infiltration, human leukocyte antigen (HLA) genes, and immune checkpoint genes expression. Additionally, the signature demonstrated potential sensitivity to chemo-/target-therapy. The FAMRGs-based signature demonstrated superior sensitivity in predicting the prognosis of early-stage LUSC. Detecting FAMRGs may provide predictive targets for the development of clinical treatment strategies.

Investigation of the shared biological mechanisms and common biomarker APTAF1 of sleep deprivation and mild cognitive impairment using integrated bioinformatics analysis.

Introduction: The relationship between sleep loss and cognitive impairment has long been widely recognized, but there is still a lack of complete understanding of the underlying mechanisms and potential biomarkers. The purpose of this study is to further explore the shared biological mechanisms and common biomarkers between sleep loss and cognitive impairment. Methods: The mitochondria-related genes and gene expression data were downloaded from the MitoCarta3.0 and Gene Expression Omnibus (GEO) databases. We identified the differentially expressed mitochondrial-related genes by combing the differentially expressed genes (DEGs) in sleep deprivation (SD) and mild cognitive impairment (MCI) datasets with mitochondria-related gene lists. Shared DEGs were then further analyzed for enrichment analysis. Next, the common biomarker was identified using two machine learning techniques and further validated using two independent GEO datasets. Then GSEA and GSVA were conducted to analyze the functional categories and pathways enriched for the common biomarker. Finally, immune infiltration analysis was used to investigate the correlation of immune cell infiltration with the common biomarker in SD and MCI. Results: A total of 32 mitochondrial-related differentially expressed genes were identified in SD and MCI. GO analysis indicated that these genes were significantly enriched for mitochondrial transport, and KEGG analysis showed they were mainly involved in pathways of neurodegenerative diseases. In addition, ATPAF1, which was significantly down-regulated in both SD and MCI, was identified through machine learning algorithms as the common biomarker with favorable diagnostic performance. GSEA and GSVA revealed that ATPAF1 was mainly involved in metabolic pathways, such as oxidative phosphorylation, acetylcholine metabolic process, valine, leucine and isoleucine degradation. Immune infiltration analysis showed that the expression of ATPAF1 was correlated with changes in immune cells, especially those key immune cell types associated with SD and MCI. Discussion: This study firstly revealed that mitochondrial dysfunction may be the common pathogenesis of sleep loss and mild cognitive impairment and identified ATPAF1 as a possible biomarker and therapeutic target involved in SD and MCI.

Identification of novel prognostic autoantibodies in diffuse large B-cell lymphoma treated with rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone via a high-throughput antigen microarray.

R-CHOP (rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone) is a standard first-line treatment for diffuse large B-cell lymphoma (DLBCL). However, 20%-40% of patients survive less than 5years. Novel prognostic biomarkers remain in demand. Baseline plasma autoantibodies (AAbs) were assessed in 336 DLBCLs. In the discovery phase (n=20), a high-density antigen microarray (∼21,000 proteins) was used to expound AAb profiles. In the verification phase (n=181), with a DLBCL-focused microarray, comparative results based on event-free survival at 24months (EFS24) and lasso Cox regression models of progression-free survival (PFS) and overall survival (OS) were integrated to identify potential biomarkers. They were further validated by enzyme-linked immunosorbent assay in validation phase 1 (n=135) and a dynamic cohort (n=12). In validation phase 2, a two-AAb-based risk score was established. They were further validated in an immunohistochemistry cohort (n=55) and four independent Gene Expression Omnibus datasets (n=1598). Four AAbs (CREB1, N4BP1, UBAP2, and DEAF1) were identified that showed associations with EFS24 status (p<.05) and superior PFS and OS (p<.05). A novel risk score model based on CREB1 and N4BP1 AAbs was developed to predict PFS with areas under the curve of 0.72, 0.71, 0.76, and 0.82 at 1, 3, 5, and 7years, respectively, in DLBCL treated with R-CHOP independent of the International Prognostic Index (IPI) and provided significant additional recurrence risk discrimination (p<.05) for the IPI. CREB1 and N4BP1 proteins and messenger RNAs were also associated with better PFS and OS (p<.05). This study identified a novel prognostic panel of CREB1, N4BP1, DEAF1, and UBAP2 AAbs that is independent of the IPI in DLBCL.

Molecular mapping of a core transcriptional signature of microglia-specific genes in schizophrenia

Besides playing a central role in neuroinflammation, microglia regulate synaptic development and is involved in plasticity. Converging lines of evidence suggest that these different processes play a critical role in schizophrenia. Furthermore, previous studies reported altered transcription of microglia genes in schizophrenia, while microglia itself seems to be involved in the etiopathology of the disease. However, the regional specificity of these brain transcriptional abnormalities remains unclear. Moreover, it is unknown whether brain and peripheral expression of microglia genes are related. Thus, we investigated the expression of a pre-registered list of 10 genes from a core signature of human microglia both at brain and peripheral levels. We included 9 independent Gene Expression Omnibus datasets (764 samples obtained from 266 individuals with schizophrenia and 237 healthy controls) from 8 different brain regions and 3 peripheral tissues. We report evidence of a widespread transcriptional alteration of microglia genes both in brain tissues (we observed a decreased expression in the cerebellum, associative striatum, hippocampus, and parietal cortex of individuals with schizophrenia compared with healthy controls) and whole blood (characterized by a mixed altered expression pattern). Our results suggest that brain underexpression of microglia genes may represent a candidate transcriptional signature for schizophrenia. Moreover, the dual brain-whole blood transcriptional alterations of microglia/macrophage genes identified support the model of schizophrenia as a whole-body disorder and lend weight to the use of blood samples as a potential source of biological peripheral biomarkers.

A novel endoplasmic reticulum stress-related lncRNA signature for prognosis prediction and immune response evaluation in Stomach adenocarcinoma

backgroundStomach adenocarcinoma (STAD) is a significant contributor to cancer-related mortality worldwide. Although previous research has identified endoplasmic reticulum stress (ERS) as a regulator of various tumor-promoting properties of cancer cells, the impact of ERS-related long non-coding RNAs (lncRNAs) on STAD prognosis has not yet been investigated. Therefore, our study aims to develop and validate an ERS-related lncRNA signature that can accurately predict the prognosis of STAD patients.MethodsWe collected RNA expression profiles and clinical data of STAD patients from The Cancer Genome Atlas (TCGA) and identified ERS-related genes from the Molecular Signature Database (MSigDB). Co-expression analysis enabled us to identify ERS-related lncRNAs, and we applied univariate Cox, least absolute shrinkage, and selection operator (LASSO), and multivariate Cox regression analyses to construct a predictive signature comprising of 9 ERS-related lncRNAs. We assessed the prognostic accuracy of our signature using Kaplan-Meier survival analysis, and validated our predictive signature in an independent gene expression omnibus (GEO) cohort. We also performed tumor mutational burden (TMB) and tumor immune microenvironment (TIME) analyses. Enrichment analysis was used to investigate the functions and biological processes of the signature, and we identified two distinct STAD patient subgroups through consensus clustering. Finally, we performed drug sensitivity analysis and immunologic efficacy analysis to explore further insights.ResultsThe 9 ERS related-lncRNAs signature demonstrated satisfactory predictive performance as an independent prognostic marker and was significantly associated with STAD clinicopathological characteristics. Furthermore, patients in the high-risk group displayed a worse STAD prognosis than those in the low-risk group. Notably, gene set enrichment analysis (GSEA) revealed significant enrichment of extracellular matrix pathways in the high-risk group, indicating their involvement in STAD progression. Additionally, the high-risk group exhibited significantly lower TMB expression levels than the low-risk group. Consensus clustering revealed two distinct STAD patient subgroups, with Cluster 1 exhibiting higher immune cell infiltration and more active immune functions. Drug sensitivity analysis suggested that the low-risk group was more responsive to oxaliplatin, epirubicinl, and other drugs.ConclusionOur study highlights the crucial regulatory roles of ERS-related lncRNAs in STAD, with significant clinical implications. The 9-lncRNA signature we have constructed represents a reliable prognostic indicator that has the potential to inform more personalized treatment decisions for STAD patients. These findings shed new light on the pathogenesis of STAD and its underlying molecular mechanisms, offering opportunities for novel therapeutic strategies to be developed for STAD patients.

Identification and validation of SERPINE1 as a prognostic and immunological biomarker in pan-cancer and in ccRCC.

Background: SERPINE1, a serine protease inhibitor involved in the regulation of the plasminogen activation system, was recently identified as a cancer-related gene. However, its clinical significance and potential mechanisms in pan-cancer remain obscure. Methods: In pan-cancer multi-omics data from public datasets, including The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx), and online web tools were used to analyze the expression of SERPINE1 in different cancers and its correlation with prognosis, genetic alteration, DNA promoter methylation, biological processes, immunoregulator expression levels, immune cell infiltration into tumor, tumor mutation burden (TMB), microsatellite instability (MSI), immunotherapy response and drug sensitivity. Further, two single-cell databases, Tumor Immune Single-cell Hub 2 (TISCH2) and CancerSEA, were used to explore the expression and potential roles of SERPINE1 at a single-cell level. The aberrant expression of SERPINE1 was further verified in clear cell renal cell carcinoma (ccRCC) through qRT-PCR of clinical patient samples, validation in independent cohorts using The Gene Expression Omnibus (GEO) database, and proteomic validation using the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Results: The expression of SERPINE1 was dysregulated in cancers and enriched in endothelial cells and fibroblasts. Copy number amplification and low DNA promoter methylation could be partly responsible for high SERPINE1 expression. High SERPINE1 expression was associated with poor prognosis in 21 cancers. The results of gene set enrichment analysis (GSEA) indicated SERPINE1 involvement in the immune response and tumor malignancy. SERPINE1 expression was also associated with the expression of several immunoregulators and immune cell infiltration and could play an immunosuppression role. Besides, SERPINE1 was found to be related with TMB, MSI, immunotherapy response and sensitivity to several drugs in cancers. Finally, the high expression of SERPINE1 in ccRCC was verified using qRT-PCR performed on patient samples, six independent GEO cohorts, and proteomic data from the CPTAC database. Conclusion: The findings of the present study revealed that SERPINE1 exhibits aberrant expression in various types of cancers and is associated with cancer immunity and tumor malignancy, providing novel insights for individualized cancer treatment.

CRISPR-Cas9 identifies growth-related subtypes of glioblastoma with therapeutical significance through cell line knockdown

BackgroundGlioblastoma (GBM) is a type of highly malignant brain tumor that is known for its significant intratumoral heterogeneity, meaning that there can be a high degree of variability within the tumor tissue. Despite the identification of several subtypes of GBM in recent years, there remains to explore a classification based on genes related to proliferation and growth.MethodsThe growth-related genes of GBM were identified by CRISPR-Cas9 and univariate Cox regression analysis. The expression of these genes in the Cancer Genome Atlas cohort (TCGA) was used to construct growth-related genes subtypes (GGSs) via consensus clustering. Validation of this subtyping was performed using the nearest template prediction (NTP) algorithm in two independent Gene Expression Omnibus (GEO) cohorts and the ZZ cohort. Additionally, copy number variations, biological functions, and potential drugs were analyzed for each of the different subtypes separately.ResultsOur research established multicenter-validated GGSs. GGS1 exhibits the poorest prognosis, with the highest frequency of chr 7 gain & chr 10 loss, and the lowest frequency of chr 19 & 20 co-gain. Additionally, GGS1 displays the highest expression of EGFR. Furthermore, it is significantly enriched in metabolic, stemness, proliferation, and signaling pathways. Besides we showed that Foretinib may be a potential therapeutic agent for GGS1, the worst prognostic subtype, through data screening and in vitro experiments. GGS2 has a moderate prognosis, with a slightly higher proportion of chr 7 gain & chr 10 loss, and the highest proportion of chr 19 & 20 co-gain. The prognosis of GGS3 is the best, with the least chr 7 gain & 10 loss and EGFR expression.ConclusionsThese results enhance our understanding of the heterogeneity of GBM and offer insights for stratified management and precise treatment of GBM patients.

Identifying potential circulating miRNA biomarkers for the diagnosis and prediction of ovarian cancer using machine-learning approach: application of Boruta.

In gynecologic oncology, ovarian cancer is a great clinical challenge. Because of the lack of typical symptoms and effective biomarkers for noninvasive screening, most patients develop advanced-stage ovarian cancer by the time of diagnosis. MicroRNAs (miRNAs) are a type of non-coding RNA molecule that has been linked to human cancers. Specifying diagnostic biomarkers to determine non-cancer and cancer samples is difficult. By using Boruta, a novel random forest-based feature selection in the machine-learning techniques, we aimed to identify biomarkers associated with ovarian cancer using cancerous and non-cancer samples from the Gene Expression Omnibus (GEO) database: GSE106817. In this study, we used two independent GEO data sets as external validation, including GSE113486 and GSE113740. We utilized five state-of-the-art machine-learning algorithms for classification: logistic regression, random forest, decision trees, artificial neural networks, and XGBoost. Four models discovered in GSE113486 had an AUC of 100%, three in GSE113740 with AUC of over 94%, and four in GSE113486 with AUC of over 94%. We identified 10 miRNAs to distinguish ovarian cancer cases from normal controls: hsa-miR-1290, hsa-miR-1233-5p, hsa-miR-1914-5p, hsa-miR-1469, hsa-miR-4675, hsa-miR-1228-5p, hsa-miR-3184-5p, hsa-miR-6784-5p, hsa-miR-6800-5p, and hsa-miR-5100. Our findings suggest that miRNAs could be used as possible biomarkers for ovarian cancer screening, for possible intervention.

A novel defined pyroptosis-related gene signature predicts prognosis and correlates with the tumour immune microenvironment in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most common causes of cancer-related death. The role of pyroptosis in LUAD remains unclear. Our study aimed to identify a prognostic signature of pyroptosis-related genes (PRGs) and explore the connection of PRGs with the tumour microenvironment in LUAD. Gene expression and clinical information were obtained from The Cancer Genome Atlas database. Consensus clustering was applied to classify LUAD patients. The least absolute shrinkage and selection operator Cox and multivariate Cox regression models were used to generate a PRG-related prognostic signature. The correlations between PRGs and tumour-infiltrating immune cells or the tumour mutational burden were analysed by Spearman’s correlation analysis. In this study, 44 PRGs significantly differed in expression between LUAD and normal tissues. Based on these genes, patients were clustered into three clusters with significantly different distributions of tumour-infiltrating immune cells and immune checkpoint regulators. A total of four PRGs (NLRP1, HMGB1, CYCS, and BAK1) were used to construct a prognostic model. Significant correlations were observed between these prognostic PRGs and immune cell infiltration or the tumour mutational burden. Predictive nomogram results showed that BAK1 could be an independent prognostic biomarker in LUAD. Additionally, the expression level of BAK1 was validated in two independent Gene Expression Omnibus cohorts. Our identified prognostic PRG signature may provide insight for future studies targeting pyroptosis and the tumour microenvironment in LUAD. Future studies are needed to verify our current findings.

Identification of p53-target genes in human papillomavirus-associated head and neck cancer by integrative bioinformatics analysis.

Head and neck cancer (HNC) is a highly prevalent and heterogeneous malignancy. Although extensive efforts have been made to advance its treatment, the prognosis remained poor with increased mortality. Human papillomaviruses (HPV) have been associated with high risk in HNC. TP53, a tumor suppressor, is the most frequently altered gene in HNC, therefore, investigating its target genes for the identification of novel biomarkers or therapeutic targets in HPV-related HNC progression is highly recommended. Transcriptomic profiles from three independent gene expression omnibus (GEO) datasets, including 44 HPV+ and 70 HPV- HNC patients, were subjected to integrative statistical and Bioinformatics analyses. For the top-selected marker, further in-silico validation in TCGA and GTEx databases and experimental validation in 65 (51 HPV- and 14 HPV+) subjects with histologically confirmed head and neck squamous cell carcinoma (HNSCC) have been performed. A total of 498 differentially expressed genes (DEGs) were identified including 291 up-regulated genes and 207 down-regulated genes in HPV+ compared to HPV- HNSCC patients. Functional annotations and gene set enrichment analysis (GSEA) showed that the up-regulated genes were significantly involved in p53-related pathways. The integrative analysis between the Hub-genes identified in the complex protein-protein network and the top frequent genes resulting from GSEA showed an intriguing correlation with five biomarkers which are EZH2, MDM2, PCNA, STAT5A and TYMS. Importantly, the MDM2 gene showed the highest gene expression difference between HPV+ and HPV- HNSCC (Average log2FC = 1.89). Further in-silico validation in a large HNSCC cohort from TCGA and GTEx databases confirmed the over-expression of MDM2 in HPV+ compared to HPV- HNSCC patients (p = 2.39E-05). IHC scoring showed that MDM2 protein expression was significantly higher in HPV+ compared to HPV- HNSCC patients (p = 0.031). Our findings showed evidence that over-expression of MDM2, proto-oncogene, may affect the occurrence and proliferation of HPV-associated HNSCC by disturbing the p53-target genes and consequently the p53-related pathways.

Integrative network-based analysis on multiple Gene Expression Omnibus datasets identifies novel immune molecular markers implicated in non-alcoholic steatohepatitis.

Non-alcoholic steatohepatitis (NASH), an advanced subtype of non-alcoholic fatty liver disease (NAFLD), has becoming the most important aetiology for end-stage liver disease, such as cirrhosis and hepatocellular carcinoma. This study were designed to explore novel genes associated with NASH. Here, five independent Gene Expression Omnibus (GEO) datasets were combined into a single cohort and analyzed using network biology approaches. 11 modules identified by weighted gene co-expression network analysis (WGCNA) showed significant association with the status of NASH. Further characterization of four gene modules of interest demonstrated that molecular pathology of NASH involves the upregulation of hub genes related to immune response, cholesterol and lipid metabolic process, extracellular matrix organization, and the downregulation of hub genes related to cellular amino acid catabolic, respectively. After DEGs enrichment analysis and module preservation analysis, the Turquoise module associated with immune response displayed a remarkably correlation with NASH status. Hub genes with high degree of connectivity in the module, including CD53, LCP1, LAPTM5, NCKAP1L, C3AR1, PLEK, FCER1G, HLA-DRA and SRGN were further verified in clinical samples and mouse model of NASH. Moreover, single-cell RNA-seq analysis showed that those key genes were expressed by distinct immune cells such as microphages, natural killer, dendritic, T and B cells. Finally, the potential transcription factors of Turquoise module were characterized, including NFKB1, STAT3, RFX5, ILF3, ELF1, SPI1, ETS1 and CEBPA, the expression of which increased with NASH progression. In conclusion, our integrative analysis will contribute to the understanding of NASH and may enable the development of potential biomarkers for NASH therapy.

Deregulated Gene Expression Profiles and Regulatory Networks in Adult and Pediatric RUNX1/RUNX1T1-Positive AML Patients

Acute myeloid leukemia (AML) is a heterogeneous and complex disease concerning molecular aberrations and prognosis. RUNX1/RUNX1T1 is a fusion oncogene that results from the chromosomal translocation t(8;21) and plays a crucial role in AML. However, its impact on the transcriptomic profile of different age groups of AML patients is not completely understood. Here, we investigated the deregulated gene expression (DEG) profiles in adult and pediatric RUNX1/RUNX1T1-positive AML patients, and compared their functions and regulatory networks. We retrospectively analyzed gene expression data from two independent Gene Expression Omnibus (GEO) datasets (GSE37642 and GSE75461) and computed their differentially expressed genes and upstream regulators, using limma, GEO2Enrichr, and X2K. For validation purposes, we used the TCGA-LAML (adult) and TARGET-AML (pediatric) patient cohorts. We also analyzed the protein-protein interaction (PPI) networks, as well as those composed of transcription factors (TF), intermediate proteins, and kinases foreseen to regulate the top deregulated genes in each group. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enrichment analyses were further performed for the DEGs in each dataset. We found that the top upregulated genes in (both adult and pediatric) RUNX1/RUNX1T1-positive AML patients are enriched in extracellular matrix organization, the cell projection membrane, filopodium membrane, and supramolecular fiber. Our data corroborate that RUNX1/RUNX1T1 reprograms a large transcriptional network to establish and maintain leukemia via intricate PPI interactions and kinase-driven phosphorylation events.

Integrating single-cell and bulk RNA sequencing to develop a cancer-associated fibroblast-related signature for immune infiltration prediction and prognosis in lung adenocarcinoma.

An accumulating amount of studies are highlighting the impacts of cancer-associated fibroblasts (CAFs) on the initiation, metastasis, invasion, and immune evasion of lung cancer. However, it is still unclear how to tailor treatment regimens based on the transcriptomic characteristics of CAFs in the tumor microenvironment of patients with lung cancer. Our study examined single-cell RNA-sequencing data from the Gene Expression Omnibus (GEO) database to identify expression profiles for CAF marker genes and constructed a prognostic signature of lung adenocarcinoma using these genes in The Cancer Genome Atlas (TCGA) database. The signature was validated in 3 independent GEO cohorts. Univariate and multivariate analyses were used to confirm the clinical significance of the signature. Next, multiple differential gene enrichment analysis methods were used to explore the biological pathways related to the signature. Six algorithms were used to assess the relative proportion of infiltrating immune cells, and the relationship between the signature and immunotherapy response of lung adenocarcinoma (LUAD) was explored based on the tumor immune dysfunction and exclusion (TIDE) algorithm. The signature related to CAFs in this study showed good accuracy and predictive capacity. In all clinical subgroups, the high-risk patients had a poor prognosis. The univariate and multivariate analyses confirmed that the signature was an independent prognostic marker. Moreover, the signature was closely associated with particular biological pathways related to cell cycle, DNA replication, carcinogenesis, and immune response. The 6 algorithms used to assess the relative proportion of infiltrating immune cells indicated that a lower infiltration of immune cells in the tumor microenvironment was associated with high-risk scores. Importantly, we found a negative correlation between TIDE, exclusion score, and risk score. Our study constructed a prognostic signature based on CAF marker genes useful for prognosis and immune infiltration estimation of lung adenocarcinoma. This tool could enhance therapy efficacy and allow individualized treatments.

Exploration of the shared pathways and common biomarker PAN3 in ankylosing spondylitis and ulcerative colitis using integrated bioinformatics analysis.

Ulcerative colitis (UC) is a chronic autoimmune-related disease that causes inflammation of the intestine. Ankylosing spondylitis (AS) is a common extraintestinal complication of UC involving the sacroiliac joint. However, the pathogenesis of AS secondary to UC has not been studied. This study aimed to investigate the shared pathways and potential common biomarkers of UC and AS. Microarray data downloaded from the Gene Expression Omnibus (GEO) database were used to screen differentially expressed genes (DEGs) in the UC and AS datasets. Weighted gene co-expression network analysis (WGCNA) was performed to identify co-expression modules related to UC and AS. Shared genes were then further analyzed for functional pathway enrichment. Next, the optimal common biomarker was selected using SVM-RFF and further validated using two independent GEO datasets. Finally, immune infiltration analysis was used to investigate the correlation of immune cell infiltration with common biomarkers in UC and AS. A total of 4428 and 2438 DEGs in UC and AS, respectively, were screened. Four modules were identified as significant for UC and AS using WGCNA. A total of 25 genes overlapped with the strongest positive and negative modules of UC and AS. KEGG analysis showed these genes may be involved in the mitogen-activated protein kinase (MAPK) signaling pathway. GO analysis indicated that these genes were significantly enriched for RNA localization. PAN3 was selected as the optimal common biomarker for UC and AS. Immune infiltration analysis showed that the expression of PAN3 was correlated with changes in immune cells. This study first explored the common pathways and genetic diagnostic markers involved in UC and AS using bioinformatic analysis. Results suggest that the MAPK signaling pathway may be associated with both pathogeneses and that PAN3 may be a potential diagnostic marker for patients with UC complicated by AS.

Comprehensively analyzing the genetic alterations, and identifying key genes in ovarian cancer.

Though significant improvements have been made in the treatment methods for ovarian cancer (OC), the prognosis for OC patients is still poor. Exploring hub genes associated with the development of OC and utilizing them as appropriate potential biomarkers or therapeutic targets is highly valuable. In this study, the differentially expressed genes (DEGs) were identified from an independent GSE69428 Gene Expression Omnibus (GEO) dataset between OC and control samples. The DEGs were processed to construct the protein-protein interaction (PPI) network using STRING. Later, hub genes were identified through Cytohubba analysis of the Cytoscape. Expression and survival profiling of the hub genes were validated using GEPIA, OncoDB, and GENT2. For exploring promoter methylation levels and genetic alterations in hub genes, MEXPRESS and cBioPortal were utilized, respectively. Moreover, DAVID, HPA, TIMER, CancerSEA, ENCORI, DrugBank, and GSCAlite were used for gene enrichment analysis, subcellular localization analysis, immune cell infiltration analysis, exploring correlations between hub genes and different diverse states, lncRNA-miRNA-mRNA co-regulatory network analysis, predicting hub gene-associated drugs, and conducting drug sensitivity analysis, respectively. In total, 8947 DEGs were found between OC and normal samples in GSE69428. After STRING and Cytohubba analysis, 4 hub genes including TTK (TTK Protein Kinase), (BUB1 mitotic checkpoint serine/threonine kinase B) BUB1B, (Nucleolar and spindle-associated protein 1) NUSAP1, and (ZW10 interacting kinetochore protein) ZWINT were selected as the hub genes. Further, it was validated that these 4 hub genes were significantly up-regulated in OC samples compared to normal controls, but overexpression of these genes was not associated with overall survival (OS). However, genetic alterations in those genes were found to be linked with OS and disease-free (DFS) survival. Moreover, this study also revealed some novel links between TTK, BUB1B, NUSAP1, and ZWINT overexpression and promoter methylation status, immune cell infiltration, miRNAs, gene enrichment terms, and various chemotherapeutic drugs. Four hub genes, including TTK, BUB1B, NUSAP1, and ZWINT, were revealed as tumor-promotive factors in OC, having the potential to be utilized as novel biomarkers and therapeutic targets for OC management.

Development and validation of a prognostic 15-gene signature for stratifying HER2+/ER+ breast cancer

BackgroundHuman epidermal growth receptor 2-positive (HER2+) breast cancer (BC) is a heterogeneous subgroup. Estrogen receptor (ER) status is emerging as a predictive marker within HER2+ BCs, with the HER2+/ER+ cases usually having better survival in the first 5 years after diagnosis but have higher recurrence risk after 5 years compared to HER2+/ER-. This is possibly because sustained ER signaling in HER2+ BCs helps escape the HER2 blockade. Currently HER2+/ER+ BC is understudied and lacks biomarkers. Thus, a better understanding of the underlying molecular diversity is important to find new therapy targets for HER2+/ER+ BCs. MethodsIn this study, we performed unsupervised consensus clustering together with genome-wide Cox regression analyses on the gene expression data of 123 HER2+/ER+ BC from The Cancer Genome Atlas Breast Invasive Carcinoma (TCGA-BRCA) cohort to identify distinct HER2+/ER+ subgroups. A supervised eXtreme Gradient Boosting (XGBoost) classifier was then built in TCGA using the identified subgroups and validated in another two independent datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) and Gene Expression Omnibus (GEO) (accession number GSE149283)). Computational characterization analyses were also performed on the predicted subgroups in different HER2+/ER+ BC cohorts. ResultsWe identified two distinct HER2+/ER+ subgroups with different survival outcomes using the expression profiles of 549 survival-associated genes from the Cox regression analyses. Genome-wide gene expression differential analyses found 197 differentially expressed genes between the two identified subgroups, with 15 genes overlapping the 549 survival-associated genes.XGBoost classifier, using the expression values of the 15 genes, achieved a strong cross-validated performance (Area under the curve (AUC) = 0.85, Sensitivity = 0.76, specificity = 0.77) in predicting the subgroup labels. Further investigation partially confirmed the differences in survival, drug response, tumor-infiltrating lymphocytes, published gene signatures, and CRISPR-Cas9 knockout screened gene dependency scores between the two identified subgroups. ConclusionThis is the first study to stratify HER2+/ER+ tumors. Overall, the initial results from different cohorts showed there exist two distinct subgroups in HER2+/ER+ tumors, which can be distinguished by a 15-gene signature. Our findings could potentially guide the development of future precision therapies targeted on HER2+/ER+ BC.

Identifying the Role of Oxidative Stress-Related Genes as Prognostic Biomarkers and Predicting the Response of Immunotherapy and Chemotherapy in Ovarian Cancer.

Ovarian cancer (OC) is one of the most frequently seen and fatal gynecological malignancies, and oxidative stress (OS) plays a critical role in the development and chemoresistance of OC. OS-related genes (OSRGs) were obtained from the Molecular Signatures Database. Besides, gene expression profiles and clinical information from The Cancer Genome Atlas (TCGA) were selected to identify the prognostic OSRGs. Moreover, univariate Cox regression, LASSO, and multivariate Cox regression analyses were conducted sequentially to establish a prognostic signature, which was later validated in three independent Gene Expression Omnibus (GEO) datasets. Next, gene set enrichment analysis (GSEA) and tumor mutation burden (TMB) analysis were performed. Afterwards, immune checkpoint genes (ICGs) and the tumor immune dysfunction and exclusion (TIDE) algorithm, together with IMvigor210 and GSE78220 cohorts, were applied to comprehensively explore the role of OSRG signature in immunotherapy. Further, the CellMiner and Genomics of Drug Sensitivity in Cancer (GDSC) databases were also applied in investigating the significance of OSRG signature in chemotherapy. Altogether, 34 prognostic OSRGs were identified, among which 14 were chosen to establish the most valuable prognostic signature. The Kaplan-Meier (KM) analysis suggested that patients with lower OS-related risk score had better prognosis. The area under the curve (AUC) values were 0.71, 0.76, and 0.85 in 3, 5, and 7 years separately, and the stability of this prognostic signature was confirmed in three GEO datasets. As revealed by GSEA and TMB analysis results, OC patients in low-risk group might have better immunotherapeutic response, which was consistent with ICG expression and TIDE analyses. Moreover, both IMvigor210 and GSE78220 cohorts demonstrated that patients with lower OS-related risk score were more likely to benefit from anti-PD-1/L1 immunotherapy. In addition, the association between prognostic signature and drug sensitivity was explored. According to our results in this work, OSRG signature can act as a powerful prognostic predictor for OC, which contributes to generating more individualized therapeutic strategies for OC patients.

192 Betacellulin alleviates Th2 cytokine-mediated impairment of skin tight junction barrier through epidermal growth factor receptor and protein kinase C pathway

Betacellulin (BTC) is a peptide ligand that belongs to the epidermal growth factor family, the members of which are implicated in skin morphogenesis, homeostasis, repair, and angiogenesis; however, the role of BTC in regulation of skin barrier remains unknown so far. To examine the role of BTC in skin barrier function, we analysed data from two independent Gene Expression Omnibus (GEO) datasets and evaluated the effects of BTC on primary human keratinocytes using real-time PCR, Western blot and transepidermal electrical resistance (TER), a functional parameter to monitor the tight junction barrier.

Comprehensive analysis of PTPN gene family revealing PTPN7 as a novel biomarker for immuno-hot tumors in breast cancer.

Background: The non-receptor protein tyrosine phosphatase (PTPN) gene family has been considered to be involved in the oncogenesis and development of multiple cancers. However, its prognostic utility and immunological relevance in breast cancer (BrCa) have not been clarified. Methods: A transcriptional level interpretation of the expressions and prognostic values was analyzed using the data from The Cancer Genome Atlas (TCGA) cohort. In addition, GO and DAVID pinpoint the functional enrichment of PTPNs. Moreover, the immune correlations of PTPN7 in BrCa and pan-cancer were further investigated based on the TCGA cohort and were testified using the in-house and the Gene Expression Omnibus (GEO) cohorts. Results: For systematic analysis of the PTPN family, we found that the expression levels of PTPN1, PTPN6, PTPN7, PTPN18, PTPN20, and PTPN22 was promoted in tumor tissues while comparing with paraneoplastic tissues during our study. We further investigated their functions and protein-protein interactions (PPI), and these results strongly suggested that PTPN family was associated with protein dephosphorylation. Next, we performed an immunological relevance analysis and found that PTPN7 was correlated with immune infiltration, suggesting a stronger association of PTPN7 with immuno-hot tumors in BrCa. In addition, results from the in-house cohort confirmed the positive correlation between PTPN7 and PD-L1. The pan-cancer analysis revealed that PTPN7 was related to PD-L1 and CTLA-4 expression in almost all cancer types. Finally, the predictive value of PTPN7 for immunotherapy was significant in two independent GEO cohorts. Conclusion: In conclusion, this is the first extensive research on the correlation between PTPN family expression and immune characterization in BrCa. As results, PTPN7 expression is associated with immuno-hot tumors and could be a promising predictive biomarker for immunotherapy in not only BrCa but multiple cancers.