Eleven genes associated with progression and prognosis of endometrial cancer (EC) identified by comprehensive bioinformatics analysis

Elevated expression of ECT2 as a diagnostic marker and prognostic indicator in endometrial cancer

OBJECTIVES

The study aims to investigate genes associated with endometrial cancer (EC) progression to identify new biomarkers for early detection.

METHODS

Differentially expressed genes (DEGs), Series test of cluster (STC) and protein-protein interaction analyses identified hub genes in EC. Clinical samples were utilized to examine the expression pattern of ECT2, assess its prognostic value, and evaluate its diagnostic potential.

RESULTS

Upregulated DEGs were significantly enriched in cancer-related processes and pathways. Validations across databases identified ASPM, ATAD2, BUB1B, ECT2, KIF14, NUF2, NCAPG, and SPAG5 as potential hub genes, with ECT2 exhibiting the highest diagnostic efficacy. The expression levels of ECT2 varied significantly across different clinical stages, pathological grades, and metastasis statuses in UCEC. Furthermore, ECT2 mRNA was upregulated in the p53abn group, indicating a poorer prognosis, and downregulated in the MMRd and NSMP groups, suggesting a moderate prognosis. In clinical samples, ECT2 expression increased from normal endometria and endometrial hyperplasia without atypia (EH) to atypical endometrial hyperplasia (AH) and EC, effectively distinguishing between benign and malignant endometria. High ECT2 expression was associated with an unfavourable prognosis.

CONCLUSIONS

ECT2 expression significantly rises in AH and EC, showing high accuracy in distinguishing between benign and malignant endometria. ECT2 emerges as a promising biomarker for diagnosing endometrial neoplasia and as a prognostic indicator in EC.

Identification of neutrophil extracellular trap-related biomarkers in non-alcoholic fatty liver disease through machine learning and single-cell analysis

Non-alcoholic Fatty Liver Disease (NAFLD), noted for its widespread prevalence among adults, has become the leading chronic liver condition globally. Simultaneously, the annual disease burden, particularly liver cirrhosis caused by NAFLD, has increased significantly. Neutrophil Extracellular Traps (NETs) play a crucial role in the progression of this disease and are key to the pathogenesis of NAFLD. However, research into the specific roles of NETs-related genes in NAFLD is still a field requiring thorough investigation. Utilizing techniques like AddModuleScore, ssGSEA, and WGCNA, our team conducted gene screening to identify the genes linked to NETs in both single-cell and bulk transcriptomics. Using algorithms including Random Forest, Support Vector Machine, Least Absolute Shrinkage, and Selection Operator, we identified ZFP36L2 and PHLDA1 as key hub genes. The pivotal role of these genes in NAFLD diagnosis was confirmed using the training dataset GSE164760. This study identified 116 genes linked to NETs across single-cell and bulk transcriptomic analyses. These genes demonstrated enrichment in immune and metabolic pathways. Additionally, two NETs-related hub genes, PHLDA1 and ZFP36L2, were selected through machine learning for integration into a prognostic model. These hub genes play roles in inflammatory and metabolic processes. scRNA-seq results showed variations in cellular communication among cells with different expression patterns of these key genes. In conclusion, this study explored the molecular characteristics of NETs-associated genes in NAFLD. It identified two potential biomarkers and analyzed their roles in the hepatic microenvironment. These discoveries could aid in NAFLD diagnosis and management, with the ultimate goal of enhancing patient outcomes.

Identification of programmed cell death-related genes and diagnostic biomarkers in endometriosis using a machine learning and Mendelian randomization approach

Background

Endometriosis (EM) is a prevalent gynecological disorder frequently associated with irregular menstruation and infertility. Programmed cell death (PCD) is pivotal in the pathophysiological mechanisms underlying EM. Despite this, the precise pathogenesis of EM remains poorly understood, leading to diagnostic delays. Consequently, identifying biomarkers associated with PCD is critical for advancing the diagnosis and treatment of EM.

Methods

This study used datasets from the Gene Expression Omnibus (GEO) to identify differentially expressed genes (DEGs) following preprocessing. By cross-referencing these DEGs with genes associated with PCD, differentially expressed PCD-related genes (DPGs) were identified. Enrichment analyses for KEGG and GO pathways were conducted on these DPGs. Additionally, Mendelian randomization and machine learning techniques were applied to identify biomarkers strongly associated with EM.

Results

The study identified three pivotal biomarkers: TNFSF12, AP3M1, and PDK2, and established a diagnostic model for EM based on these genes. The results revealed a marked upregulation of TNFSF12 and PDK2 in EM samples, coupled with a significant downregulation of AP3M1. Single-cell analysis further underscored the potential of TNFSF12, AP3M1, and PDK2 as biomarkers for EM. Additionally, molecular docking studies demonstrated that these genes exhibit significant binding affinities with drugs currently utilized in clinical practice.

Conclusion

This study systematically elucidated the molecular characteristics of PCD in EM and identified TNFSF12, AP3M1, and PDK2 as key biomarkers. These findings provide new directions for the early diagnosis and personalized treatment of EM.

Using clusterProfiler to characterize multiomics data

With the advent of multiomics, software capable of multidimensional enrichment analysis has become increasingly crucial for uncovering gene set variations in biological processes and disease pathways. This is essential for elucidating disease mechanisms and identifying potential therapeutic targets. clusterProfiler stands out for its comprehensive utilization of databases and advanced visualization features. Importantly, clusterProfiler supports various biological knowledge, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes, through performing over-representation and gene set enrichment analyses. A key feature is that clusterProfiler allows users to choose from various graphical outputs to visualize results, enhancing interpretability. This protocol describes innovative ways in which clusterProfiler has been used for integrating metabolomics and metagenomics analyses, identifying and characterizing transcription factors under stress conditions, and annotating cells in single-cell studies. In all cases, the computational steps can be completed within ~2 min. clusterProfiler is released through the Bioconductor project and can be accessed via https://bioconductor.org/packages/clusterProfiler/ .

Revealing the pathogenesis of gastric intestinal metaplasia based on the mucosoid air-liquid interface

BACKGROUND

Gastric intestinal metaplasia (GIM) is an essential precancerous lesion. Although the reversal of GIM is challenging, it potentially brings a state-to-art strategy for gastric cancer therapeutics (GC). The lack of the appropriate in vitro model limits studies of GIM pathogenesis, which is the issue this work aims to address for further studies.

METHOD

The air-liquid interface (ALI) model was adopted for the long-term culture of GIM cells in the present work. This study conducted Immunofluorescence (IF), quantitative real-time polymerase chain reaction (qRT-PCR), transcriptomic sequencing, and mucoproteomic sequencing (MS) techniques to identify the pathways for differential expressed genes (DEGs) enrichment among different groups, furthermore, to verify novel biomarkers of GIM cells.

RESULT

Our study suggests that GIM-ALI model is analog to the innate GIM cells, which thus can be used for mucus collection and drug screening. We found genes MUC17, CDA, TRIM15, TBX3, FLVCR2, ONECUT2, ACY3, NMUR2, and MAL2 were highly expressed in GIM cells, while GLDN, SLC5A5, MAL, and MALAT1 showed down-regulated, which can be used as potential biomarkers for GIM cells. In parallel, these genes that highly expressed in GIM samples were mainly involved in cancer-related pathways, such as the MAPK signal pathway and oxidative phosphorylation signal pathway.

CONCLUSION

The ALI model is validated for the first time for the in vitro study of GIM. GIM-ALI model is a novel in vitro model that can mimic the tissue micro-environment in GIM patients and further provide an avenue for studying the characteristics of GIM mucus. Our study identified new markers of GIM as well as pathways associated with GIM, which provides outstanding insight for exploring GIM pathogenesis and potentially other related conditions.

A prognostic model established using bile acid genes to predict the immunity and survival of patients with gastrointestinal cancer

BACKGROUND

The metabolism of abnormal bile acids (BAs) is implicated in the initiation and development of gastrointestinal (GI) cancer. However, there was a lack of research on the molecular mechanisms of BAs metabolism in GI.

METHODS

Genes involved in BAs metabolism were excavated from public databases of The Cancer Genome Atlas (TCGA) database, Gene Expression Omnibus (GEO) database, and Molecular Signatures Database (MSigDB). ConsensusClusterPlus was used to classify molecular subtypes for GI. To develop a RiskScore model for predicting GI prognosis, univariate Cox analysis was performed on the genes in protein-protein interaction (PPI) network, followed by using Lasso regression and stepwise regression to refine the model and to determine the key prognostic genes. Tumor immune microenvironment in GI patients from different risk groups was assessed using the ESTIMATE algorithm and enrichment analysis. Reverse transcription-quantitative real-time PCR (RT-qPCR), Transwell assay, and wound healing assay were carried out to validate the expression and functions of the model genes.

RESULTS

This study defined three molecular subtypes (C1, C2, and C3). Specifically, C1 had the best prognosis, while C3 had the worst prognosis with high immune checkpoint gene expression levels and TIDE scores. We selected nine key genes (AXIN2, ATOH1, CHST13, PNMA2, GYG2, MAGEA3, SNCG, HEYL, and RASSF10) that significantly affected the prognosis of GI and used them to develop a RiskScore model accordingly. Combining the verification results from a nomogram, the prediction of the model was proven to be accurate. The high RiskScore group was significantly enriched in tumor and immune-related pathways. Compared with normal gastric mucosal epithelial cells, the mRNA levels of the nine genes were differential in the gastric cancer cells. Inhibition of PNMA2 suppressed migration and invasion of the cancer cells.

CONCLUSION

We distinguished three GI molecular subtypes with different prognosis based on the genes related to BAs metabolism and developed a RiskScore model, contributing to the diagnosis and treatment of patients with GI.

HIF‑1 and macrophage activation signalling pathways are potential biomarkers of invasive aspergillosis

Invasive aspergillosis (IA) is a severe disease, the pathogenesis of which remains unclear. The present study aimed to determine the molecular mechanism of IA and to identify potential biomarkers using bioinformatics analysis. The GSE78000 dataset, which includes data from patients with IA and healthy individuals, was downloaded from Gene Expression Omnibus. Differentially expressed genes (DEGs) between the IA and control groups were identified with the 'affy' package in R software. The Gene Ontology (GO) and Kyoto Encyclopaedia of Genes and Genomes (KEGG) databases were then used to analyse the function and pathway enrichment of DEGs. The protein-protein interaction network was analysed with the Search Tool for the Retrieval of Interacting Genes (STRING) website. In addition, DEGs were confirmed using reverse transcription-quantitative PCR and western blotting in samples with IA (n=6) and control samples (n=6) collected from the Department of Respiratory and Critical Care Medicine of the First Affiliated Hospital of Henan University of Science and Technology (Luoyang, China). The present study identified 735 DEGs, including 312 upregulated and 423 downregulated genes. Through GO and KEGG analyses of the DEGs, macrophage activation and hypoxia-inducible factor 1 (HIF-1) signalling pathways were revealed to be significantly upregulated and downregulated, respectively, in patients with IA compared with that of the healthy individuals. Subsequently, correlation analysis of macrophage activation and HIF-1 signalling pathways was revealed using correlation as a distance metric for hierarchical clustering correlation analysis. However, there was no protein-protein interaction between the macrophage activity regulation and HIF-1 signalling pathways based on STRING analysis. In summary, the present study identified candidate genes and associated molecules that may be associated to IA and revealed potential biomarkers and therapeutic targets for IA.

Multimodal investigation reveals the neuroprotective mechanism of Angong Niuhuang pill for intracerebral hemorrhage: Converging bioinformatics, network pharmacology, and experimental validation

ETHNOPHARMACOLOGICAL RELEVANCE

Angong Niuhuang Pill (ANP) is a traditional Chinese medicine formula that has been used clinically for many years in the treatment of cerebral hemorrhage. It is composed of ingredients such as calculus bovis, moschus, and others. Ancient texts have documented that ANP's multiple components possess properties such as heat-clearing, detoxification, and sedation, which can be effective in treating conditions such as coma and stroke. However, the underlying mechanisms of ANP's potential actions are still under investigation.

AIM OF THE STUDY

ANP is a Chinese medicine widely utilized for the treatment of intracerebral hemorrhage (ICH). However, the precise mechanism underlying the therapeutic effects remains largely elusive. The present study aims to unravel the effects and pharmacological molecular mechanisms of ANP in combatting ICH, employing a comprehensive network pharmacology approach and experimental validation.

MATERIALS AND METHODS

The molecular targets of ANP and ICH were obtained from various databases, followed by the construction of protein-protein interaction (PPI) networks using the STRING database. Further, gene ontology (GO) enrichment and Kyoto encyclopedia of genes and genomes (KEGG) analyses were conducted using the Metascape database and Cytoscape, respectively. Finally, molecular docking was performed. We performed a series of behavioral tests, immunohistochemical staining, TUNEL staining, and Western Blot to verify the effects of ANP.

RESULTS

IL-6, JUN, MMP9, IL-1β, VEGFA were the main candidate targets and were associated with fluid shear stress and atherosclerosis, TNF signaling pathway, etc. It is suggested that the potential mechanism of ANP against ICH may be mainly related to pyroptosis, inflammation. In vivo validation showed that ANP treatment significantly reduced the number of TUNEL-positive cells and ANP inhibited the activation of Iba-1 positive neurons, and suppressed the expression of inflammatory factors and pyroptosis indicators. In addition, ANP improved the cognitive level and motor ability of ICH mice.

CONCLUSION

The results of the study combined with virtual screening and experimental validation showed that ANP has an important contribution in protecting the brain from neuronal damage by regulating the pathways of inflammation and pyroptosis, laying the foundation and innovative ideas for future studies.

The Prognostic Hub Gene POLE2 Promotes BLCA Cell Growth via the PI3K/AKT Signaling Pathway

BACKGROUND

BLCA is a common urothelial malignancy characterized by a high recurrence rate. Despite its prevalence, the molecular mechanisms underlying its development remain unclear.

AIMS

This study aimed to explore new prognostic biomarkers and investigate the underlying mechanism of bladder cancer (BLCA).

OBJECTIVE

The objective of this study is to identify key prognostic biomarkers for BLCA and to elucidate their roles in the disease.

METHODS

We first collected the overlapping DEGs from GSE42089 and TCGA-BLCA samples for the subsequent weighted gene co-expression network analysis (WGCNA) to find a key module. Then, key module genes were analyzed by the MCODE algorithm, prognostic risk model, expression and immunohistochemical staining to identify the prognostic hub gene. Finally, the hub gene was subjected to clinical feature analysis, as well as cellular function assays.

RESULTS

In WGCNA on 1037 overlapping genes, the blue module was the key module. After a series of bioinformatics analyses, POLE2 was identified as a prognostic hub gene in BLCA from potential genes (TROAP, POLE2, ANLN, and E2F8). POLE2 level was increased in BLCA and related to different clinical features of BLCA patients. Cellular assays showed that si-POLE2 inhibited BLCA proliferation, and si-POLE2+ 740Y-P in BLCA cells up-regulated the PI3K and AKT protein levels.

CONCLUSION

In conclusion, POLE2 was identified to be a promising prognostic biomarker as an oncogene in BLCA. It was also found that POLE2 exerts a promoting function by the PI3K/AKT signaling pathway in BLCA.

Decoding the mitochondrial connection: development and validation of biomarkers for classifying and treating systemic lupus erythematosus through bioinformatics and machine learning

BACKGROUND

Systemic lupus erythematosus (SLE) is a multifaceted autoimmune disease characterized by clinical and pathological diversity. Mitochondrial dysfunction has been identified as a critical pathogenetic factor in SLE. However, the specific molecular aspects and regulatory roles of this dysfunction in SLE are not fully understood. Our study aims to explore the molecular characteristics of mitochondria-related genes (MRGs) in SLE, with a focus on identifying reliable biomarkers for classification and therapeutic purposes.

METHODS

We sourced six SLE-related microarray datasets (GSE61635, GSE50772, GSE30153, GSE99967, GSE81622, and GSE49454) from the Gene Expression Omnibus (GEO) database. Three of these datasets (GSE61635, GSE50772, GSE30153) were integrated into a training set for differential analysis. The intersection of differentially expressed genes with MRGs yielded a set of differentially expressed MRGs (DE-MRGs). We employed machine learning algorithms-random forest (RF), support vector machine (SVM), and least absolute shrinkage and selection operator (LASSO) logistic regression-to select key hub genes. These genes' classifying potential was validated in the training set and three other validation sets (GSE99967, GSE81622, and GSE49454). Further analyses included differential expression, co-expression, protein-protein interaction (PPI), gene set enrichment analysis (GSEA), and immune infiltration, centered on these hub genes. We also constructed TF-mRNA, miRNA-mRNA, and drug-target networks based on these hub genes using the ChEA3, miRcode, and PubChem databases.

RESULTS

Our investigation identified 761 differentially expressed genes (DEGs), mainly related to viral infection, inflammatory, and immune-related signaling pathways. The interaction between these DEGs and MRGs led to the identification of 27 distinct DE-MRGs. Key among these were FAM210B, MSRB2, LYRM7, IFI27, and SCO2, designated as hub genes through machine learning analysis. Their significant role in SLE classification was confirmed in both the training and validation sets. Additional analyses included differential expression, co-expression, PPI, GSEA, immune infiltration, and the construction of TF-mRNA, miRNA-mRNA, and drug-target networks.

CONCLUSIONS

This research represents a novel exploration into the MRGs of SLE, identifying FAM210B, MSRB2, LYRM7, IFI27, and SCO2 as significant candidates for classifying and therapeutic targeting.

Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic-associated molecular subtypes and genomic mutations

Objective: Due to a lack of effective therapy, triple-negative breast cancer (TNBC) is extremely poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity and targeted therapeutics. Methods: 115 TNBC patients from TCGA were combined into a virtual cohort and verified by other verification sets, discovering differentially expressed genes (DEGs). To identify reliable metabolic features, we applied the same procedures to five independent datasets to verify the identified TNBC subtypes, which differed in terms of prognosis, metabolic characteristics, immune infiltration, clinical features, somatic mutation, and drug sensitivity. Results: In general, TNBC could be classified into two metabolically distinct subtypes. C1 had high immune checkpoint genes expression and immune and stromal scores, demonstrating sensitivity to the treatment of PD-1 inhibitors. On the other hand, C2 displayed a high variation in metabolism pathways involved in carbohydrate, lipid, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with late pathological stage, low immune infiltration and poor prognosis. Interestingly, C2 had a high mutation frequency in PIK3CA, KMT2D, and KMT2C and displayed significant activation of the PI3K and angiogenesis pathways. As a final output, we created a 100-gene classifier to reliably differentiate the TNBC subtypes and AKR1B10 was a potential biomarker for C2 subtypes. Conclusion: In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies.

Renal interferon-inducible protein 16 expression is associated with disease activity and prognosis in lupus nephritis

BACKGROUND

Lupus nephritis (LN) is one of the most severe complications of systemic lupus erythematosus (SLE). However, the current management of LN remains unsatisfactory due to sneaky symptoms during early stages and lack of reliable predictors of disease progression.

METHODS

Bioinformatics and machine learning algorithms were initially used to explore the potential biomarkers for LN development. Identified biomarker expression was evaluated by immunohistochemistry (IHC) and multiplex immunofluorescence (IF) in 104 LN patients, 12 diabetic kidney disease (DKD) patients, 12 minimal change disease (MCD) patients, 12 IgA nephropathy (IgAN) patients and 14 normal controls (NC). The association of biomarker expression with clinicopathologic indices and prognosis was analyzed. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were utilized to explore potential mechanisms.

RESULTS

Interferon-inducible protein 16 (IFI16) was identified as a potential biomarker for LN. IFI16 was highly expressed in the kidneys of LN patients compared to those with MCD, DKD, IgAN or NC. IFI16 co-localized with certain renal and inflammatory cells. Glomerular IFI16 expression was correlated with pathological activity indices of LN, while tubulointerstitial IFI16 expression was correlated with pathological chronicity indices. Renal IFI16 expression was positively associated with systemic lupus erythematosus disease activity index (SLEDAI) and serum creatinine while negatively related to baseline eGFR and serum complement C3. Additionally, higher IFI16 expression was closely related to poorer prognosis of LN patients. GSEA and GSVA suggested that IFI16 expression was involved in adaptive immune-related processes of LN.

CONCLUSION

Renal IFI16 expression is a potential biomarker for disease activity and clinical prognosis in LN patients. Renal IFI16 levels may be used to shed light on predicting the renal response and develop precise therapy for LN.

Identification of Protein Complexes by Integrating Protein Abundance and Interaction Features Using a Deep Learning Strategy

Many essential cellular functions are carried out by multi-protein complexes that can be characterized by their protein-protein interactions. The interactions between protein subunits are critically dependent on the strengths of their interactions and their cellular abundances, both of which span orders of magnitude. Despite many efforts devoted to the global discovery of protein complexes by integrating large-scale protein abundance and interaction features, there is still room for improvement. Here, we integrated >7000 quantitative proteomic samples with three published affinity purification/co-fractionation mass spectrometry datasets into a deep learning framework to predict protein-protein interactions (PPIs), followed by the identification of protein complexes using a two-stage clustering strategy. Our deep-learning-technique-based classifier significantly outperformed recently published machine learning prediction models and in the process captured 5010 complexes containing over 9000 unique proteins. The vast majority of proteins in our predicted complexes exhibited low or no tissue specificity, which is an indication that the observed complexes tend to be ubiquitously expressed throughout all cell types and tissues. Interestingly, our combined approach increased the model sensitivity for low abundant proteins, which amongst other things allowed us to detect the interaction of MCM10, which connects to the replicative helicase complex via the MCM6 protein. The integration of protein abundances and their interaction features using a deep learning approach provided a comprehensive map of protein-protein interactions and a unique perspective on possible novel protein complexes.

Identification of NETs-related biomarkers and molecular clusters in systemic lupus erythematosus

Neutrophil extracellular traps (NETs) is an important process involved in the pathogenesis of systemic lupus erythematosus (SLE), but the potential mechanisms of NETs contributing to SLE at the genetic level have not been clearly investigated. This investigation aimed to explore the molecular characteristics of NETs-related genes (NRGs) in SLE based on bioinformatics analysis, and identify associated reliable biomarkers and molecular clusters. Dataset GSE45291 was acquired from the Gene Expression Omnibus repository and used as a training set for subsequent analysis. A total of 1006 differentially expressed genes (DEGs) were obtained, most of which were associated with multiple viral infections. The interaction of DEGs with NRGs revealed 8 differentially expressed NRGs (DE-NRGs). The correlation and protein-protein interaction analyses of these DE-NRGs were performed. Among them, HMGB1, ITGB2, and CREB5 were selected as hub genes by random forest, support vector machine, and least absolute shrinkage and selection operator algorithms. The significant diagnostic value for SLE was confirmed in the training set and three validation sets (GSE81622, GSE61635, and GSE122459). Additionally, three NETs-related sub-clusters were identified based on the hub genes' expression profiles analyzed by unsupervised consensus cluster assessment. Functional enrichment was performed among the three NETs subgroups, and the data revealed that cluster 1 highly expressed DEGs were prevalent in innate immune response pathways while that of cluster 3 were enriched in adaptive immune response pathways. Moreover, immune infiltration analysis also revealed that innate immune cells were markedly infiltrated in cluster 1 while the adaptive immune cells were upregulated in cluster 3. As per our knowledge, this investigation is the first to explore the molecular characteristics of NRGs in SLE, identify three potential biomarkers (HMGB1, ITGB2, and CREB5), and three distinct clusters based on these hub biomarkers.

Study on the role and pharmacology of cuproptosis in gastric cancer

Objective

Gastric cancer has a poor prognosis and high mortality. Cuproptosis, a novel programmed cell death, is rarely studied in gastric cancer. Studying the mechanism of cuproptosis in gastric cancer is conducive to the development of new drugs, improving the prognosis of patients and reducing the burden of disease.

Methods

The TCGA database was used to obtain transcriptome data from gastric cancer tissues and adjacent tissues. GSE66229 was used for external verification. Overlapping genes were obtained by crossing the genes obtained by differential analysis with those related to copper death. Eight characteristic genes were obtained by three dimensionality reduction methods: lasso, SVM, and random forest. ROC and nomogram were used to estimate the diagnostic efficacy of characteristic genes. The CIBERSORT method was used to assess immune infiltration. ConsensusClusterPlus was used for subtype classification. Discovery Studio software conducts molecular docking between drugs and target proteins.

Results

We have established the early diagnosis model of eight characteristic genes (ENTPD3, PDZD4, CNN1, GTPBP4, FPGS, UTP25, CENPW, and FAM111A) for gastric cancer. The results are validated by internal and external data, and the predictive power is good. The subtype classification and immune type analysis of gastric cancer samples were performed based on the consensus clustering method. We identified C2 as an immune subtype and C1 as a non-immune subtype. Small molecule drug targeting based on genes associated with cuproptosis predicts potential therapeutics for gastric cancer. Molecular docking revealed multiple forces between Dasatinib and CNN1.

Conclusion

The candidate drug Dasatinib may be effective in treating gastric cancer by affecting the expression of the cuproptosis signature gene.

High expression of TARS is associated with poor prognosis of endometrial cancer

INTRODUCTION

Endometrial cancer is the second largest and most common cancer in the world. It is urgent to explore novel biomarkers.

METHODS

Data were obtained from The Cancer Genome Atlas (TCGA) database. The receiver operating characteristic (ROC) curves, Kaplan-Meier curves and Cox analysis, nomograms, gene set enrichment analysis (GSEA) were conducted. Cell proliferation experiments were performed in Ishikawa cell.

RESULTS

TARS was significantly highly expressed in serous type, G3 grade, and deceased status. Significant association was between high TARS expression with poor overall survival (P = 0.0012) and poor disease specific survival (P = 0.0034). Significant differences were observed in advanced stage, G3 and G4, and old. The stage, diabetes, histologic grade, and TARS expression showed independent prognostic value for overall survival of endometrial cancer. The stage, histologic grade, and TARS expression showed independent prognostic value for disease specific survival of endometrial cancer. Activated CD4⁺ T cell, effector memory CD4⁺ T cell, memory B cell and type 2 T helper cell may participate in the high TARS expression related immune response in endometrial cancer. The CCK-8 results showed significantly inhibited cell proliferation in si-TARS (P < 0.05) and promoted cell proliferation in O-TARS (P < 0.05), confirmed by the colony formation and live/dead staining.

CONCLUSION

High TARS expression was found in endometrial cancer with prognostic and predictive value. This study will provide new biomarker TARS for diagnosis and prognosis of endometrial cancer.

Diagnostic, clustering, and immune cell infiltration analysis of m6A regulators in patients with sepsis

RNA N6-methladenosine (m6A) regulators are required for a variety of biological processes, including immune responses, and increasing evidence indicates that their dysregulation is closely associated with many diseases. However, the potential roles of m6A regulators in sepsis remain unknown. We comprehensively analyzed the transcriptional variations in and interactions of 26 m6A regulators in sepsis based on the Gene Expression Omnibus (GEO) database. A random forest (RF) model and nomogram were established to predict the occurrence and risk of sepsis in patients. Then, two different m6A subtypes were defined by consensus clustering analysis, and we explored the correlation between the subtypes and immune cells. We found that 17 of the 26 m6A regulators were significantly differentially expressed between patients with and without sepsis, and strong correlations among these 17 m6A regulators were revealed. Compared with the support vector machine (SVM) model, the RF model had better predictive ability, and therefore was used to construct a reliable nomogram containing 10 candidate m6A regulators to predict the risk of sepsis in patients. In addition, a consensus clustering algorithm was used to identify two different subtypes of m6A, which helped us distinguish different levels of immune cell infiltration and inflammation in patients with sepsis. Comprehensive analysis of m6A regulators in sepsis revealed their potential roles in sepsis occurrence, immune cell infiltration and inflammation in patients with sepsis. This study may contribute to the development of follow-up treatment strategies for sepsis.

LGALS1 was related to the prognosis of clear cell renal cell carcinoma identified by weighted correlation gene network analysis combined with differential gene expression analysis

Understanding the molecular mechanism of clear cell renal cell carcinoma (ccRCC) is essential for predicting the prognosis and developing new targeted therapies. Our study is to identify hub genes related to ccRCC and to further analyze its prognostic significance. The ccRCC gene expression profiles of GSE46699 from the Gene Expression Omnibus (GEO) database and datasets from the Cancer Genome Atlas Database The Cancer Genome Atlas were used for the Weighted Gene Co-expression Network Analysis (WGCNA) and differential gene expression analysis. We screened out 397 overlapping genes from the four sets of results, and then performed Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genome (KEGG) pathways. In addition, the protein-protein interaction (PPI) network of 397 overlapping genes was mapped using the STRING database. We identified ten hub genes (KNG1, TIMP1, ALB, C3, GPC3, VCAN, P4HB, CHGB, LGALS1, EGF) using the CytoHubba plugin of Cytoscape based on the Maximal Clique Centrality (MCC) score. According to Kaplan-Meier survival analysis, higher expression of LGALS1 and TIMP1 was related to poorer overall survival (OS) in patients with ccRCC. Univariate and multivariate Cox proportional hazard analysis showed that the expression of LGALS1 was an independent risk factor for poor prognosis. Moreover, the higher the clinical grade and stage of ccRCC, the higher the expression of LGALS1. LGALS1 may play an important role in developing ccRCC and may be potential a biomarker for prognosis and treatment targets.

Comprehensive analysis of cuproptosis-related genes and tumor microenvironment infiltration characterization in breast cancer

Background

Cuproptosis is a newly discovered programmed cell death dependent on overload copper-induced mitochondrial respiration dysregulation. The positive response to immunotherapy, one of the most important treatments for invasive breast cancer, depends on the dynamic balance between tumor cells and infiltrating lymphocytes in the tumor microenvironment (TME). However, cuproptosis-related genes (CRGs) in clinical prognosis, immune cell infiltration, and immunotherapy response remain unclear in breast cancer progression.

Methods

The expression and mutation patterns of 12 cuproptosis-related genes were systematically evaluated in the BRCA training group. Through unsupervised clustering analysis and developing a cuproptosis-related scoring system, we further explored the relationship between cuproptosis and breast cancer progression, prognosis, immune cell infiltration, and immunotherapy.

Results

We identified two distinct CuproptosisClusters, which were correlated with the different patterns between clinicopathological features, prognosis, and immune cell infiltration. Moreover, the differences of the three cuproptosis-related gene subtypes were evaluated based on the CuproptosisCluster-related DEGs. Then, a cuproptosis-related gene signature (PGK1, SLC52A2, SEC14L2, RAD23B, SLC16A6, CCL5, and MAL2) and the scoring system were constructed to quantify the cuproptosis pattern of BRCA patients in the training cohort, and the testing cohorts validated them. Specifically, patients from the low-CRG_score group were characterized by higher immune cell infiltration, immune checkpoint expression, immune checkpoint inhibitor (ICI) scores, and greater sensitivity to immunotherapy. Finally, we screened out RAD23B as a favorable target and indicated its expression was associated with breast cancer progression, drug resistance, and poor prognosis in BRCA patients by performing real-time RT-PCR, cell viability, and IC50 assay.

Conclusions

Our results confirmed the essential function of cuproptosis in regulating the progression, prognosis, immune cell infiltration, and response to breast cancer immunotherapy. Quantifying cuproptosis patterns and constructing a CRG_score could help explore the potential molecular mechanisms of cuproptosis regulating BRCA advancement and provide more effective immunotherapy and chemotherapy targets.

Identification of diagnostic gene biomarkers and immune infiltration in patients with diabetic kidney disease using machine learning strategies and bioinformatic analysis

Objective

Diabetic kidney disease (DKD) is the leading cause of chronic kidney disease and end-stage renal disease worldwide. Early diagnosis is critical to prevent its progression. The aim of this study was to identify potential diagnostic biomarkers for DKD, illustrate the biological processes related to the biomarkers and investigate the relationship between them and immune cell infiltration.

Materials and methods

Gene expression profiles (GSE30528, GSE96804, and GSE99339) for samples obtained from DKD and controls were downloaded from the Gene Expression Omnibus database as a training set, and the gene expression profiles (GSE47185 and GSE30122) were downloaded as a validation set. Differentially expressed genes (DEGs) were identified using the training set, and functional correlation analyses were performed. The least absolute shrinkage and selection operator (LASSO), support vector machine-recursive feature elimination (SVM-RFE), and random forests (RF) were performed to identify potential diagnostic biomarkers. To evaluate the diagnostic efficacy of these potential biomarkers, receiver operating characteristic (ROC) curves were plotted separately for the training and validation sets, and immunohistochemical (IHC) staining for biomarkers was performed in the DKD and control kidney tissues. In addition, the CIBERSORT, XCELL and TIMER algorithms were employed to assess the infiltration of immune cells in DKD, and the relationships between the biomarkers and infiltrating immune cells were also investigated.

Results

A total of 95 DEGs were identified. Using three machine learning algorithms, DUSP1 and PRKAR2B were identified as potential biomarker genes for the diagnosis of DKD. The diagnostic efficacy of DUSP1 and PRKAR2B was assessed using the areas under the curves in the ROC analysis of the training set (0.945 and 0.932, respectively) and validation set (0.789 and 0.709, respectively). IHC staining suggested that the expression levels of DUSP1 and PRKAR2B were significantly lower in DKD patients compared to normal. Immune cell infiltration analysis showed that B memory cells, gamma delta T cells, macrophages, and neutrophils may be involved in the development of DKD. Furthermore, both of the candidate genes are associated with these immune cell subtypes to varying extents.

Conclusion

DUSP1 and PRKAR2B are potential diagnostic markers of DKD, and they are closely associated with immune cell infiltration.

Network Pharmacology and Molecular Docking Analyses Unveil the Mechanisms of Yiguanjian Decoction against Parkinson’s Disease from Inner/Outer Brain Perspective

Objective

This study aims to explore the pharmacodynamic mechanism of Yiguanjian (YGJ) decoction against Parkinson's disease (PD) through integrating the central nervous (inner brain) and peripheral system (outer brain) relationship spectrum.

Methods

The active components of YGJ were achieved from the TCMSP, TCMID, and TCM@Taiwan databases. The blood-brain barrier (BBB) permeability of the active components along with their corresponding targets was evaluated utilizing the existing website, namely, SwissADME and SwissTargetPrediction. The targets of PD were determined through database retrieval. The interaction network was constructed upon the STRING database, followed by the visualization using Cytoscape software. Then, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses on potential targets. Finally, the molecular docking approach was employed to assess the binding affinity between key components and key targets.

Results

Overall, we identified 79 active components, 128 potential targets of YGJ, and 97 potential targets of YGJ-BBB potentially suitable for the treatment of PD. GO and KEGG analyses showed that the YGJ treatment of PD mainly relied on PI3K-Akt pathway while the YGJ-BBB was mostly involved in endocrine resistance. The molecular docking results displayed high affinity between multiple compounds and targets in accordance with previous observations.

Conclusions

Our study unveiled the potential mechanisms of YGJ against PD from a systemic perspective: (1) for the YGJ, they have potential exerting effects on the peripheral system and inhibiting neuronal apoptosis through regulating the PI3K-Akt pathway; (2) for the YGJ-BBB, they can directly modulate endocrine resistance of the central nervous and holistically enhance body resistance to PD along with YGJ on PI3K-Akt pathway.

The Role of Gender-Related Immune Genes in Childhood Acute Myeloid Leukemia

The study of immune genes and immune cells is highly focused in recent years. To find immunological genes with prognostic value, the current study examines childhood acute myeloid leukemia according to gender. The TARGET database was used to gather the "mRNA expression profile data" and relevant clinical data of children with AML. To normalize processing and find differentially expressed genes (DEG) between male and female subgroups, the limma software package is utilized. We identified prognostic-related genes and built models using LASSO, multivariate Cox, and univariate Cox analysis. The prognostic significance of prognostic genes was then examined through the processing of survival analysis and risk score (RS) calculation. We investigated the connections between immune cells and prognostic genes as well as the connections between prognostic genes and medications. Finally, five immune genes from the TARGET database have been identified. These immune genes are considerably correlated to the prognosis of male patients.

Development and validation of an epithelial–mesenchymal transition-related gene signature for predicting prognosis

BACKGROUND

Currently, there are many therapeutic methods for lung adenocarcinoma (LUAD), but the 5-year survival rate is still only 15% at later stages. Epithelial– mesenchymal transition (EMT) has been shown to be closely associated with local dissemination and subsequent metastasis of solid tumors. However, the role of EMT in the occurrence and development of LUAD remains unclear.

AIM

To further elucidate the value of EMT-related genes in LUAD prognosis.

METHODS

Univariate, least absolute shrinkage and selection operator, and multivariate Cox regression analyses were applied to establish and validate a new EMT-related gene signature for predicting LUAD prognosis. The risk model was evaluated by Kaplan–Meier survival analysis, principal component analysis, and functional enrichment analysis and was used for nomogram construction. The potential structures of drugs to which LUAD is sensitive were discussed with respect to EMT-related genes in this model.

RESULTS

Thirty-three differentially expressed genes related to EMT were found to be highly associated with overall survival (OS) by using univariate Cox regression analysis (log2FC ≥ 1, false discovery rate < 0.001). A prognostic signature of 7 EMT-associated genes was developed to divide patients into two risk groups by high or low risk scores. Kaplan–Meier survival analysis showed that the OS of patients in the high-risk group was significantly poorer than that of patients in the low-risk group (P < 0.05). Multivariate Cox regression analysis showed that the risk score was an independent risk factor for OS (HR > 1, P < 0.05). The results of receiver operator characteristic curve analysis suggested that the 7-gene signature had a perfect ability to predict prognosis (all area under the curves > 0.5).

CONCLUSION

The EMT-associated gene signature classifier could be used as a feasible indicator for predicting OS.

Predicting recurrence and metastasis risk of endometrial carcinoma via prognostic signatures identified from multi-omics data

Objectives

Endometrial carcinoma (EC) is one of the three major gynecological malignancies, in which 15% - 20% patients will have recurrence and metastasis. Though there are many studies on the prognosis on this cancer, the performances of existing models evaluating the risk of its recurrence and metastasis are yet to be improved. In addition, a comprehensive multi-omics analyses on the prognostic signatures of EC are on demand. In this study, we aimed to construct a relatively stable and reliable model for predicting recurrence and metastasis of EC. This will help determine the risk level of patients and choose appropriate adjuvant therapy, thereby avoiding improper treatment, and improving the prognosis of patients.

Methods

The mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), copy number variation (CNV) data and clinical information of patients with EC were downloaded from The Cancer Genome Atlas (TCGA). Differential expression analyses were performed between the recurrence or metastasis group and the non-recurrence/metastasis group. Then, we screened potential prognostic markers from the four kinds of omics data respectively and established prediction models using three classifiers.

Results

We achieved differential expressed mRNAs, lncRNAs, miRNAs and CNVs between the two groups. According to feature selection scores by the random forest algorithm, 275 CNV features, 50 lncRNA features, 150 miRNA features and 150 mRNA features were selected, respectively. And the prediction model constructed by the features of lncRNA data using random forest method showed the best performance, with an area under the curve of 0.763, and an accuracy of 0.819 under 10-fold cross-validation.

Conclusion

We developed a computational model using omics information, which is able to predicting recurrence and metastasis risk of EC accurately.

Identification of Human Retinal Organoid Cell Differentiation-Related Genes via Single-Cell Sequencing Data Analysis

Objective. To study the development process of the human retina, we analyzed the development track of main cell types and transitional cell populations, identifying the retinal organoid cell differentiation-related genes (RDRGs). Methods. Single-cell RNA sequencing data (scRNA-Seq) of human retinal organoids were downloaded from Gene Expression Omnibus (GEO) database in this study. Data were processed with quality analysis and analysis of variance. Principal component analysis and $t$ -distributed stochastic neighbor embedding were used to conduct dimension reduction analysis and type annotation for the screened data. Marker genes and RDRGs were identified by differential analysis. Cell differentiation characteristics were determined by trajectory analysis. Enrichment pathways were analyzed by Gene Ontology(GO) and Kyoto Encyclopedia of Genes and Genomes(KEGG), and functional modules were obtained by protein-protein interaction (PPI) network analysis. Results. iPSCs were mainly located at the root of differentiation trajectory, while neurons and astrocytes were distributed in different branches, respectively. Meanwhile, 220 RDRGs were obtained. They were involved in the biological functions related to vision and visual development, as well as significantly enriched in signaling pathways associated with retinal vascular development and retinal neuroregulation. Protein-protein interaction network construction and functional subnetwork analysis were conducted on RDRGs, and two functional submodules were obtained. The enrichment analysis presented that the two submodules played a vital role in retinal development, visual perception, and cell respiration. Conclusions. This study identified RDRGs and revealed the biological functions involved in these genes, which are expected to provide evidence for researching retinal development and diseases.

Network analysis combined with pharmacological evaluation strategy to reveal the mechanism of Tibetan medicine Wuwei Shexiang pills in treating rheumatoid arthritis

Tibetan medicine is an important part of traditional Chinese medicine and a significant representative of ethnic medicine in China. Tibetan medicine is gradually recognized by the world for its unique curative effects. Wuwei Shexiang pills (WPW) has been widely used to treat “Zhenbu” disease (Also known as rheumatoid arthritis) in Tibetan medicine, however, its potential bioactive ingredients and mechanism for RA treatment remain unclear. In this study, we used a combination of gas chromatography-mass spectrometry (GC-MS), ultra-performance liquid chromatography coupled with quadrupole time-of-fight mass spectrometry (UPLC-Q-TOF/MS), network analysis and experimental validation to elucidate the potential pharmacodynamic substances and mechanisms of WPW in the treatment of rheumatoid arthritis (RA). The results showed that songoramine, cheilanthifoline, saussureanine C, acoric acid, arjunolic acid, peraksine, ellagic acid, arjungenin and other 11 components may be the main activities of WPW in the treatment of RA. PIK3CA, AKT, MAPK, IL-6, TNF, MMP1, MMP3, and CDK1 are considered as core targets. PI3K-AKT, MAPK, apoptosis, cell cycle, and other signaling pathways may be the key pathways for WPW to play a role in the treatment of RA. Furthermore, we validated the underlying molecular mechanism of WPW predicted by network analysis and demonstrated its possible mechanism through in vivo animal experiments. It was found that WPW could significantly improve the degree of paw swelling, and reduce ankle joint diameter and arthritis index. Further histomorphological analysis showed that WPW could reduce the degree of synovial tissue inflammation and ankle joint cartilage damage. Meanwhile, WPW could down-regulate the levels of IL-6, IL-1β, and IL-17, and increase the levels of IL-10 and IL-4 in the serum of AA rats. TUNEL staining confirmed that WPW could significantly promote the apoptosis of synovial cells. Moreover, the immunohistochemical results showed that WPW decreased the expression of PI3K, AKT, MAPK, MMP1, MMP3, CDK1, and Bcl-2, as well as increased the expression of Bax protein. In conclusion, we successfully combined GC-MS, UPLC-Q-TOF/MS, network analysis, and experimental validation strategies to elucidate the inhibition of inflammation by WPW in AA model rats via PI3K/AKT, MAPK, cell cycle and apoptotic pathways process. This not only provides new evidence for the study of potential pharmacodynamic substances and the mechanism of WPW in the treatment of RA, but also provides ideas for the study of other Tibetan medicine compound preparations.

Systems pharmacology, proteomics and in vivo studies identification of mechanisms of cerebral ischemia injury amelioration by Huanglian Jiedu Decoction

ETHNOPHARMACOLOGICAL RELEVANCE

Huanglian Jiedu Decoction (HLJDD) has the effect of clearing heat and detoxifying, and has been considered as an effective prescription for cerebral ischemia (CI) for thousands of years in traditional Chinese medicine (TCM). It can improve the quality of life of patients with ischemic stroke, but its pharmacological mechanism remains unclear.

AIM OF THE STUDY

The study aimed to explore the pharmacological action and potential mechanism of HLJDD against CI by systems pharmacology, proteomics and in vivo experiments.

MATERIALS AND METHODS

In this study, databases such as TCMIP V2.0 and Genecards were used to predict compounds, targets and CI related targets, and network topology criteria of protein-protein interaction (PPI) network was used to screen core targets. The Database for Annotation, Visualization and Integrated Discovery database (DAVID) was used to discover biological processes and pathways. In addition, molecular docking was performed between the screened core biological active compounds and targets to verify the binding activity. Finally, proteomics and Western blot were performed on cerebral cortex tissues of middle cerebral artery occlusion (MCAO) model rats with HLJDD intervention to further verify the predicted results.

RESULTS

77 compounds and 308 targets of HLJDD were identified, 54 of which were predicted to be associated with cerebral ischemia. PPI network and enrichment results showed that 8 targets, including AKT1, PTGS2 and TLR4, were core targets of HLJDD in CI. And 19 signaling pathways, including Rap1 signaling pathway, cAMP signaling pathway and arachidonic acid metabolism, were identified as key pathways to the therapeutic activity of HLJDD in CI. Combined with proteomics studies, we identified that Rap1 signaling pathway and upstream and downstream targets were the key mechanisms. Molecular biology experiments showed that RAP1A and AKT expression levels were significantly up-regulated in middle cerebral artery occlusion (MCAO) rats treated with HLJDD (P < 0.0001), GRIN1 expression level was significantly down-regulated (P < 0.0001). However, ACTB expression level was slightly down-regulated (P > 0.05), which may be related to the biological function.

CONCLUSION

This study confirms the pharmacological effect of HLJDD on cerebral ischemia. These results indicate that HLJDD mediates various pathways such as inhibition of apoptosis, regulation of oxygen balance, inhibition of excitatory toxicity and maintenance of basic cell functions to improve CI by regulating Rap1 signaling pathway.

Integrated bioinformatics data analysis reveals a risk signature and PKD1 induced progression in endometrial cancer patients with postmenopausal status

Background: Endometrial cancer (EC) is one of the most common type of female genital malignancies. The purpose of the present study was to reveal the underlying oncogene and mechanism that played a pivotal role in postmenopausal EC patients.

Methods: Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of EC patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to identify significant gene modules and hub genes associated with postmenopausal status in EC patients. LASSO regression was conducted to build and validate the risk model. Finally, expression of hub gene was validated in pre- and post-menopausal EC patients in our center.

Results: 1240 common genes were used to construct the WGCNA model. According to the WGCNA results, we identified a brown module with 471 genes which was significantly associated with postmenopausal status in EC patients. Furthermore, we constructed an 11-gene risk signature to predict the overall survival of EC patients. The Kaplan–Meier curve and area under the ROC curve (AUC) of this model showed high accuracy in prediction. We also validate the risk model in patients in our center and it also has a high accuracy. Among the 11 genes, PKD1 was recognized as a potential biomarker in the progression of EC patients with postmenopausal status.

Conclusion: Taken together, we uncovered a common PKD1-mediated mechanism underlying postmenopausal EC patients’ progression by integrated analyses. This finding may improve targeted therapy for EC patients.

miR-155-5p regulates hypoxia-induced pulmonary artery smooth muscle cell function by targeting PYGL

Pulmonary arterial hypertension (PAH) is a cardiovascular disease that has high incidence and causes massive deaths. miR-155-5p/PYGL pathway was revealed to play a crucial role in PAH by weighted gene co-expression network analysis (WGCNA). The potential mechanism of miR-155-5p in regulating hypoxia-induced pulmonary artery smooth muscle cell (PASMC) function was analyzed through in vitro experiments. Hypoxia treatment stimulated the proliferation of PASMCs and increased the expression of vascular endothelial growth factor (VEGF) and hypoxia-inducible factor-1α (HIF-1α). At the same time, revealed by qRT-PCR and western blot, the level of miR-155-5p was raised, and the level of PYGL was decreased in hypoxia-induced PASMCs. Through CCK-8 assay, transwell assay and flow cytometry, it was revealed that miR-155-5p inhibitor remarkably inhibited the cell proliferation and migration and decreased the proportion of hypoxia-stimulated PASMCs in S and G2/M phases. Dual-luciferase reporter system was subsequently applied to validate the straight regulation of miR-155-5p on PYGL based on the analysis of online database. Furthermore, siPYGL was revealed to reverse the influence of miR-155-5p inhibitor on hypoxia-induced PASMCs. These outcomes indicate that the increased level of miR-155-5p in hypoxia-stimulated PASMCs could enhance the cell proliferation, cell migration, and cell cycle progression by targeting PYGL directly. This study may supply novel treatment strategies for PAH.Abbreviations: PH, pulmonary hypertension; PAH, pulmonary arterial hypertension; WGCNA, weighted gene co-expression network analysis; PASMCs, pulmonary artery smooth muscle cells; VEGF, vascular endothelial growth factor; HIF-1α, hypoxia-inducible factor-1α; SMCs, smooth muscle cells; DEGs, differentially expressed genes; GEO, Gene Expression Omnibus; GO, Gene Ontology; KEGG, Kyoto Encyclopedia of Genes and Genomes; FBS, fetal bovine serum; OD, optical density; BCA, bicinchoninic acid; PVDF, polyvinylidene fluoride; PBS, phosphate-buffered saline; BP, biological process; MF, molecular function; CC, cell component.

Comprehensive Analysis of the Prognostic Signature of Mutation-Derived Genome Instability-Related lncRNAs for Patients With Endometrial Cancer

Background: Emerging evidence shows that genome instability-related long non-coding RNAs (lncRNAs) contribute to tumor–cell proliferation, differentiation, and metastasis. However, the biological functions and molecular mechanisms of genome instability-related lncRNAs in endometrial cancer (EC) are underexplored.

Methods: EC RNA sequencing and corresponding clinical data obtained from The Cancer Genome Atlas (TCGA) database were used to screen prognostic lncRNAs associated with genomic instability via univariate and multivariate Cox regression analysis. The genomic instability-related lncRNA signature (GILncSig) was developed to assess the prognostic risk of high- and low-risk groups. The prediction performance was analyzed using receiver operating characteristic (ROC) curves. The immune status and mutational loading of different risk groups were compared. The Genomics of Drug Sensitivity in Cancer (GDSC) and the CellMiner database were used to elucidate the relationship between the correlation of prognostic lncRNAs and drug sensitivity. Finally, we used quantitative real-time PCR (qRT-PCR) to detect the expression levels of genomic instability-related lncRNAs in clinical samples.

Results: GILncSig was built using five lncRNAs (AC007389.3, PIK3CD-AS2, LINC01224, AC129507.4, and GLIS3-AS1) associated with genomic instability, and their expression levels were verified using qRT-PCR. Further analysis revealed that risk score was negatively correlated with prognosis, and the ROC curve demonstrated the higher accuracy of GILncSig. Patients with a lower risk score had higher immune cell infiltration, a higher immune score, lower tumor purity, higher immunophenoscores (IPSs), lower mismatch repair protein expression, higher microsatellite instability (MSI), and a higher tumor mutation burden (TMB). Furthermore, the level of expression of prognostic lncRNAs was significantly related to the sensitivity of cancer cells to anti-tumor drugs.

Conclusion: A novel signature composed of five prognostic lncRNAs associated with genome instability can be used to predict prognosis, influence immune status, and chemotherapeutic drug sensitivity in EC.

Establishing a Prognostic Signature Based on Epithelial-Mesenchymal Transition-Related Genes for Endometrial Cancer Patients

Backgrounds

Epithelial-mesenchymal transition (EMT) is a sequential process where tumor cells develop from the epithelial state to the mesenchymal state. EMT contributes to various tumor functions including initiation, propagating potential, and resistance to therapy, thus affecting the survival time of patients. The aim of this research is to set up an EMT-related prognostic signature for endometrial cancer (EC).

Methods

EMT-related gene (ERG) expression and clinical data were acquired from The Cancer Genome Atlas (TCGA). The entire set was randomly divided into two sets, one for contributing the risk model (risk score) and the other for validating. Univariate and multivariate Cox proportional hazards regression analyses were applied to the training set to select the prognostic ERGs. The expression of 10 ERGs was confirmed by qRT-PCR in clinical samples. Then, we developed a nomogram predicting 1-/3-/5-year survival possibility combining the risk score and clinical factors. The entire set was stratified into the high- and low-risk groups, which was used to analyze the immune infiltrating, tumorigenesis pathways, and response to drugs.

Results

A total of 220 genes were screened out from 1,316 ERGs for their differential expression in tumor versus normal. Next, 10 genes were found to be associated with overall survival (OS) in EC, and the expression was validated by qRT-PCR using clinical samples, so we constructed a 10-ERG-based risk score to distinguish high-/low-risk patients and a nomogram to predict survival rate. The calibration plots proved the predictive value of our model. Gene Set Enrichment Analysis (GSEA) discovered that in the low-risk group, immune-related pathways were enriched; in the high-risk group, tumorigenesis pathways were enriched. The low-risk group showed more immune activities, higher tumor mutational burden (TMB), and higher CTAL4/PD1 expression, which was in line with a better response to immune checkpoint inhibitors. Nevertheless, response to chemotherapeutic drugs turned out better in the high-risk group. The high-risk group had higher N ⁶-methyladenosine (m⁶A) RNA expression, microsatellite instability level, and stemness indices.

Conclusion

We constructed the ERG-related signature model to predict the prognosis of EC patients. What is more, it might offer a reference for predicting individualized response to immune checkpoint inhibitors and chemotherapeutic drugs.

Identification of Novel Prognostic Markers Associated With Laryngeal Squamous Cell Carcinoma Using Comprehensive Analysis

Background

Laryngeal squamous cell carcinoma (LSCC) is a leading malignant cancer of the head and neck. Patients with LSCC, in which the cancer has infiltrated and metastasized, have a poor prognosis. Therefore, there is an urgent need to identify more potential targets for drugs and biomarkers for early diagnosis.

Methods

RNA sequence data from LSCC and patients’ clinical traits were obtained from the Gene Expression Omnibus (GEO) (GSE142083) and The Cancer Genome Atlas (TCGA) database. Differentially expressed gene (DEG) analysis and weighted gene co-expression network analysis (WGCNA) were performed to identify hub genes. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, prognostic value analysis, receiver operating characteristic (ROC) curve analysis, gene mutation analysis, tumor-infiltrating immune cell abundance profile estimation, gene set variation analysis (GSVA), and gene set enrichment analysis (GSEA) were performed. Single-gene RNA sequencing data were obtained from the GSE150321 dataset. Cell proliferation and viability were confirmed by the CCK-8 assay and real-time PCR.

Results

A total of 701 DEGs, including 329 upregulated and 372 downregulated genes, were screened in the GSE142083 dataset. Using WGCNA, three modules were identified to be closely related to LSCC. After intersecting the DEGs and performing univariate and multivariate Cox analyses, a novel prognostic model based on three genes (SLC35C1, HOXB7, and TEDC2) for LSCC was established. Interfering TEDC2 expression inhibited tumor cell proliferation and migration.

Conclusions

Our results show that SLC35C1, HOXB7, and TEDC2 have the potential to become new therapeutic targets and prognostic biomarkers for LSCC.

Development and Clinical Validation of Novel 8-Gene Prognostic Signature Associated With the Proportion of Regulatory T Cells by Weighted Gene Co-Expression Network Analysis in Uterine Corpus Endometrial Carcinoma

Background

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with low survival rate and poor prognosis. The traditional clinicopathological staging is insufficient to estimate the prognosis of UCEC. It is necessary to select a more effective prognostic signature of UCEC to predict the prognosis and immunotherapy effect of UCEC.

Methods

CIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen modules related to regulatory T (Treg) cells. Subsequently, univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses were used to identify the genes in key modules. The difference in overall survival (OS) between high- and low-risk patients was analyzed by Kaplan-Meier analysis. The Tregs-related risk signature (TRRS) was screened by uni- and multivariate Cox analyses. Afterward, we analyzed the expression difference of TRRS and verified its ability to predict the prognosis of UCEC and the effect of immunotherapy.

Results

Red module has the highest correlation with Tregs among all clustered modules. Pathways enrichment indicated that the related processes of UCEC were primarily associated to the immune system. Eight genes (ZSWIM1, NPRL3, GOLGA7, ST6GALNAC4, CDC16, ITPK1, PCSK4, and CORO1B) were selected to construct TRRS. We found that this TRRS is a significantly independent prognostic factor of UCEC. Low-risk patients have higher overall survival than high-risk patients. The immune status of different groups was different, and tumor-related pathways were enriched in patients with higher risk score. Low-risk patients are more likely take higher tumor mutation burden (TMB). Meanwhile, they are more sensitive to chemotherapy than patients with high-risk score, which indicated a superior prognosis. Immune checkpoints such as PD-1, CTLA4, PD-L1, and PD-L2 all had a higher expression level in low-risk group. TRRS expression really has a relevance with the sensitivity of UCEC patients to chemotherapeutic drugs.

Conclusion

We developed and validated a TRRS to estimate the prognosis and reflect the immune status of UCEC, which could accurately assess the prognosis of patients with UCEC and supply personalized treatments for them.

Integrated bioinformatics analysis reveals dynamic candidate genes and signaling pathways involved in the progression and prognosis of diffuse large B-cell lymphoma

Background

Diffuse large B-cell lymphoma (DLBCL) is a highly heterogeneous malignancy with varied outcomes. However, the fundamental mechanisms remain to be fully defined.

Aim

We aimed to identify core differentially co-expressed hub genes and perturbed pathways relevant to the pathogenesis and prognosis of DLBCL.

Methods

We retrieved the raw gene expression profile and clinical information of GSE12453 from the Gene Expression Omnibus (GEO) database. We used integrated bioinformatics analysis to identify differentially co-expressed genes. The CIBERSORT analysis was also applied to predict tumor-infiltrating immune cells (TIICs) in the GSE12453 dataset. We performed survival and ssGSEA (single-sample Gene Set Enrichment Analysis) (for TIICs) analyses and validated the hub genes using GEPIA2 and an independent GSE31312 dataset.

Results

We identified 46 differentially co-expressed hub genes in the GSE12453 dataset. Gene expression levels and survival analysis found 15 differentially co-expressed core hub genes. The core genes prognostic values and expression levels were further validated in the GEPIA2 database and GSE31312 dataset to be reliable (p < 0.01). The core genes’ main KEGG (Kyoto Encyclopedia of Genes and Genomes) pathway enrichments were Ribosome and Coronavirus disease-COVID-19. High expressions of the 15 core hub genes had prognostic value in DLBCL. The core genes showed significant predictive accuracy in distinguishing DLBCL cases from non-tumor controls, with the area under the curve (AUC) ranging from 0.992 to 1.00. Finally, CIBERSORT analysis on GSE12453 revealed immune cells, including activated memory CD4⁺ T cells and M0, M1, and M2-macrophages as the infiltrates in the DLBCL microenvironment.

Conclusion

Our study found differentially co-expressed core hub genes and relevant pathways involved in ribosome and COVID-19 disease that may be potential targets for prognosis and novel therapeutic intervention in DLBCL.

Atlas of Prenatal Hair Follicle Morphogenesis Using the Pig as a Model System

The pig is an increasingly popular biomedical model, but only a few in depth data exist on its studies in hair follicle (HF) morphogenesis and development. Hence, the objective of this study was to identify the suitability of the pig as an animal model for human hair research. We performed a classification of pig HF morphogenesis stages and hair types. All four different hair types sampled from 17 different body parts in pig were similar to those of human. The Guard_2 sub-type was more similar to type II human scalp hair while Guard_1, Awl, Auchene, and Zigzag were similar to type I scalp hair. Based on morphological observation and marker gene expression of HF at 11 different embryonic days and six postnatal days, we classified pig HF morphogenesis development from E41 to P45 into three main periods - induction (E37-E41), organogenesis (E41-E85), and cytodifferentiation (>E85). Furthermore, we demonstrated that human and pig share high similarities in HF morphogenesis occurrence time (early/mid gestational) and marker gene expression patterns. Our findings will facilitate the study of human follicle morphogenesis and research on complex hair diseases and offer researchers a suitable model for human hair research.

Comprehensive Analysis of Pyroptosis-Related Genes and Tumor Microenvironment Infiltration Characterization in Breast Cancer

Background

Immunotherapy has emerged as a significant strategy to treat numerous tumors. The positive response to immunotherapy depends on the dynamic interaction between tumor cells and infiltrating lymphocytes in the tumor microenvironment (TME). Pyroptosis, inflammation-induced cell death, is intricately associated with several tumors. However, the relationship between pyroptosis and clinical prognosis, immune cell infiltration, and immunotherapy effect is unclear in breast cancer (BRCA).

Methods

We comprehensively evaluated 33 pyroptosis-related genes and systematically assessed the relationship between pyroptosis and tumor progression, prognosis, and immune cell infiltration. The PyroptosisScore was used to quantify the pyroptosis pattern of a single tumor patient. We then assessed their values for predicting prognoses and therapeutic responses in BRCA.

Results

Three different modes of PyroptosisClusters were determined. The characteristics of TME cell infiltration in these three PyroptosisClusters were highly consistent with three immunophenotypes of tumors, including immune-excluded, immune-inflamed, and immune-desert phenotypes. Comprehensive bioinformatics analysis revealed that patients with a low PyroptosisScore had higher immune checkpoint expression, higher immune checkpoint inhibitor (ICI) scores, increased immune microenvironment infiltration, and were more sensitive to immunotherapy than those with a high PyroptosisScore.

Conclusions

Our findings revealed the crucial role of pyroptosis in maintaining the diversity and complexity of TME. Pyroptosis is closely related to tumor progression, tumor prognosis, and immunotherapy response. Evaluating the PyroptosisScore of a single tumor can assist in understanding the characteristics of TME infiltration and lead to the development of more effective immunotherapy strategies.

The modulatory properties of Astragalus membranaceus treatment on endometrial cancer: an integrated pharmacological method

Astragalus membranaceus is a traditional Chinese medicine and has been used for adjuvant clinical therapy for a variety of cancers. However, the mechanism of its action on endometrial carcinoma is unclear. Based on the Gene Expression Omnibus (GEO) database, the Cancer Genome Atlas (TCGA) database, and the Traditional Chinese Medicine System Pharmacology Database (TCMSP™), the drug and target compounds were initially screened to construct a common network module. Twenty active compounds in Astragalus membranaceus were successfully identified, which hit by 463 potential targets related to endometrial cancer. Eight of the more highly predictive compounds (such as Jaranol, Bifendate, Isorhamnetin, Calycosin, 7-O-methylisomucronulatol, Formononetin, Kaempferol, Quercetin) were involved in DNA integrity checkpoint, cyclin-dependent protein kinase holoenzyme complex, and histone kinase activity. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway confirmed that Astragalus membranaceus might play a role in the treatment of endometrial cancer through p53 signalling pathway, transcriptional misregulation in cancer, and endometrial cancer signalling pathway. Drug-target-pathway networks were constructed using Cytoscape to provide a visual perspective. In addition, we verified that formononetin inhibited the proliferation of endometrial cancer cells through cell viability tests and clone formation tests. And qPCR and western blot found that formononetin exerts anti-cancer effects by promoting the expression of estrogen receptor beta (ERβ) and p53. Based on a systematic network pharmacology approach, our works successfully predict the active ingredients and potential targets of Astragalus membranaceus for application to endometrial cancer and helps to illustrate mechanism of action on a comprehensive level.

Identification of Biomarkers for Cervical Cancer Radiotherapy Resistance Based on RNA Sequencing Data

Cervical cancer as a common gynecological malignancy threatens the health and lives of women. Resistance to radiotherapy is the primary cause of treatment failure and is mainly related to difference in the inherent vulnerability of tumors after radiotherapy. Here, we investigated signature genes associated with poor response to radiotherapy by analyzing an independent cervical cancer dataset from the Gene Expression Omnibus, including pre-irradiation and mid-irradiation information. A total of 316 differentially expressed genes were significantly identified. The correlations between these genes were investigated through the Pearson correlation analysis. Subsequently, random forest model was used in determining cancer-related genes, and all genes were ranked by random forest scoring. The top 30 candidate genes were selected for uncovering their biological functions. Functional enrichment analysis revealed that the biological functions chiefly enriched in tumor immune responses, such as cellular defense response, negative regulation of immune system process, T cell activation, neutrophil activation involved in immune response, regulation of antigen processing and presentation, and peptidyl-tyrosine autophosphorylation. Finally, the top 30 genes were screened and analyzed through literature verification. After validation, 10 genes (KLRK1, LCK, KIF20A, CD247, FASLG, CD163, ZAP70, CD8B, ZNF683, and F10) were to our objective. Overall, the present research confirmed that integrated bioinformatics methods can contribute to the understanding of the molecular mechanisms and potential therapeutic targets underlying radiotherapy resistance in cervical cancer.

Mechanism-Centric Approaches for Biomarker Detection and Precision Therapeutics in Cancer

Biomarker discovery is at the heart of personalized treatment planning and cancer precision therapeutics, encompassing disease classification and prognosis, prediction of treatment response, and therapeutic targeting. However, many biomarkers represent passenger rather than driver alterations, limiting their utilization as functional units for therapeutic targeting. We suggest that identification of driver biomarkers through mechanism-centric approaches, which take into account upstream and downstream regulatory mechanisms, is fundamental to the discovery of functionally meaningful markers. Here, we examine computational approaches that identify mechanism-centric biomarkers elucidated from gene co-expression networks, regulatory networks (e.g., transcriptional regulation), protein-protein interaction (PPI) networks, and molecular pathways. We discuss their objectives, advantages over gene-centric approaches, and known limitations. Future directions highlight the importance of input and model interpretability, method and data integration, and the role of recently introduced technological advantages, such as single-cell sequencing, which are central for effective biomarker discovery and time-cautious precision therapeutics.

Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

Accumulating evidence has unveiled the pivotal roles of N6-methyladenosine (m6A) in pancreatic adenocarcinoma (PAAD). However, there are not many researches to predict the prognosis of PAAD using m6A-related long non-coding RNAs (lncRNAs). Raw data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and the Genotype-Tissue Expression project (GTEx) were utilized to comprehensively analyze the expression and prognostic performances of 145 m6A-related lncRNAs in PAAD and to develop and validate a novel m6A-related multi-lncRNA prognostic signature (m6A-LPS) for PAAD patients. In total, 57 differentially expressed m6A-related lncRNAs with prognostic values were identified. Based on LASSO-Cox regression analysis, m6A-LPS was constructed and verified by using five-lncRNA expression profiles for TCGA and ICGC cohorts. PAAD patients were then divided into high- and low-risKBIE_A_1933868k subgroups with different clinical outcomes according to the median risk score; this was further verified by time-dependent receiver operating characteristic curves. Risk scores were significantly associated with clinical parameters such as histological grade and cancer status among PAAD patients. A nomogram consisting of risk score, grade, and cancer status was generated to predict the survival probability of PAAD patients, as also demonstrated by calibration curves. Discrepancies in cellular processes, signaling pathways, and immune status between the high- and low-risk subgroups were investigated by functional and single-sample gene set enrichment analyses. In conclusion, the novel m6A-LPS for PAAD patients was developed and validated, which might provide new insight into clinical decision-making and precision medicine.

RAB5C, SYNJ1, and RNF19B promote male ankylosing spondylitis by regulating immune cell infiltration

Background

This study aimed to identify the key genes related to male ankylosing spondylitis (AS) and to analyze the role of immune cell infiltration in the pathological process of this disease.

Methods

The AS dataset was downloaded from the Gene Expression Omnibus (GEO) public database, and the data of male healthy controls (M_HC) and male AS patients (M_AS) were extracted. R software was used to identify differentially expressed genes (DEGs). Functional and pathway enrichment analysis of the DEGs was performed. A protein-protein interaction (PPI) network was constructed, and the hub genes were screened out. All expression profile data were analyzed by weighted correlation network analysis (WGCNA) to screen out the hub genes, which were then intersected with the hub genes from the PPI network to obtain the key genes. Finally, the difference in immune cell infiltration in the two sets of samples was evaluated with CIBERSORT, and the correlation between the key genes and infiltrating immune cells was analyzed.

Results

A total of 689 DEGs were obtained, of which 395 genes were up-regulated and 294 genes were down-regulated. Functional and pathway enrichment analysis showed that DEGs were mainly enriched in pathways related to immune response. Based on the PPI analysis, five clusters with high scores were selected. Through WGCNA, 14 gene modules were obtained. The green module with the highest correlation was selected and intersected with the cluster previously obtained to obtain three key genes, RAB5C, SYNJ1, and RNF19B. Immune infiltration analysis found that monocytes and gamma delta T cells may be involved in the process of AS. Also, RAB5C, SYNJ1, and RNF19B are all related to increased levels of monocytes and macrophages.

Conclusions

RAB5C, SYNJ1, and RNF19B are key DEGs expressed in M_AS and may play a role in the disease’s occurrence and development through regulating immune cell functions.

Overexpression of TICRR and PPIF confer poor prognosis in endometrial cancer identified by gene co-expression network analysis

The incidence of endometrial cancer (EC) is intensively increasing. However, due to the complexity and heterogeneity of EC, the molecular targeted therapy is still limited. The reliable and accurate biomarkers for tumor progression are urgently demanded. After normalizing the data from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA), we utilized limma and WGCNA packages to identify differentially expressed genes (DEGs). The copy number variations of candidate genes were investigated by cBioPortal. Enrichment pathways analysis was performed by ClueGO and CluePedia. The methylation status was explored by UALCAN. ROC curve and survival analysis were conducted by SPSS and Kaplan–Meier. Infiltration immune cells in microenvironment were analyzed by TISIDB. Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA) were applied to explore potential biological pathways. Immunohistochemistry staining (IHC), cell proliferation, cell apoptosis, colony formation, migration, invasion and scratch-wound assays were performed to investigate the function of key genes in vitro. In this study, four expression profile datasets were integrated to identify candidate genes. Combined with WGCNA analysis, the top ten candidates were screened out, whose abnormal methylation patterns were extremely correlated with their expression level and they were associated with tumor grades and predicted poor survival. GSEA and GSVA demonstrated they were involved in DNA replication and cell cycle transition in EC. Gene silencing of TICRR and PPIF dramatically inhibited cell growth, migration and epithelial-mesenchymal transition (EMT) and enhanced progesterone sensitivity. Additionally, from DrugBank database, cyclosporine may be effective for PPIF targeted therapy. By integrative bioinformatics analysis and in vitro experiments, our study shed novel light on the molecular mechanisms of EC. TICRR and PPIF may promise to be potential therapeutic targets for endometrial cancer.

Identification of biomarkers correlated with diagnosis and prognosis of endometrial cancer using bioinformatics analysis

Endometrial cancer (EC) is one of the most common malignancies in the female genital system, characterized by high mortality and recurrence rates. This study attempted to screen key genes and potential prognostic biomarkers for EC using bioinformatics analysis. Twenty-seven normal endometrial tissues and 135 EC samples were collected from four Gene Expression Omnibus (GEO) databases, then we identified the differentially expressed genes (DEGs) and conducted downstream analyses. Moreover, we screened hub genes by constructing a protein-protein interaction (PPI) network. Finally, we assessed the prognostic values and molecular mechanism of the potential prognostic genes using the Kaplan-Meier curve and Gene Set Enrichment Analysis (GSEA). As a result, 28 upregulated and 94 downregulated genes were determined after gene integration of these four GEO data sets. Gene Ontology analysis indicated that DEGs were mainly involved in transcriptional regulation and cell proliferation. The Kyoto Encyclopedia of Gene and Genome pathway analysis primarily related to transcriptional misregulation and apoptosis. Moreover, the PPI analysis revealed 10 hub genes (JUN, UBE2I, GATA2, WT1, PIAS1, FOXL2, RUNXI, EZR, TCF4, and NR2F2) with a high degree of connectivity, among them, the expression tendency of nine genes except UBE2I were consistent with messenger RNA level from The Cancer Genome Atlas data. Furthermore, only FOXL2, TCF4, and NR2F2 were significantly correlated with prognosis of EC patients, and their low expression associated biological pathways were enriched in the cell cycle and fatty acid metabolism. In conclusion, this study identified three key genes as biomarkers and potential therapeutic targets of EC on the basis of integrated bioinformatics analysis. The findings will improve our comprehension of the molecular mechanisms underlying the pathogenesis and prognosis of EC.

Identification and prognostic value of DLGAP5 in endometrial cancer

Background

Endometrial cancer poses a serious threat to women’s health worldwide, and its pathogenesis, although actively explored, is not fully understood. DLGAP5 is a recently identified cell cycle-regulation gene not reported in endometrial cancer. This study was aiming to analyze the role of DLGAP5 in tumorigenesis and development and to investigate its prognostic significance of patients with endometrial cancer.

Methodology

Microarray datasets (GSE17025, GSE39099 and GSE63678) from the GEO database were used for comparative analysis, and their intersection was obtained by applying the Venn diagram, and DLGAP5 was selected as the target gene. Next, transcriptome data (n = 578) was downloaded from TCGA-UCEC to analyze the mRNA expression profile of DLGAP5. Then, immunohistochemical data provided by HPA were used to identify the different protein expression levels of DLGAP5 in tumor tissues and normal tissues. Subsequently, the prognostic meaning of DLGAP5 in patients with endometrial cancer was explored based on survival data from TCGA-UCEC (n = 541). Finally, the reliability of DLGAP5 expression was verified by RT-qPCR.

Results

Transcriptome data from TCGA-UCEC, immunohistochemical data from HPA, and RT-qPCR results from clinical samples were used for triple validation to confirm that the expression of DLGAP5 in endometrial cancer tissues was significantly higher than that in normal endometrial tissues. Kaplan–Meier survival analysis announced that the expression level of DLGAP5 was negatively correlated with the overall survival of patients with endometrial cancer.

Conclusions

DLGAP5 is a potential oncogene with cell cycle regulation, and its overexpression can predict the poor prognosis of patients with endometrial cancer. As a candidate target for the diagnosis and treatment of endometrial cancer, it is worthwhile to make further study to reveal the carcinogenicity of DLGAP5 and the mechanism of its resistance of organisms.

COL3A1, COL6A3, and SERPINH1 Are Related to Glucocorticoid-Induced Osteoporosis Occurrence According to Integrated Bioinformatics Analysis

Background

Glucocorticoid-induced osteoporosis (GIOP) represents the most frequently seen type of secondary osteoporosis, a systemic skeleton disorder. Numerous factors are associated with GIOP occurrence, but there are no specific diagnostic and therapeutic biomarkers for GIOP so far.

Material/Methods

In this work, gene modules related to GIOP were screened through weighted gene coexpression network analysis. Moreover, protein-protein interaction (PPI) networks and gene set enrichment analysis (GSEA) were carried out for hub genes. In addition, microarray GSE30159 dataset was used as a training set to analyze gene expression within bone biopsy samples from patients with endogenous Cushing’s syndrome with GIOP and from normal controls. GSE129228 was used as the test set for investigating the hub gene involvement within GIOP.

Results

According to our results, the turquoise module showed clinical significance, and 10 genes (COL3A1, POSTN, COL6A3, COL14A1, SERPINH1, ASPN, OGN, THY1, NID2, and TNMD) were discovered to be the “real” hub genes within coexpression as well as PPI networks. GSEA showed that the interaction of extracellular matrix receptors together with the focal adhesion pathway had significant enrichment within samples with high COL3A1 and COL6A3 expression. After the results from both test and training sets were overlapped, SERPINH1 was also significantly altered between GIOP and normal control samples.

Conclusions

COL3A1, COL6A3, and SERPINH1 were identified to be the candidate biomarkers for GIOP.

Identification of a five-gene signature for predicting the progression and prognosis of stage I endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is often diagnosed at an early clinical stage based on abnormal vaginal bleeding. However, the prognosis of UCEC is poor. The present study was conducted to identify novel tumor grade-related genes with the potential to predict the prognosis and progression of UCEC. A total of three gene expression microarray datasets were downloaded from the Gene Expression Omnibus database, and one RNA-sequencing dataset with corresponding clinical information of patients with UCEC was obtained from The Cancer Genome Atlas database. In summary, 1,447 differentially expressed genes (DEGs) were identified between endometrial cancerous tissues and normal endometrial tissues. Weighted gene co-expression network analysis was performed to assess the associations between DEGs and clinical traits. In total, five genes were found to be highly associated with the tumorigenesis and prognosis of UCEC. Among them, BUB1 mitotic checkpoint serine/threonine kinase B, cyclin B1, cell-division cycle protein 20 and non-SMC condensing I complex subunit G were involved in cell cycle regulation pathways, and DLG-associated protein 5 was involved in the Notch receptor 3 signaling pathway based on functional enrichment analyses. Of the five genes, four were highly expressed in endometrial cancerous tissues compared with normal endometrial tissues at the protein level. In addition, the higher expression of these genes predicted a higher tumor grade and worse overall survival. In conclusion, the present study revealed a 5-gene signature that can be used to predict the progression of UCEC.

A novel risk score system of immune genes associated with prognosis in endometrial cancer

Background

Endometrial cancer was the commonest gynecological malignancy in developed countries. Despite striking advances in multimodality management, however, for patients in advanced stage, targeted therapy still remained a challenge. Our study aimed to investigate new biomarkers for endometrial cancer and establish a novel risk score system of immune genes in endometrial cancer.

Methods

The clinicopathological characteristics and gene expression data were downloaded from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) of immune genes between tumors and normal tissues were identified. Protein–protein interaction (PPI) network of immune genes and transcriptional factors was integrated and visualized in Cytoscape. Univariate and multivariate analysis were employed for key genes to establish a new risk score system. Receiver operating characteristic (ROC) curve and survival analysis were performed to investigate the prognostic value of the model. Association between clinical characteristics and the model was analyzed by logistic regression. For validation, we identified 34 patients with endometrial cancer from Fudan University Shanghai Cancer Center (FUSCC). We detected 14-genes mRNA expression and calculated the risk scores of each patients and we performed survival analysis between the high-risk group and the low-risk group.

Results

23 normal tissues and 552 tumor tissues were obtained from TCGA database. 410 immune-related DEGs was identified by difference analysis and correlation analysis. KEGG and GO analysis revealed these DEGs were enriched in cell adhesion, chemotaxis, MAPK pathways and PI3K-Akt signaling pathway, which might regulate tumor progression and migration. All genes were screened for risk model construction and 14 hub immune-related genes (HTR3E, CBLC, TNF, PSMC4, TRAV30, PDIA3, FGF8, PDGFRA, ESRRA, SBDS, CRHR1, LTA, NR2F1, TNFRSF18) were prognostic in endometrial cancer. The area under the curve (AUC) was 0.787 and the high-risk group estimated by the model possessed worse outcome (P < 0.001). Multivariate analysis suggested that the model was indeed an independent prognostic factor (high-risk vs. low-risk, HR = 1.14, P < 0.001). Meanwhile, the high-risk group was prone to have higher grade (P = 0.002) and advanced clinical stage (P = 0.018). In FUSCC validation set, the high-risk group had worse survival than the low-risk group (P < 0.001).

Conclusions

In conclusion, the novel risk model of immune genes had some merits in predicting the prognosis of endometrial cancer and had strong correlation with clinical outcomes. Furthermore, it might provide new biomarkers for targeted therapy in endometrial cancer.

Comprehensive Analysis of Prognostic Alternative Splicing Signatures in Endometrial Cancer

Background

Alternative splicing (AS) is one of the critical post-transcriptional regulatory mechanisms of various cancers and also plays a crucial role in the development of cancers, including endometrial cancer (EC).

Methods

The splicing data and gene expression profiles of EC were obtained from The Cancer Genome Atlas. The corresponding clinical data were extracted from TCGA-CDR. With univariate Cox regression analysis, least absolute shrinkage and selection operator model, and multivariate Cox regression analysis, the survival-related AS events were selected. Functional enrichment analysis was also performed to investigate the functions of these AS events. Splicing factors and AS regulation network were constructed to understand the correlation among these AS events.

Result

A total of 1826 AS events were identified as survival-related events. Functional enrichment analysis showed that these AS events were associated with several immune system-related processes. Then, the prognostic signatures were developed based on these survival-related events and acted as an independent prognostic factor for EC. Splicing factors and AS regulation network were also constructed to understand the regulatory mechanisms of AS events in EC.

Conclusion

This study systematically analyzed the role of AS events in EC and developed the prognostic model for EC.