Gene Expression Omnibus Datasets Research Articles

Elevated HSPB1 Expression Is Associated with a Poor Prognosis in Glioblastoma Multiforme Patients.

Glioblastoma multiforme (GBM) is a highly aggressive form of brain cancer. This study investigated the clinical predictive value of heat shock protein β1 (HSPB1) in patients with GBM. A correlation was established between HSPB1 expression and GBM progression using data from The Cancer Genome Atlas (TCGA) dataset, Chinese Glioma Genome Atlas dataset, Gene Expression Omnibus dataset, and Human Protein Atlas database. A survival analysis was conducted and an HSPB1-based nomogram was constructed to evaluate the prognostic value of HSPB1 in patients with GBM. Based on TCGA data mining, we discovered that HSPB1 was significantly elevated in patients with GBM and may reflect their response to immunotherapy. In survival analysis, it appeared to have a predictive role in the prognosis of patients with GBM. Five signaling pathways were significantly enriched in the high HSPB1 expression phenotype according to the gene set enrichment analysis. In addition, a significant association was found between HSPB1 expression and immune checkpoints, tumor immune infiltration, tumor immune microenvironment, and immune cell markers in glioma. Overall, our results suggest that HSPB1 may regulate the function of immune cells, serve as a new immunotherapy target, and predict the response to immunotherapy in patients with GBM. HSPB1 appears to serve as a potential predictor of the clinical prognosis and response to immunotherapy in patients with GBM. It may be possible to identify patients who are likely to benefit from immunotherapy by assessing the expression level of HSPB1.

Cuproptosis-related gene-located DNA methylation in lower-grade glioma: Prognosis and tumor microenvironment.

Cuproptosis a novel copper-dependent cell death modality, plays a crucial part in the oncogenesis, progression and prognosis of tumors. However, the relationships among DNA-methylation located in cuproptosis-related genes (CRGs), overall survival (OS) and the tumor microenvironment remain undefined. In this study, we systematically assessed the prognostic value of CRG-located DNA-methylation for lower-grade glioma (LGG). Clinical and molecular data were sourced from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We employed Cox hazard regression to examine the associations between CRG-located DNA-methylation and OS, leading to the development of a prognostic signature. Kaplan-Meier survival and time-dependent receiver operating characteristic (ROC) analyses were utilized to gauge the accuracy of the signature. Gene Set Enrichment Analysis (GSEA) was applied to uncover potential biological functions of differentially expressed genes between high- and low-risk groups. A three CRG-located DNA-methylation prognostic signature was established based on TCGA database and validated in GEO dataset. The 1-year, 3-year, and 5-year area under the curve (AUC) of ROC curves in the TCGA dataset were 0.884, 0.888, and 0.859 while those in the GEO dataset were 0.943, 0.761 and 0.725, respectively. Cox-regression-analyses revealed the risk signature as an independent risk factor for LGG patients. Immunogenomic profiling suggested that the signature was associated with immune infiltration level and immune checkpoints. Functional enrichment analysis indicated differential enrichment in cell differentiation in the hindbrain, ECM receptor interactions, glycolysis and reactive oxygen species pathway across different groups. We developed and verified a novel CRG-located DNA-methylation signature to predict the prognosis in LGG patients. Our findings emphasize the potential clinical implications of CRG-located DNA-methylation indicating that it may serve as a promising therapeutic target for LGG patients.

Characteristics of the immune environment in prostate cancer as an adjunct to immunotherapy.

The tumor microenvironment (TME) exerts an important role in carcinogenesis and progression. Several investigations have suggested that immune cell infiltration (ICI) is of high prognostic importance for tumor progression and patient survival in many tumors, particularly prostate cancer. The pattern of immune infiltration of PCa, on the other hand, has not been thoroughly understood. The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets on PCa were obtained, and several datasets were merged into one data set using the "ComBat" algorithm. The ICI profiles of PCa patients were then to be uncovered by two computer techniques. The unsupervised clustering method was utilized to identify three ICI patterns in tumor samples, and Principal Component Analysis (PCA) was conducted to estimate the ICI score. Three different clusters of three ICIs were identified in 1341 PCa samples, which also correlated with different clinical features/characteristics and biological pathways. Patients with PCa are classified into high and low subtypes based on the ICI scores extracted from immune-associated signature genes. High ICI score subtypes are associated with a worse prognosis, which may intrigue the activation of cancer-related and immune-related pathways such as pathways involving Toll-like receptors, T-cell receptors, JAK-STAT, and natural killer cells. The ICI score was linked to tumor mutation load and immune/cancer-relevant signaling pathways, which explain prostate cancer's poor prognosis. The findings of this study not only advanced our knowledge of the mechanism of immune response in the prostate tumor microenvironment but also provided a novel biomarker, that is, the ICI score, for disease prognosis and guiding precision immunotherapy.

Constructing an immune-related prognostic signature for predicting prognosis and immune response in hepatocellular carcinoma

BackgroundCurrently, there are few studies on immune-related prognostic analysis of hepatocellular carcinoma (HCC). Our aim was to establish an immune-correlated prognostic model for HCC. MethodsImmune-associated cells were obtained from the scRNA-seq dataset (GSE149614) of HCC. Differentially expressed genes between normal and tumor cells from immune-associated cells and the immune-related genes from the ImmPort database were used to identify immune-related differentially expressed genes (IRDEGs). Subsequently, the risk model was established in the TCGA-LIHC cohort (n = 438) from the Cancer Genome Atlas (TCGA) database by using Kaplan-Meier (K-M) survival curve, univariate/multivariate Cox regression analysis. Subsequently, we further analyzed tumor immune microenvironment characteristics, somatic mutation, immune checkpoint and its ligand expression levels between high- and low-risk groups, as well as drug sensitivity prediction. ICGC cohort was set as the validation cohort. TCGA-LIHC cohort and three independent the Gene Expression Omnibus (GEO) datasets (GSE54236, GSE14520, and GSE64041) was used to verify IRDEGs expression, as well as PCR assays using clinical samples. ResultsThe IRDEGs was composed of 4 genes, namely B2M, SPP1, PPIA, and HRG. The 438 HCC patients were divided into high- and low-risk group. The high-risk group was associated with poor prognosis, including higher T stage, advanced pathological stages, less immune cell infiltration, higher TP53 mutation rate, the high expression of CTLA4 and HAVCR2. Besides, high-risk populations benefit from most chemotherapy drugs. Similarly, the performance of the risk model was validated in the ICGC. All four datasets (TCGA-LIHC cohort, GSE54236, GSE14520, and GSE64041) and clinical q-PCR results demonstrated that, compared with normal samples, the expressions of B2M and HRG were lower in tumor samples, and the expression of SPP1 was higher. ConclusionIn summary, the immune-related prognostic signature had a good predictive performance on prognosis and immunotherapy for HCC patients.

Exploration of the Shared Gene Signatures and Molecular Mechanisms between Chronic Bronchitis and Antineutrophil Cytoplasmic Antibody-associated Glomerulonephritis: Evidence from Transcriptome Data

Background: Chronic Bronchitis (CB) is a recurrent and persistent pulmonary inflammation disease. Growing evidence suggests an association between CB and Anti-neutrophil Cytoplasmic Antibody-associated Glomerulonephritis (ANCA-GN). However, the precise mechanisms underlying their association remain unclear. Aims: The purpose of this study was to further explore the molecular mechanism of the occurrence of chronic bronchitis (CB) associated with anti-neutrophil cytoplasmic antibody-associated glomerulonephritis (ANCA- GN). Objective: Our study aimed to investigate the potential shared pathogenesis of CB-associated ANCA-GN. Methods: Datasets of ANCA (GSE108113 and GSE104948) and CB (GSE151052 and GSE162635) were obtained from the Gene Expression Omnibus (GEO) datasets. Firstly, GSE108113 and GSE151052 were analyzed to identify common differentially expressed genes (DEGs) by Limma package. Based on common DEGs, protein-protein interaction (PPI) network and functional enrichment analyses, including GO, KEGG, and GSEA, were performed. Then, hub genes were identified by degree algorithm and validated in GSE104948 and GSE162635. Further PPI network and functional enrichment analyses were performed on hub genes. Additionally, a competitive ceRNA network was constructed through miRanda and spongeScan. Transcription factors (TFs) were predicted and verified using the TRRUST database. Furthermore, the CIBERSORT algorithm was employed to explore immune cell infiltration. The Drug Gene Interaction Database (DGIDB) was utilized to predict small-molecular compounds of CB and ANCA-GN. Result: A total of 963 DEGs were identified in the integrated CB dataset, and 610 DEGs were identified in the integrated ANCA-GN dataset. Totally, we identified 22 common DEGs, of which 10 hub genes (LYZ, IRF1, PIK3CG, IL2RG, NT5E, ARG2, HBEGF, NFATC2, ALPL, and FKBP5) were primarily involved in inflammation and immune responses. Focusing on hub genes, we constructed a ceRNA network composed of 323 miRNAs and 348 lncRNAs. Additionally, five TFs (SP1, RELA, NFKB1, HIF1A, and SP3) were identified to regulate the hub genes. Furthermore, immune cell infiltration results revealed immunoregulation in CB and ANCA-GN. Finally, some small-molecular compounds (Daclizumab, Aldesleukin, and NT5E) were predicted to predominantly regulate inflammation and immunity, especially IL-2. Conclusion: Our study explores the inflammatory-immune pathways underlying CB-associated ANCA-GN and emphasizes the importance of NETs and lymphocyte differentiation, providing novel insights into the shared pathogenesis and therapeutic targets.

Identification of cancer-associated fibroblast-related Ectodysplasin-A as a novel indicator for prognosis and immune response in gastric cancer

Studies have indicated cancer-associated fibroblasts (CAFs) could have a significant impact in gastric cancer (GC) progression and chemotherapy resistance. However, the gene related to cancer fibroblasts that can be used as biomarkers to judge the occurrence of gastric cancer has not been fully explored. Based on two Gene Expression Omnibus (GEO) datasets, we focus on differentially expressed genes which may act as CAFs markers related to GC. Through COX regression, LASSO regression and Kaplan-Meier survival analysis, we discovered three upregulated genes (GLT8D2, GNAS and EDA) associated with poor GC patients’ survival. By single-cell analysis and nomogram, we found that EDA may affect fibroblast production and disease prognosis in GC patients. EDA expression showed a positive correlation with 5-Fluorouracil IC50 values. Immunohistochemistry (IHC) and real time PCR indicated elevated EDA levels in GC tissues and cells. Enrichment analysis revealed that EDA was closely linked to immune system regulation. IHC and single-cell analysis indicated that EDA gene was associated with cancer fibroblasts marker FGF12 and influence cell interferon-gamma response, which may play a role in regulating immune-related characteristics. In summary, we concluded that EDA may be used as a new therapeutic CAFs marker for GC.

Decoding dysregulated genes, molecular pathways and microRNAs involved in cervical cancer

AbstractBackgroundThe present study aimed to identify dysregulated genes, molecular pathways, and regulatory mechanisms in human papillomavirus (HPV)‐associated cervical cancers. We have investigated the disease‐associated genes along with the Gene Ontology, survival prognosis, transcription factors and the microRNA (miRNA) that are involved in cervical carcinogenesis, enabling a deeper comprehension of cervical cancer linked to HPV.MethodsWe used 10 publicly accessible Gene Expression Omnibus (GEO) datasets to examine the patterns of gene expression in cervical cancer. Differentially expressed genes (DEGs), which showed a clear distinction between cervical cancer and healthy tissue samples, were analyzed using the GEO2R tool. Additional bioinformatic techniques were used to carry out pathway analysis and functional enrichment, as well as to analyze the connection between altered gene expression and HPV infection.ResultsIn total, 48 DEGs were identified to be differentially expressed in cervical cancer tissues in comparison to healthy tissues. Among DEGs, CCND1, CCNA2 and SPP1 were the key dysregulated genes involved in HPV‐associated cervical cancer. The five common miRNAs that were identified against these genes are miR‐7‐5p, miR‐16‐5p, miR‐124‐3p, miR‐10b‐5p and miR‐27a‐3p. The hub‐DEGs targeted by miRNA hsa‐miR‐27a‐3p are controlled by the common transcription factor SP1.ConclusionsThe present study has identified DEGs involved in HPV‐associated cervical cancer progression and the various molecular pathways and transcription factors regulating them. These findings have led to a better understanding of cervical cancer resulting in the development and identification of possible therapeutic and intervention targets, respectively.

Bioinformatics analysis of the association between obesity and gastric cancer.

Obesity and gastric cancer (GC) are prevalent diseases worldwide. In particular, the number of patients with obesity is increasing annually, while the incidence and mortality rates of GC are ranked high. Consequently, these conditions seriously affect the quality of life of individuals. While evidence suggests a strong association between these two conditions, the underlying mechanisms of this comorbidity remain unclear. We obtained the gene expression profiles of GSE94752 and GSE54129 from the Gene Expression Omnibus database. To investigate the associated biological processes, pathway enrichment analyses were conducted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes for the shared differentially expressed genes in obesity and GC. A protein-protein interaction (PPI) network was subsequently established based on the Search Tool for the Retrieval of Interacting Genes (STRING) database, followed by the screening of the core modules and central genes in this network using Cytoscape plug-in MCODE. Furthermore, we scrutinized the co-expression network and the interplay network of transcription factors (TFs), miRNAs, and mRNAs linked to these central genes. Finally, we conducted further analyses using different datasets to validate the significance of the hub genes. A total of 246 shared differentially expressed genes (209 upregulated and 37 downregulated) were selected for ensuing analyses. Functional analysis emphasized the pivotal role of inflammation and immune-associated pathways in these two diseases. Using the Cytoscape plug-in CytoHubba, nine hub genes were identified, namely, CXCR4, CXCL8, CXCL10, IL6, TNF, CCL4, CXCL2, CD4, and CCL2. IL6 and CCL4 were confirmed as the final hub genes through validation using different datasets. The TF-miRNA-mRNA regulatory network showed that the TFs primarily associated with the hub genes included RELA and NFKB1, while the predominantly associated miRNAs included has-miR-195-5p and has-miR-106a-5p. Using bioinformatics methods, we identified two hub genes from the Gene Expression Omnibus datasets for obesity and GC. In addition, we constructed a network of hub genes, TFs, and miRNAs, and identified the major related TFs and miRNAs. These factors may be involved in the common molecular mechanisms of obesity and GC.

The prognostic value of immune escape-related genes in lung adenocarcinoma.

Lung cancer is one of the most common cancers in humans, and lung adenocarcinoma (LUAD) has become the most common histological type of lung cancer. Immune escape promotes progression of LUAD from the early to metastatic late stages and is one of the main obstacles to improving clinical outcomes for immunotherapy targeting immune detection points. Our study aims to explore the immune escape related genes that are abnormally expressed in lung adenocarcinoma, providing assistance in predicting the prognosis of lung adenocarcinoma and targeted. RNA data and related clinical details of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database. Through weighted gene coexpression network analysis (WGCNA), 3112 key genes were screened and intersected with 182 immune escape genes obtained from a previous study to identify the immune escape-related genes (IERGs). The role of IERGs in LUAD was systematically explored through gene ontology (GO) and Kyoto Encyclopedia of Genes and Genome (KEGG) analyses, which were used to enrich the relevant pathways of IERGs. The least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analysis were used to identify the key prognostic genes, and a prognostic risk model was constructed. Estimation of Stromal and Immune Cells in Malignant Tumor Tissues Using Expression Data (ESTIMATE) and microenvironment cell populations (MCP) counter methods (which can accurately assess the amount of eight immune cell populations and two stromal cell groups) were used to analyze the tumor immune status of the high and low risk subgroups. The protein expression level of the differentially expressed genes in lung cancer samples was determined by using the Human Protein Atlas (HPA) database. A nomogram was constructed, and the prognostic risk model was verified via the Gene Expression Omnibus (GEO) datasets GSE72094 and GSE30219. Twenty differentially expressed IERGs were obtained. GO analysis of these 20 IERGs revealed that they were mainly associated with the regulation of immune system processes, immune responses, and interferon-γ enrichment in mediating signaling pathways and apoptotic signaling pathways; meanwhile, KEGG analysis revealed that IERGs were associated with necroptosis, antigen processing and presentation, programmed cell death ligand 1 (PD-L1) expression and programmed cell death 1 (PD-1) pathway in tumors, cytokine-cytokine receptor interactions, T helper cell 1 (Th1) and Th2 differentiation, and tumor necrosis factor signaling pathways. Using LASSO and Cox regression analysis, we constructed a four-gene model that could predict the prognosis of patients with LUAD, and the model was validated with a validation cohort. The immunohistochemical results of the HPA database showed that AHSA1 and CEP55 had low expression in normal lung tissue but high expression in lung cancer tissue. We constructed an IERG-based model for predicting the prognosis of LUAD. Among the genes identified, CEP55 and AHSA1 may be potential prognostic and therapeutic targets, and reducing their expression may represent a novel approach in the treatment of LUAD.

Exploration of the role of EMC3‑AS1 as a potential diagnostic and prognostic indicator in liver cancer.

The aim of the present study was to evaluate the diagnostic and prognostic significance of the long non-coding RNA (lncRNA) endoplasmic reticulum membrane protein complex subunit 3 antisense RNA 1 (EMC3-AS1) in liver cancer, and its impact on the proliferative and invasive capabilities of liver cancer cells. EMC3-AS1 expression in liver cancer was assessed using data from The Cancer Genome Atlas and three Gene Expression Omnibus datasets, and validated in clinical liver cancer samples using reverse transcription-quantitative PCR. The prognostic and diagnostic potentials of this lncRNA were evaluated using Kaplan-Meier and receiver operating characteristic analyses, respectively. The infiltration of immune cells and differential expression of immune checkpoints (ICs) between high- and low-EMC3-AS1 expression groups were investigated. Therapeutic correlation analyses were also undertaken to assess the impact of EMC3-AS1 in the treatment of liver cancer. In addition, in vitro experiments were conducted using small interfering RNA to knock down the expression of EMC3-AS1 in HepG2, Sk-Hep-1 and Huh-7 cells, and evaluate the effect on cell proliferation, colony formation and migration. The results revealed a significant upregulation of EMC3-AS1 expression in liver cancer tissues compared with that in adjacent normal tissues, which was associated with an unfavorable prognosis and demonstrated diagnostic effectiveness for patients with liver cancer. Furthermore, patients with high EMC3-AS1 expression exhibited increased levels of IC markers in comparison with those with low EMC3-AS1 expression. In addition, EMC3-AS1 was indicated to have clinical significance in the prediction of the response to immunotherapy and chemotherapy. Notably, the in vitro experiments demonstrated that the knockdown of EMC3-AS1 significantly hindered cell proliferation, colony formation and migration. Consequently, it was concluded that EMC3-AS1 is upregulated in liver cancer and serves as a prognostic indicator for unfavorable outcomes in patients with liver cancer. Additionally, targeting EMC3-AS1 through knockdown interventions showed potential in mitigating the ability of liver cancer cells to proliferate and migrate, which highlights its dual role as a biomarker and therapeutic target for liver cancer.

An Apoptosis-Related Specific Risk Model for Breast Cancer: From Genomic Analysis to Precision Medicine.

Breast cancer (BC) ranks as the most prevalent malignancy affecting women globally, with apoptosis playing a pivotal role in its pathological progression. Despite the crucial role of apoptosis in BC development, there is limited research exploring the relationship between BC prognosis and apoptosis-related genes (ARGs). Therefore, this study aimed to establish a BC-specific risk model centered on apoptosis-related factors, presenting a novel approach for predicting prognosis and immune responses in BC patients. Utilizing data from The Cancer Gene Atlas (TCGA), Cox regression analysis was employed to identify differentially prognostic ARGs and construct prognostic models. The accuracy and clinical relevance of the model, along with its efficacy in predicting immunotherapy outcomes, were evaluated using independent datasets, Receiver Operator Characteristic (ROC) curves, and nomogram. Additionally, Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) analyses were used to predict potential mechanical pathways. The CellMiner database is used to assess drug sensitivity of model genes. A survival risk model comprising eight prognostically relevant apoptotic genes (PMAIP1, TP53AIP1, TUBA3D, TUBA1C, BCL2A1, EMP1, GSN, F2) was established based on BC patient samples from TCGA. Calibration curves validated the ROC curve and nomogram, demonstrating excellent accuracy and clinical utility. In samples from the Gene Expression Omnibus (GEO) datasets and immunotherapy groups, the low-risk group (LRG) demonstrated enhanced immune cell infiltration and improved immunotherapy responses. Model genes also displayed positive associations with sensitivity to multiple drugs, including vemurafenib, dabrafenib, PD-98059, and palbociclib. This study successfully developed and validated a prognostic model based on ARGs, offering new insights into prognosis and immune response prediction in BC patients. These findings hold promise as valuable references for future research endeavors in this field.

Identification of diagnostic biomarkers and potential therapeutic targets for biliary atresia via WGCNA and machine learning methods.

Biliary atresia (BA) is a severe and progressive biliary obstructive disease in infants that requires early diagnosis and new therapeutic targets. This study employed bioinformatics methods to identify diagnostic biomarkers and potential therapeutic targets for BA. Our analysis of mRNA expression from Gene Expression Omnibus datasets revealed 3,273 differentially expressed genes between patients with BA and those without BA (nBA). Weighted gene coexpression network analysis determined that the turquoise gene coexpression module, consisting of 298 genes, is predominantly associated with BA. The machine learning method then filtered out the top 2 important genes, CXCL8 and TMSB10, from the turquoise module. The area under receiver operating characteristic curves for TMSB10 and CXCL8 were 0.961 and 0.927 in the training group and 0.819 and 0.791 in the testing group, which indicated a high diagnostic value. Besides, combining TMSB10 and CXCL8, a nomogram with better diagnostic performance was built for clinical translation. Several studies have highlighted the potential of CXCL8 as a therapeutic target for BA, while TMSB10 has been shown to regulate cell polarity, which was related to BA progression. Our analysis with qRT PCR and immunohistochemistry also confirmed the upregulation of TMSB10 at mRNA and protein levels in BA liver samples. These findings highlight the sensitivity of CXCL8 and TMSB10 as diagnostic biomarkers and their potential as therapeutic targets for BA.

Development and validation of a novel disulfidptosis-related lncRNAs signature in patients with HPV-negative oral squamous cell carcinoma

Disulfidptosis is a recently identified mode of regulated cell death. Regulating disulfidptosis in carcinoma is a promising therapeutic approach. Long non-coding RNAs (lncRNAs) have been reported to be related to the occurrence and development of many cancers. Disulfidptosis-related lncRNAs (DRLs) in HPV-negative oral squamous cell carcinoma (OSCC) have not been studied. Based on The Cancer Genome Atlas (TCGA) database, least absolute shrinkage selection operator (LASSO) analysis and Cox regression analysis were used to identify overall survival related DRLs and construct the signature. Kaplan–Meier, time-dependent receiver operating characteristics (ROC) and principal component analyses (PCA) were explored to demonstrate the prediction potential of the signature. Subgroup analysis stratified by different clinicopathological characteristics were conducted. Nomogram was established by DRLs signature and independent clinicopathological characteristics. The calibration plots were performed to reveal the accuracy of nomogram. Immune cell subset infiltration, immunotherapy response, drug sensitivity analysis, and tumor mutation burden (TMB) were conducted. Underlying functions and pathways were explored by Gene Set Enrichment Analysis (GSEA) analysis. Previous lncRNA signatures of OSCC were retrieved from PubMed for further validation. Gene expression omnibus (GEO) datasets (GSE41613 and GSE85446) were merged as an external validation for DRLs signature. Consensus clustering analysis of DRLs signature and experimental validation of DRLs were also explored. This research sheds light on the robust performance of DRLs signature in survival prediction, immune cell infiltration, immune escape, and immunotherapy of HPV-negative OSCC.

Computational identification of novel potential genetic pathogenesis and otherwise biomarkers in acute liver allograft rejection

Acute cellular rejection (ACR) is a prevalent postoperative complication following liver transplantation (LT), exhibiting an increasing incidence of morbidity and mortality. However, the molecular mechanisms of ACR following LT remain unclear. To explore the genetic pathogenesis and identify biomarkers of ACR following LT, three relevant Gene Expression Omnibus (GEO) datasets consisting of data on ACR or non-ACR patients after LT were comprehensively investigated by computational analysis. A total of 349 upregulated and 260 downregulated differentially expressed genes (DEGs) and eight hub genes (ISG15, HELZ2, HNRNPK, TIAL1, SKIV2L2, PABPC1, SIRT1, and PPARA) were identified. Notably, HNRNPK, TIAL1, and PABPC1 exhibited the highest predictive potential for ACR with AUCs of 0.706, 0.798, and 0.801, respectively. KEGG analysis of hub genes revealed that ACR following LT was predominately associated with ferroptosis, protein processing in the endoplasmic reticulum, complement and coagulation pathways, and RIG-I/NOD/Toll-like receptor signaling pathway. According to the immune cell infiltration analysis, γδT cells, NK cells, Tregs, and M1/M2-like macrophages had the highest levels of infiltration. Compared to SIRT1, ISG15 was positively correlated with γδT cells and M1-like macrophages but negatively correlated with NK cells, CD4+ memory T cells, and Tregs. In conclusion, this study identified eight hub genes and their potential pathways, as well as the immune cells involved in ACR following LT with the greatest levels of infiltration. These findings provide a new direction for future research on the underlying mechanism of ACR following LT.

Opening SCID newborn screening for novel exon genetic variants through whole-exome sequencing in China

BackgroundSevere combined immunodeficiency (SCID) is the most fatal form of inherited primary immunodeficiency disease. Known molecular defect mutations occur in most children with SCID. MethodsHerein, we report Adenosine Deaminase-SCID (ADA-SCID) using whole-exome sequencing (WES), explore exome mutational landscape and significance for 17 SCID samples, and verify the mutated exon genes using the Gene Expression Omnibus (GEO) datasets. A total of 250 patients, who were hospitalized at the Neonatal Intensive Care Unit (NICU) of The Seventh Medical Center of the PLA General Hospital for 3 years (from 2017 to 2020), were screened for SCID. We collected mutated genes from the WES data of 17 SCID children. GSE609 and GSE99176 cohorts were used to identify the expressions of mutated exon genes and molecular features in SCID. Gene set variation analyses (GSVA) and correlation analyses were performed. ResultsThe detection rate with approximately 6.8 % (17/250) of SCID is high in the NICU. A total of 16 genes were identified among 17 SCID samples, of which the Top 2 genes (MUC6 and RP11-683L23.1) might be crucial in the progression of SCID with 94 % mutation frequency. Furthermore, CNN2 and SCGB1C1 had significant co-mutations and may cooperate to affect SCID development. Importantly, the phylogenetic tree classification results of 17 SCID samples are more correlated to MUC6 with the most significant mutations. Expression profiles of seven mutated genes and five mutated genes were documented in GSE609 and GSE99176 cohorts based on microarray, respectively. Several immune-related pathways were significantly enriched, and Foxd4, differing from the other four mutated genes, was inversely correlated with the GSVA-enriched pathway. ConclusionDue to its high detection rate (6.8%) and fatality rate (100%), the inclusion of SCID in newborn screening (NBS) is urgent for children in China. The WES successfully identified several common exonic variants (e.g., MUC6) and depicted the feature of mutations and evolution, which will help develop new diagnostic methods for SCID.

Circ_0000518 regulates miR-326/EPHB3 axis to promote colorectal cancer progression

BackgroundCircular RNAs (circRNAs) have emerged as potential clinical targets in cancer therapy, since they have been shown to play a crucial role in tumor development by acting as competitive endogenous RNAs (ceRNAs) that regulate downstream mRNA expression by sequestering microRNAs (miRNAs). The present study, aimed to identify novel ceRNA networks involving circRNAs, miRNAs, and mRNAs in using a combination of Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) datasets and explore the function of circ_0000518 in the malignancy of colorectal cancer (CRC). MethodsRaw data related to CRC, including circRNAs, miRNAs and mRNAs, were downloaded from GEO and TCGA databases. A circRNA/miRNA/mRNA network was established by differential analysis and multiple databases were used to predict downstream targets. The role of this network in the development of CRC was further validated by a series of cellular experiments including dual luciferase assay, migration assay and flow apoptosis assay. ResultsDifferential expression analysis of two GEO datasets (GSE142837 and GSE126094) revealed four circRNAs, which were used to predict downstream miRNAs and mRNAs based on several databases. These predictions, combined with TCGA analysis, were used to construct a ceRNA network composed of 4 circRNAs, 9 miRNAs, and 52 mRNAs. The circ_0000518/miR-326/ephrin type-B receptor 3 (EPHB3) axis was eventually selected for functional validation in two CRC cell lines. Subsequently, it was experimentally verified that circ_0000518 was highly expressed in CRC; meanwhile, it was found that the proliferation and migration of CRC cells were significantly weakened by knocking down circ_0000518, while apoptosis of tumors was promoted. ConclusionThe data demonstrated the identification of a novel circRNA/miRNA/mRNA competitive endogenous network in CRC. The regulatory role of the circ_0000518/miR-326/EPHB3 axis in this network was preliminarily validated by cellular experiments exploring CRC tissue samples and two CRC cell lines.

DcR3-associated risk score: correlating better prognosis and enhanced predictive power in colorectal cancer

Decoy receptor 3 (DcR3), a novel soluble protein belonging to the tumor necrosis factor receptor (TNFR) family, has been previously associated with tumorigenesis in various cancers. However, in our study, we unexpectedly found that DcR3 may promote patient survival time in colorectal cancer (CRC). Through an analysis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets, we discovered that high levels of DcR3 are associated with improved overall survival (OS) and disease-free survival (DFS) in CRC patients. Further investigation revealed that DcR3 is correlated with favorable clinical features in Metastasis 0 (M0) and stage I/II CRC patients, suggesting it may act as a suppressive factor in CRC. Gene Set Enrichment Analysis (GSEA) demonstrated that the high DcR3 group is enriched in the IL-17 signaling pathway and other immune-related pathways, and Single Sample Gene Set Enrichment Analysis (ssGSEA) revealed a higher abundance of Tumor Infiltrating Lymphocytes (TIL) in the DcR3 high group. To better understand the function of DcR3, we constructed a DcR3-associated riskscore (DARS) model using machine learning, comprising three genes (DPP7, KDM3A, and TMEM86B). The DARS model indicated that high riskscore patients have an unfavorable prognosis, and it is associated with advanced stages (III/IV), T3/4 tumors, and N1/2 lymph node involvement. Additionally, high riskscore group exhibited more frequent gene mutations, such as TTN, MUC16, and SYNE1, with SYNE1 mutation being related to poor prognosis. Intriguingly, DcR3 showed higher expression in the low riskscore group. These results suggest that DcR3 could serve as a potential prognostic biomarker in CRC and may play a crucial role in favorably modulating the immune response in this malignancy.

Identification of anoikis-related gene signatures and construction of the prognosis model in prostate cancer.

One of the primary reasons for tumor invasion and metastasis is anoikis resistance. Biochemical recurrence (BCR) of prostate cancer (PCa) serves as a harbinger of its distant metastasis. However, the role of anoikis in PCa biochemical recurrence has not been fully elucidated. Differential expression analysis was used to identify anoikis-related genes based on the TCGA and GeneCards databases. Prognostic models were constructed utilizing LASSO regression, univariate and multivariate Cox regression analyses. Moreover, Gene Expression Omnibus datasets (GSE70770 and GSE46602) were applied as validation cohorts. Gene Ontology, KEGG and GSVA were utilized to explore biological pathways and molecular mechanisms. Further, immune profiles were assessed using CIBERSORT, ssGSEA, and TIDE, while anti-cancer drugs sensitivity was analyzed by GDSC database. In addition, gene expressions in the model were examined using online databases (Human Protein Atlas and Tumor Immune Single-Cell Hub). 113 differentially expressed anoikis-related genes were found. Four genes (EEF1A2, RET, FOSL1, PCA3) were selected for constructing a prognostic model. Using the findings from the Cox regression analysis, we grouped patients into groups of high and low risk. The high-risk group exhibited a poorer prognosis, with a maximum AUC of 0.897. Moreover, larger percentage of immune infiltration of memory B cells, CD8 Tcells, neutrophils, and M1 macrophages were observed in the high-risk group than those in the low-risk group, whereas the percentage of activated mast cells and dendritic cells in the high-risk group were lower. An increased TIDE score was founded in the high-risk group, suggesting reduced effectiveness of ICI therapy. Additionally, the IC50 results for chemotherapy drugs indicated that the low-risk group was more sensitive to most of the drugs. Finally, the genes EEF1A2, RET, and FOSL1 were expressed in PCa cases based on HPA website. The TISCH database suggested that these four ARGs might contribute to the tumor microenvironment of PCa. We created a risk model utilizing four ARGs that effectively predicts the risk of BCR in PCa patients. This study lays the groundwork for risk stratification and predicting survival outcomes in PCa patients with BCR.

Identification of a signature of histone modifiers in kidney renal clear cell carcinoma.

Kidney renal clear cell carcinoma (KIRC) is a cancer that is closely associated with epigenetic alterations, and histone modifiers (HMs) are closely related to epigenetic regulation. Therefore, this study aimed to comprehensively explore the function and prognostic value of HMs-based signature in KIRC. HMs were first obtained from top journal. Then, the mRNA expression profiles and clinical information in KIRC samples were downloaded from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) datasets. Cox regression analysis and least absolute shrinkage and selection operator (Lasso) analysis were implemented to find prognosis-related HMs and construct a risk model related to the prognosis in KIRC. Kaplan-Meier analysis was used to determine prognostic differences between high- and low-risk groups. Immune infiltration and drug sensitivity analysis were also performed between high- and low-risk groups. Eventually, 8 HMs were successfully identified for the construction of a risk model in KIRC. The results of the correlation analysis between risk signature and the prognosis showed HMs-based signature has good prognostic value in KIRC. Results of immune analysis of risk models showed there were significant differences in the level of immune cell infiltration and expression of immune checkpoints between high- and low-risk groups. The results of the drug sensitivity analysis showed that the high-risk group was more sensitive to several chemotherapeutic agents such as Sunitinib, Tipifarnib, Nilotinib and Bosutinib than the low-risk group. In conclusion, we successfully constructed HMs-based prognostic signature that can predict the prognosis of KIRC.

Zinc finger protein 367 exerts a cancer-promoting role in small cell lung cancer by influencing the CIT/LATS2/YAP signaling cascade

A remarkable cancer-related role of zinc finger protein 367 (ZNF367) has been demonstrated in multiple malignancies. However, whether ZNF367 has a role in small-cell lung cancer (SCLC) remains unexplored. The purpose of this work was to explore the potential role and mechanism of ZNF367 in SCLC. In silico analysis using the Gene Expression Omnibus (GEO) dataset revealed high levels of the ZNF367 transcript in SCLC. Examination of clinical tissues confirmed the significant abundance of ZNF367 in SCLC tissues compared with adjacent non-malignant tissues. The genetic depletion of ZNF367 in SCLC cells led to remarkable alterations in cell proliferation, the cell cycle, colony formation and chemosensitivity. Mechanistically, ZNF367 was shown to regulate the activation of yes-associated protein (YAP) associated with the up-regulation of phosphorylated large tumour suppressor kinase 2 (LATS2). Further investigation revealed that ZNF367 affected the LATS2-YAP cascade by regulating the expression of citron kinase (CIT). Re-expression of constitutively active YAP diminished the tumour-inhibiting function of ZNF367 depletion. Xenograft experiments confirmed the tumour-inhibiting effect of ZNF367 depletion in vivo. In summary, our results demonstrate that the inhibition of ZNF367 displays anticancer effects in SCLC by inhibiting YAP activation, suggesting it as a potential druggable oncogenic target.