Identifying GPSM Family Members as Potential Biomarkers in Breast Cancer: A Comprehensive Bioinformatics Analysis

RNA Sequencing Identifies Novel Signaling Pathways and Potential Drug Target Genes Induced by FOSL1 in Glioma Progression and Stemness

Background

Glioblastoma is a highly aggressive brain tumor, and the transition from the proneural to mesenchymal subtype is associated with more aggressive and therapy-resistant features. However, the signaling pathways and genes involved in this transition remain largely undefined.

Methods

We utilized patient-derived xenograft (PDX) samples of glioblastoma, specifically PDX-L14, which exhibit both negative and overexpressed FOSL1 expression. mRNA expression profiles were assessed by RNA sequencing in these samples, followed by gene ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, and Gene Set Enrichment Analysis (GSEA). Validation of the hub genes was performed using qPCR and immunohistochemistry assays.

Results

Differentially expressed genes (DEGs) between FOSL1 overexpression groups were predominantly involved in ferroptosis, immune response, angiogenesis, vascular mimicry, autophagy, epithelial-mesenchymal transition (EMT), cancer cell stemness, temozolomide (TMZ) resistance, and NF-κB signaling. Downregulated DEGs were associated with TMZ resistance, glioma proliferation, RNA processing, and Wnt/β-catenin signaling. Key enrichment pathways, including NF-κB, Want, and BMP, are all critical for maintaining glioma stemness. FOSL1 was found to regulate RNA processing and ubiquitination. Notably, 8 upregulated (ITGA5, SDC1, PHLDB2, TNFRSF8, ADAM8, TLR7, STEAP3, and POU3F2) and 4 downregulated (IFIT1, FBXO16, ARL3, and BEX1) genes were identified, with implications for glioblastoma prognosis.

Conclusion

This transcriptome investigation emphasizes the diverse functions of FOSL1 in different biological processes and signaling networks during the shift from proneural to mesenchymal state in glioblastoma.

Comprehensive analysis of CMTM family and immune infiltration in esophageal carcinoma

OBJECTIVE

Esophageal carcinoma (ESCA) is one of the most common malignant diseases and contributes to the annual burden of death worldwide. A better understanding of the underlying molecular changes is urgently required to identify early diagnostic biomarkers and effective therapeutics. The chemokine-like factor (CKLF)-like MARVEL transmembrane domain-containing family (CMTMs) is reported to be entangled in many human cancers. However, the role of CMTMs in ESCA remains unclear.

METHODS

The differential expressions of CMTMs between ESCA and normal tissues were analyzed using TCGA database. The relationships between CMTMs and immune infiltration in the tumor microenvironment (TME) were also evaluated to explore their underlying values in the diagnosis and prognosis of ESCA.

RESULTS

The results showed that ESCA showed significantly higher expressions of CMTM1,3,6,7 and lower expressions of CMTM4,5 than normal tissue (P < 0.05). Meanwhile, CMTM3,4,8 expressions were correlated with the tumor stage of ECSA patients. The analysis on immune infiltrations (CD8 + T, Tregs, NK and macrophages) showed that M2 macrophages was dominant in TME, with significantly higher levels than the other cells (F = 326.93, P < 0.001). The higher abundance of M2 macrophages and Tregs significantly shortened the survival time of patients with ESCA (P = 0.01). Interestingly, the expression levels of CMTM1,3,5,7 were comparable to the abundance of M2 macrophages (CMTM1: r = 0.172168; CMTM3: r = 0.313221; CMTM5: r = 0.130669; CMTM7: r = 0.119922; P < 0.05). CMTM2,4,5,7,8 positively correlated with Tregs (P < 0.05). Moreover, we found positive associations between the expression of CMTMs and the signatures of M2 macrophages (MS4A4A, VSIG4 and CD163).

CONCLUSION

There were differential expressions of CMTMs between ESCA and normal tissues. Furthermore, the expression of CMTMs was positively correlated with M2 macrophages, indicating a possibility that CMTMs may become a new immunotherapy target for ESCA.

GMIP: A Novel Prognostic Biomarker Influencing Immune Infiltration and Tumour Dynamics Across Cancer Types

GMIP, a member of the RhoGAP family, plays a critical role in cytoskeletal remodelling, cell migration and immune modulation. Its aberrant expression in cancers suggests a pivotal role in tumour progression. GMIP expression was assessed using transcriptomic datasets from GDC and UCSC XENA, and protein distribution across tissues via HPA and GeneMANIA. The TISCH database identified primary GMIP-expressing cell types in the tumour microenvironment. Univariate Cox regression assessed GMIP's prognostic potential, while cBioPortal and GSCA explored genomic alterations. TIMER 2.0 was used to investigate immune cell infiltration and GMIP's role in immune regulation. GSEA and GSVA unveiled GMIP-related biological pathways, and molecular docking with CellMiner identified potential drug interactions. In vitro assays confirmed GMIP's functional relevance in breast cancer. GMIP exhibits differential expression across multiple cancer types, demonstrating significant prognostic implications. Its expression is inversely correlated with CNV and methylation in several cancers. GMIP is closely linked to immunotherapy biomarkers and immune suppression, influencing therapeutic responses. Functional studies suggest that GMIP inhibition reduces cancer cell proliferation and migration. GMIP is identified as a promising oncological biomarker, particularly in breast cancer, with potential therapeutic implications. GMIP's therapeutic potential is especially pronounced in BRCA-mutated tumours, underscoring its relevance for novel anticancer interventions.

Tumor Mutation Signature Reveals the Risk Factors of Lung Adenocarcinoma with EGFR or KRAS Mutation

INTRODUCTION

EGFR and KRAS mutations are frequently detected in lung adenocarcinoma (LUAD). Tumor mutational signature (TMS) determination is an approach to identify somatic mutational patterns associated with pathogenic factors. In this study, through the analysis of TMS, the underlying pathogenic factors of LUAD with EGFR and KRAS mutations were traced.

METHODS

This was a retrospective study. TMS of LUAD with KRAS and EGFR mutations from the TCGA, OncoSG, and MSK datasets was determined by two bioinformatics tools, namely the "MutationalPatterns" and "FitMS" packages. Elevated microsatellite alterations at selected tetranucleotide repeats (EMAST) of LUAD clinical specimens was analyzed using capillary electrophoresis.

RESULTS

In LUAD with KRAS mutations, TMS analysis indicated that the smoking-related SBS4 signature was enriched. For LUAD with EGFR L858R mutation, the smoking-related SBS4 signature was enriched in the Western population from the TCGA database; however, the smoking-related SBS4 signature was not obvious in Asian LUAD patients. LUAD with EGFR exon19 deletion (19Del) exhibited stronger SBS15 signature, which was related to defective DNA mismatch repair. Capillary electrophoresis analysis showed that an EMAST locus was frequently instable in LUAD with EGFR 19Del. Different from the Western population, Asian LUAD patients with EGFR mutations exhibited the enrichment of SBS1, SBS2, and SBS13 signatures, which were associated with the endogenous mutation process of cytidine deamination.

CONCLUSIONS

TMS analysis reveals that smoking is associated with LUAD with KRAS mutations. Defective DNA mismatch repair and endogenous cytidine deamination are associated with LUAD with EGFR mutations, especially for the EGFR 19Del. The endogenous mutational process is stronger in Asian LUAD patients than Western LUAD patients.

The Predictive Value of Monocarboxylate Transporter 4 (MCT4) on Lung Adenocarcinoma Patients Treated with PD-1 Inhibitors

Purpose

Monocarboxylate transporter 4 (MCT4) can influence the amount of lactate in the tumor microenvironment and further control cancer cell proliferation, migration, and angiogenesis. This study aimed to evaluate the predictive value of MCT4 for prognosis and immunotherapy efficacy in advanced lung adenocarcinoma (LUAD).

Patients and methods

First, bioinformatics analysis was used to assess the relevance of MCT4 for survival and immunotherapy outcomes in LUAD. Subsequently, we performed a retrospective study involving 126 patients with stage IIIb to IV LUAD treated with programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) inhibitors. MCT4 expression in LUAD tissues was detected by immunohistochemistry (IHC), then the patients were divided into high and low expression groups. The differences in the medical records of the two groups were compared using the X2 test. Kaplan-Meier (K-M) method was used for survival analysis. Univariate and multivariate analysis were used to pinpoint independent predictors, and a nomogram was developed based on the significant factors for overall survival (OS) in the multivariate analysis. The predictive ability of the nomogram was evaluated through C-index, receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA).

Results

Both bioinformatics analysis and clinical study revealed that low MCT4 expression was associated with better prognosis and immunotherapy efficacy. Multivariate analysis of clinical characteristics showed that age >65 years, stage IV, high MCT4 expression, neutrophil-to-lymphocyte ratio (NLR)>3, lactate dehydrogenase (LDH)>250 (U/L) and carcinoembryonic antigen (CEA)>5 (ng/mL) were significantly associated with poor prognosis on immunotherapy. These factors were subsequently incorporated into the nomogram model. The C-index value of the model stood at 0.735 (95% CI= 0.662 ~ 0.807), indicating robust predictive performance of the model. The DCA curve showed that the model had a notable clinical application value.

Conclusion

High expression of MCT4 is associated with poor prognosis and reduced efficacy of immunotherapy in patients with advanced LUAD.

Butyrate Metabolism-Related Gene Signature in Tumor Immune Microenvironment in Lung Adenocarcinoma: A Comprehensive Bioinformatics Study

BACKGROUND

Experimental results have verified the suppressive impact of butyrate on tumor formation. Nevertheless, there is a limited understanding of the hidden function of butyrate metabolism within the tumor immune microenvironment (TIME) of lung adenocarcinoma (LUAD). This research aimed at digging the association between genes related to butyrate metabolism (butyrate metabolism-related genes [BMRGs) and immune infiltrates in LUAD patients.

METHODS

Through analyzing The Cancer Genome Atlas dataset (TCGA), the identification of 38 differentially expressed BMRGs was made between LUAD and normal samples. Later, a prognostic signature made up of nine BMRGs was made to evaluate the risk score of LUAD subjects. Notably, high-risk scores emerged as negative prognostic indicators for overall survival in LUAD subjects. Additionally, BMRGs displayed associations with immunocyte infiltration levels, immune pathway activities, and pivotal prognostic hub BMRGs.

RESULTS

One key prognostic BMRG, PTGDS, exhibited a robust correlation with T cells, the chemokine-related pathway, and the TCR signaling pathway. This study suggests that investigating the interplay between butyrate metabolism and T cells could present a promising novel approach to cancer treatment. OncoPredict analysis further unveiled distinct sensitivities of nine medicine in high- and low-risk groups, facilitating the selection of optimal treatment strategies for individual LUAD patients.

CONCLUSIONS

The study establishes that the BMRG signature serves as a sensitive predictive biomarker, providing profound insights into the crucial effect of butyrate metabolism in the context of LUAD TIME.

Identification of key ubiquitination-related genes in gestational diabetes mellitus: A bioinformatics-driven study

Background and Aims

Gestational diabetes mellitus (GDM) is characterized by glucose intolerance that occurs during pregnancy. This study aimed to identify key ubiquitination-related genes associated with GDM pathogenesis.

Methods

Microarray data from GSE154377 was analyzed to identify differentially expressed genes (DEGs) in GDM vs normal pregnancy samples. Weighted gene co-expression network analysis was performed on ubiquitination-related genes. Functional enrichment, protein-protein interaction network, and TF-mRNA-miRNA interaction network analyses were conducted on differentially expressed ubiquitination-related genes (DE-URGs).

Results

We identified 2337 DEGs and 65 DE-URGs in GDM. Functional enrichment analysis of the 65 DE-URGs revealed involvement in protein ubiquitination and ubiquitin-dependent catabolic processes. Protein-protein interaction network analysis identified 8 hub genes, including MAP1LC3C, USP26, USP6, UBE2U, USP2, USP43, UCHL1, and USP44. ROC curve analysis showed these hub genes have high diagnostic accuracy for GDM (AUC > 0.6). The TF-mRNA-miRNA interaction network suggested USP2 and UCHL1 may be key ubiquitination genes in GDM.

Conclusion

In conclusion, this study contributes to our understanding of the molecular landscape of GDM by uncovering key ubiquitination-related genes. These findings may serve as a foundation for further investigations, offering potential biomarkers and therapeutic targets for clinical applications in GDM management.

Shared diagnostic genes and potential mechanisms between polycystic ovary syndrome and recurrent miscarriage revealed by integrated transcriptomics analysis and machine learning

Objective

More and more studies have found that polycystic ovary syndrome (PCOS) is significantly associated with recurrent spontaneous abortion (RSA), but the specific mechanism is not yet clear.

Methods

Based on the GEO database, we downloaded the PCOS (GSE10946, GSE6798 and GSE137684) and RSA (GSE165004, GSE26787 and GSE22490) datasets and performed differential analysis, weighted gene co-expression network (WGCNA), functional enrichment, and machine learning, respectively, on the datasets of the two diseases, Nomogram and integrated bioinformatics analysis such as immune infiltration analysis. Finally, the reliability of the diagnostic gene was verified by external verification and collection of human specimens.

Results

In this study, PCOS and RSA datasets were obtained from Gene Expression Omnibus (GEO) database, and a total of 23 shared genes were obtained by differential analysis and WGCNA analysis. GO results showed that the shared genes were mainly enriched in the functions of lipid catabolism and cell cycle transition (G1/S). DO enrichment revealed that shared genes are mainly involved in ovarian diseases, lipid metabolism disorders and psychological disorders. KEGG analysis showed significant enrichment of Regulation of lipolysis in adipocytes, Prolactin signaling pathway, FoxO signaling pathway, Hippo signaling pathway and other pathways. A diagnostic gene FAM166 B was obtained by machine learning and Nomogram screening, which mainly played an important role in Cellular component. GSEA analysis revealed that FAM166B may be involved in the development of PCOS and RSA by regulating the cell cycle, amino acid metabolism, lipid metabolism, and carbohydrate metabolism. CIBERSORT analysis showed that the high expression of FAM166 B was closely related to the imbalance of multiple immune cells. Further verification by qPCR suggested that FAM166 B could be used as a common marker of PCOS and RSA.

Conclusions

In summary, this study identified FAM166B as a common biomarker for PCOS and RSA, and conducted in-depth research and analysis of this gene, providing new data for basic experimental research and early prognosis, diagnosis and treatment of clinical diseases.

Key Cell-in-Cell Related Genes are Identified by Bioinformatics and Experiments in Glioblastoma

Purpose

This study aimed to explore the roles of cell-in-cell (CIC)-related genes in glioblastoma (GBM) using bioinformatics and experimental strategies.

Patients and Methods

The ssGSEA algorithm was used to calculate the CIC score for each patient. Subsequently, differentially expressed genes (DEGs) between the CIClow and CIChigh groups and between the tumor and control samples were screened using the limma R package. Key CIC-related genes (CICRGs) were further filtered using univariate Cox and LASSO analyses, followed by the construction of a CIC-related risk score model. The performance of the risk score model in predicting GBM prognosis was evaluated using ROC curves and an external validation cohort. Moreover, their location and differentiation trajectory in GBM were analyzed at the single-cell level using the Seurat R package. Finally, the expression of key CICRGs in clinical samples was examined by qPCR.

Results

In the current study, we found that CIC scorelow group had a significantly better survival in the TCGA-GBM cohort, supporting the important role of CICRGs in GBM. Using univariate Cox and LASSO analyses, PTX3, TIMP1, IGFBP2, SNCAIP, LOXL1, SLC47A2, and LGALS3 were identified as key CICRGs. Based on this data, a CIC-related prognostic risk score model was built using the TCGA-GBM cohort and validated in the CGGA-GBM cohort. Further mechanistic analyses showed that the CIC-related risk score is closely related to immune and inflammatory responses. Interestingly, at the single-cell level, key CICRGs were expressed in the neurons and myeloids of tumor tissues and exhibited unique temporal dynamics of expression changes. Finally, the expression of key CICRGs was validated by qPCR using clinical samples from GBM patients.

Conclusion

We identified novel CIC-related genes and built a reliable prognostic prediction model for GBM, which will provide further basic clues for studying the exact molecular mechanisms of GBM pathogenesis from a CIC perspective.

Identifying potential therapeutic targets in lung adenocarcinoma: a multi-omics approach integrating bulk and single-cell RNA sequencing with Mendelian randomization

Our research aimed to identify new therapeutic targets for Lung adenocarcinoma (LUAD), a major subtype of non-small cell lung cancer known for its low 5-year survival rate of 22%. By employing a comprehensive methodological approach, we analyzed bulk RNA sequencing data from 513 LUAD and 59 non-tumorous tissues, identifying 2,688 differentially expressed genes. Using Mendelian randomization (MR), we identified 74 genes with strong evidence for a causal effect on risk of LUAD. Survival analysis on these genes revealed significant differences in survival rates for 13 of them. Our pathway enrichment analysis highlighted their roles in immune response and cell communication, deepening our understanding. We also utilized single-cell RNA sequencing (scRNA-seq) to uncover cell type-specific gene expression patterns within LUAD, emphasizing the tumor microenvironment's heterogeneity. Pseudotime analysis further assisted in assessing the heterogeneity of tumor cell populations. Additionally, protein-protein interaction (PPI) network analysis was conducted to evaluate the potential druggability of these identified genes. The culmination of our efforts led to the identification of five genes (tier 1) with the most compelling evidence, including SECISBP2L, PRCD, SMAD9, C2orf91, and HSD17B13, and eight genes (tier 2) with convincing evidence for their potential as therapeutic targets.

Ferns G, Batra J, Lam AKY, Giovannetti E, Avan A. G-Protein Signaling Modulator 2 as a Potential Biomarker in Colorectal Cancer: Integrative Analysis Using Genetic Profiling and Pan-Cancer Studies

Colorectal cancer (CRC) imposes a significant healthcare burden globally, prompting the quest for innovative biomarkers to enhance diagnostic and therapeutic strategies. This study investigates the G-protein signaling modulator (GPSM) family across several cancers and presents a comprehensive pan-cancer analysis of the GPSM2 gene across several gastrointestinal (GI) cancers. Leveraging bioinformatics methodologies, we investigated GPSM2 expression patterns, protein interactions, functional enrichments, prognostic implications, genetic alterations, and immune infiltration associations. Furthermore, the expression of the GPSM2 gene was analyzed using real-time analysis. Our findings reveal a consistent upregulation of GPSM2 expression in all GI cancer datasets analyzed, suggesting its potential as a universal biomarker in GI cancers. Functional enrichment analysis underscores the involvement of GPSM2 in vital pathways, indicating its role in tumor progression. The prognostic assessment indicates that elevated GPSM2 expression correlates with adverse overall and disease-free survival outcomes across multiple GI cancer types. Genetic alteration analysis highlights the prevalence of mutations, particularly missense mutations, in GPSM2. Furthermore, significant correlations between GPSM2 expression and immune cell infiltration are observed, suggesting its involvement in tumor immune evasion mechanisms. Collectively, our study underscores the multifaceted role of GPSM2 in GI cancers, particularly in CRC, emphasizing its potential as a promising biomarker for prognosis and therapeutic targeting. Further functional investigations are warranted to elucidate its clinical utility and therapeutic implications in CRC management.

Molecular signature to predict quality of life and survival with glioblastoma using Multiview omics model

Glioblastoma multiforme (GBM) patients show a variety of signs and symptoms that affect their quality of life (QOL) and self-dependence. Since most existing studies have examined prognostic factors based only on clinical factors, there is a need to consider the value of integrating multi-omics data including gene expression and proteomics with clinical data in identifying significant biomarkers for GBM prognosis. Our research aimed to isolate significant features that differentiate between short-term (≤ 6 months) and long-term (≥ 2 years) GBM survival, and between high Karnofsky performance scores (KPS ≥ 80) and low (KPS ≤ 60), using the iterative random forest (iRF) algorithm. Using the Cancer Genomic Atlas (TCGA) database, we identified 35 molecular features composed of 19 genes and 16 proteins. Our findings propose molecular signatures for predicting GBM prognosis and will improve clinical decisions, GBM management, and drug development.

Identification of a Novel Eight-Gene Risk Model for Predicting Survival in Glioblastoma: A Comprehensive Bioinformatic Analysis

Glioblastoma (GBM) is one of the most progressive and prevalent cancers of the central nervous system. Identifying genetic markers is therefore crucial to predict prognosis and enhance treatment effectiveness in GBM. To this end, we obtained gene expression data of GBM from TCGA and GEO datasets and identified differentially expressed genes (DEGs), which were overlapped and used for survival analysis with univariate Cox regression. Next, the genes' biological significance and potential as immunotherapy candidates were examined using functional enrichment and immune infiltration analysis. Eight prognostic-related DEGs in GBM were identified, namely CRNDE, NRXN3, POPDC3, PTPRN, PTPRN2, SLC46A2, TIMP1, and TNFSF9. The derived risk model showed robustness in identifying patient subgroups with significantly poorer overall survival, as well as those with distinct GBM molecular subtypes and MGMT status. Furthermore, several correlations between the expression of the prognostic genes and immune infiltration cells were discovered. Overall, we propose a survival-derived risk score that can provide prognostic significance and guide therapeutic strategies for patients with GBM.

Impact of TREM1 Variants on the Risk and Prognosis of Glioma in the Chinese Han Population

Background

Glioma is the main pathological subtype of brain tumors with high mortality.

Objective

This study aimed to elucidate the correlation between TREM1 variants and glioma risk in the Chinese Han population.

Methods

Genotyping of six variants of TREM1 was completed by Agena MassARRAY platform in 1061 subjects (503 controls and 558 glioma patients). The relationship between TREM1 polymorphisms and glioma risk was calculated using the logistic regression model, with odds ratio (OR) and 95% confidence intervals (CIs). A multifactor dimensionality reduction (MDR) method was performed to assess SNP-SNP interactions to predict glioma risk.

Results

In this research, overall analysis illustrated an association between TREM1 rs9369269 and an increased risk of glioma. Rs9369269 was also related to the risk of glioma in patients aged ≤40 years and females. Subjects with rs9369269 AC genotype were likely to obtain glioma compared to people with CC genotype (patients with astroglioma vs healthy people). Compared to TT genotype carriers, carriers with AT genotype of rs1351835 were significantly associated with overall survival (OS).

Conclusion

Taken together, the study identified the association between TREM1 variants and glioma risk and TREM1 variants were significantly associated with the prognosis of glioma. In the future, larger samples are needed to verify the results.

Identification and validation of SOCS1/2/3/4 as potential prognostic biomarkers and correlate with immune infiltration in glioblastoma

Suppressor of cytokine signalling (SOCS) 1/2/3/4 are involved in the occurrence and progression of multiple malignancies; however, their prognostic and developmental value in patients with glioblastoma (GBM) remains unclear. The present study used TCGA, ONCOMINE, SangerBox3.0, UALCAN, TIMER2.0, GENEMANIA, TISDB, The Human Protein Atlas (HPA) and other databases to analyse the expression profile, clinical value and prognosis of SOCS1/2/3/4 in GBM, and to explore the potential development mechanism of action of SOCS1/2/3/4 in GBM. The majority of analyses showed that SOCS1/2/3/4 transcription and translation levels in GBM tissues were significantly higher than those in normal tissues. qRT-PCR, western blotting (WB) and immunohistochemical staining were used to verify that SOCS3 was expressed at higher mRNA and protein levels in GBM than in normal tissues or cells. High SOCS1/2/3/4 mRNA expression was associated with poor prognosis in patients with GBM, especially SOCS3. SOCS1/2/3/4 were highly contraindicated, which had few mutations, and were not associated with clinical prognosis. Furthermore, SOCS1/2/3/4 were associated with the infiltration of specific immune cell types. In addition, SOCS3 may affect the prognosis of patients with GBM through JAK/STAT signalling pathway. Analysis of the GBM-specific protein interaction (PPI) network showed that SOCS1/2/3/4 were involved in multiple potential carcinogenic mechanisms of GBM. In addition, colony formation, Transwell, wound healing and western blotting assays revealed that inhibition of SOCS3 decreased the proliferation, migration and invasion of GBM cells. In conclusion, the present study elucidated the expression profile and prognostic value of SOCS1/2/3/4 in GBM, which may provide potential prognostic biomarkers and therapeutic targets for GBM, especially SOCS3.

Pan-cancer analysis of aldolase B gene as a novel prognostic biomarker for human cancers

Aldolase B (ALDOB) gene is essential for the process of glycolysis and differentially expressed in cancers. The aims of this study were to explore the potential role of ALDOB in pan-cancer, in order to deepen the research on the pathological mechanism of cancer. Hence, we used several online tools (TIMER2, GEPIA2, UALCAN, cBioPortal, and MXPRESS) and R language to identify the correlation between the ALDOB expression and survival analysis, genetic alteration, DNA methylation, and immune cell infiltration based on The Cancer Genome Atlas project. The results showed that ALDOB was lowly expressed in pan-cancer. Survival analysis revealed that low expression of ALDOB was markedly related with poor clinical prognosis, while the genetic alteration within ALDOB changed along with the difference of overall survival (OS) and disease-free survival (DFS) prognosis in several cancers. A possible relationship between DNA methylation and ALDOB expression for several tumors was found. Besides, ALDOB expression was confirmed to be associated with tumor immune cell infiltration, especially in breast invasive carcinoma (BRCA), esophageal carcinoma (ESCA), and testicular germ cell tumors (TGCT) cases. Further, the enrichment analysis demonstrated that metabolic pathway was closely related to ALDOB expression. Our results provide a comprehensive pan-cancer analysis and suggest ALDOB could act as a promising tumor predictive biomarker for human cancer.

Systematic pan-cancer analysis identifies gasdermin B as an immunological and prognostic biomarker for kidney renal clear cell carcinoma

Gasdermin (GSDM)-mediated cell lytic death plays an essential role in immunity and tumorigenesis. Despite the association of gasdermin B (GSDMB) with the tumorigenesis of various cancers, whether GSDMB functions as a prognostic biomarker in renal cell carcinoma remains poorly understood. Here, we explored the potential immunological functions and the prognostic value of GSDMB across multiple tumors with The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases, including analyzing the relationship between GSDMB expression and prognosis, tumor–immune system interactions, immunomodulators, and immune cell infiltration of different tumors. Importantly, elevated expression of GSDMB is an essential factor for the poor prognosis of kidney renal clear cell carcinoma (KIRC) patients, suggesting that it might be helpful to predict a survival benefit from a clinical therapy regimen. Furthermore, GSDMB expression promoted the level of CD4+ T-cell infiltration of the tumors but is significantly negatively associated with immature dendritic cells (iDCs) in KIRC. Additionally, we identified TNFRSF25 and TNFSF14 as immunostimulators highly correlated with GSDMB expression. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses showed that GSDMB and its interacting proteins might affect tumor growth through the serine metabolism pathway. Our current results demonstrate a promising therapeutic strategy targeting GSDMB and provide new insights into GSDMB as an immunological and prognostic biomarker for KIRC.

RNA-Seq Analysis of Clinical Samples from TCGA Reveal Molecular Signatures for Ovarian Cancer

Identifying differentially expressed genes and co-expression modules lead to novel biomarkers. GO, pathway enrichment, network, and tumor stage analysis of 318 ovarian cancer samples from TCGA, categorised into primary and recurrent, pre-menopause and post-menopause, and early and late stage tumors was performed. Upregulated and downregulated genes in primary vs recurrent, early stage vs late-stage and pre-menopause vs post-menopause tumors were 84 and 62, 84 and 35, and 88 and 14, respectively. IRAK2 and CXCL8 had higher expression in recurrent tumors while REG1A had higher expression in post-menopause samples. In late stage tumors constant expression of IRAK2 and REG1A was observed, while that of CXCL8 and EGF decreased. These genes may be potential biomarkers for the diagnosis of the disease.

Multi-omics analyses of CD276 in pan-cancer reveals its clinical prognostic value in glioblastoma and other major cancer types

BACKGROUND

CD276 (also known as B7-H3) is one of the most important immune checkpoints of the CD28 and B7 superfamily, and its abnormal expression is closely associated with various types of cancer. It has been shown that CD276 is able to inhibit the function of T cells, and that this gene may potentially be a promising immunotherapy target for different types of cancer.

METHODS

Since few systematic studies have been published on the role of CD276 in cancer to date, the present study has employed single-cell sequencing and bioinformatics methods to analyze the expression patterns, clinical significance, prognostic value, epigenetic alterations, DNA methylation level, tumor immune cell infiltration and immune functions of CD276 in different types of cancer. In order to analyze the potential underlying mechanism of CD276 in glioblastoma (GBM) to assess its prognostic value, the LinkedOmics database was used to explore the biological function and co-expression pattern of CD276 in GBM, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. In addition, a simple validation of the above analyses was performed using reverse transcription-quantitative (RT-q)PCR assay.

RESULTS

The results revealed that CD276 was highly expressed, and was often associated with poorer survival and prognosis, in the majority of different types of cancer. In addition, CD276 expression was found to be closely associated with T cell infiltration, immune checkpoint genes and immunoregulatory interactions between lymphoid and a non-lymphoid cell. It was also shown that the CD276 expression network exerts a wide influence on the immune activation of GBM. The expression of CD276 was found to be positively correlated with neutrophil-mediated immunity, although it was negatively correlated with the level of neurotransmitters, neurotransmitter transport and the regulation of neuropeptide signaling pathways in GBM. It is noteworthy that CD276 expression was found to be significantly higher in GBM compared with normal controls according to the RT-qPCR analysis, and the co-expression network, biological function and chemotherapeutic drug sensitivity of CD276 in GBM were further explored. In conclusion, the findings of the present study have revealed that CD276 is strongly expressed and associated with poor prognosis in most types of cancer, including GBM, and its expression is strongly associated with T-cell infiltration, immune checkpoint genes, and immunomodulatory interactions between lymphocytes and non-lymphoid cells.

CONCLUSIONS

Taken together, based on our systematic analysis, our findings have revealed important roles for CD276 in different types of cancers, especially GBM, and CD276 may potentially serve as a biomarker for cancer.

Upregulation of MAD2L1 mediated by ncRNA axis is associated with poor prognosis and tumor immune infiltration in hepatocellular carcinoma: A review

BACKGROUND

The mortality rate and prognosis of patients with hepatocellular carcinoma (HCC) are well known. A variety of highly malignant human cancers express mitotic arrest deficient 2 like 1 (MAD2L1), a transcription factor that plays a critical role in their development and progression. However, MAD2L1's particular mechanisms and effects on HCC remain uncertain.

METHODS

We performed a pan-cancer analysis for MAD2L1 prognosis and expression using The Cancer Genome Atlas and Genotype-Tissue Expression data in the present study. MAD2L1 may act as an oncogene in HCC, and a combination of in silico analyses, including expression, survival, and correlation analyses, were performed to identify non-coding ribonucleic acids (ncRNAs) that contribute to MAD2L1 overexpression.

RESULTS

In conclusion, MAD2L1 is most likely regulated by HCP5/miRNA-139-5p/MAD2L1 in HCC based on its upstream ncRNA-related pathway. A significant positive association was also found between MAD2L1 levels and tumor immune cell infiltration, immune cell biomarkers, and immune checkpoint expression.

CONCLUSION

Our findings demonstrate that ncRNA-mediated upregulation of MAD2L1 in HCC is closely related to poor prognosis and tumor infiltration.

A comprehensive analysis of G-protein-signaling modulator 2 as a prognostic and diagnostic marker for pan-cancer

Background: G-protein signaling modulator 2 (GPSM2) maintains cell polarization and regulates the cell cycle. Recent studies have shown that it is highly expressed in various tumors, but its pan-cancer analysis has not been reported.

Methods: First, we analyzed the differential GPSM2 expression in normal and cancer tissues by the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx) and Human Protein Atlas databases and investigated its expression effect on the survival of cancer patients by gene expression profiling interactive analysis 2 (GEPIA2). Second, we analyzed the GPSM2 phosphorylation level using the clinical proteomic tumor analysis consortium dataset. In addition, we investigated GPSM2 gene mutations in human tumor specimens and the impact of gene mutations on patient survival. Finally, we analyzed the relationship between GPSM2 expression and cellular immune infiltration through the TIMER 2.0 database. Meanwhile, the possible signaling pathway of the gene was analyzed by the Gene Ontology (GO)| Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to explore its potential mechanism.

Results:GPSM2 is overexpressed in most cancers, which leads to reduced overall survival (OS) and disease-free survival in patients. The results of phosphorylation analysis suggest that tumor development involves a complex GPSM2 phosphorylation process. We identified GPSM2 mutation loci with the highest frequency of mutations in uterine corpus endometrial carcinoma (UCEC), and this mutation increased progression-free survival and overall survival in uterine corpus endometrial carcinoma patients. Finally, we found that the role of GPSM2 in tumors may be associated with cellular immune infiltration. Gene Ontology|KEGG pathway analysis showed that the enrichment pathways were mainly “mitotic nuclear division,” “chromosome segregation,” and “spindle.”

Conclusions: Our pan-cancer analysis provides a comprehensive overview of the oncogenic roles and potential mechanisms of GPSM2 in multiple human cancers.

Analysis of the potential ferroptosis mechanism and multitemporal expression change of central ferroptosis-related genes in cardiac ischemia–reperfusion injury

Acute myocardial infraction is the most severe type of coronary artery disease and remains a substantial burden to the health care system globally. Although myocardial reperfusion is critical for ischemic cardiac tissue survival, the reperfusion itself could cause paradoxical injury. This paradoxical phenomenon is known as ischemia–reperfusion injury (IRI), and the exact molecular mechanism of IRI is still far from being elucidated and is a topic of controversy. Meanwhile, ferroptosis is a nonapoptotic form of cell death that has been reported to be associated with various cardiovascular diseases. Thus, we explored the potential ferroptosis mechanism and target in cardiac IRI via bioinformatics analysis and experiment. GSE4105 data were obtained from the GEO database and consist of a rat IRI model and control. After identifying differentially expressed ferroptosis-related genes (DEFRGs) and hub genes of cardiac IRI, we performed enrichment analysis, coexpression analysis, drug–gene interaction prediction, and mRNA–miRNA regulatory network construction. Moreover, we validated and explored the multitemporal expression of hub genes in a hypoxia/reoxygenation (H/R)-induced H9C2 cell injury model under different conditions via RT-qPCR. A total of 43 DEFRGs and 7 hub genes (tumor protein p53 [Tp53], tumor necrosis factor [Tnf], hypoxia-inducible factor 1 subunit alpha [Hif1a], interleukin 6 [Il6], heme oxygenase 1 [Hmox1], X-box binding protein 1 [Xbp1], and caspase 8 [Casp8]) were screened based on bioinformatics analysis. The functional annotation of these genes revealed apoptosis, and the related signaling pathways could have association with the pathogenesis of ferroptosis in cardiac IRI. In addition, the expression of the seven hub genes in IRI models were found higher than that of control under different H/R conditions and time points. In conclusion, the analysis of 43 DEFRGs and 7 hub genes could reveal the potential biological pathway and mechanism of ferroptosis in cardiac IRI. In addition, the multitemporal expression change of hub genes in H9C2 cells under different H/R conditions could provide clues for further ferroptosis mechanism exploring, and the seven hub genes could be potential biomarkers or therapeutic targets in cardiac IRI.

By characterizing metabolic and immune microenvironment reveal potential prognostic markers in the development of colorectal cancer

Colon adenocarcinoma (COAD) is one of the deadliest cancers in the world and survival rates vary significantly between early and advanced stage patients. Therefore, the identification of the pathogenesis in the development of COAD and prognostic markers is urgently demanded. Herein, we collected RNA-seq and somatic mutation data of COAD for statistical analysis. Clinical stage-specific differentially expressed genes (DEGs) and tumor development-dependent DEGs were identified. By characterizing the metabolic and immune features of COAD between stages, we found that the energy supply and inflammatory response of advanced tumors were suppressed. Next, the ETS1, AR, GATA1, GATA2, SREBF1, FOXP3, STAT4, and NFKB1 were identified to drive the metabolic and immune-related pathways in the development of COAD. The three potential prognostic markers (HOXC8, IRF7, and CXCL13) were identified based on Cox regression analysis. Additionally, immune infiltration analysis revealed that the resting CD4⁺ T cell was significantly related to the overall survival (OS) of COAD patients. Collectively, the specific metabolic and immune characteristics of advanced patients and the identified prognostic biomarkers will contribute to the development of precision medicine.

Systematic Analysis of Chemokines Reveals CCL18 is a Prognostic Biomarker in Glioblastoma

Background

Glioblastoma (GBM) is the most common and aggressive brain tumor in adults, in which chemokines are often upregulated and may play pivotal roles in their development and progression. Chemokines are a large subfamily of cytokines with leukocyte chemotactic activities involved in various tumor progression. However, gene expression patterns of the chemokines on a global scale were not known in GBM.

Methods

Differentially expressed chemokine genes in glioma and normal samples were screened by using The Cancer Genome Atlas (TCGA) database. Cox regression identified the prognosis-related genes in each glioma subtype. The protein expression levels of chemokines in 72 glioma tissues were detected by ELISA.

Results

We found that the transcripts of seven chemokines, including CCL2, CCL8, CCL18, CCL28, CXCL1, CXCL5, and CXCL13, were highly expressed in GBM that evidenced by involving immune cell infiltration regulation and accompanied with worse outcomes of GBM patients. The prognostic nomogram construction demonstrated that CCL18 held the highest risk score in patients with GBM. Furthermore, experiments on 72 glioma tissue samples confirmed that CCL18 protein expression was positively associated with tumor grade and IDH1 status but inversely with glioma patients’ overall survival (OS).

Conclusion

Our study reveals comprehensive and comparable roles of chemokine members in glioblastoma, and identified CCL18 as a critical driver of GBM malignant behaviors, therefore providing a potential target for developing prognosis and therapy in human glioblastoma.

Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer

Background

Lung cancer is the most common malignant tumor, and it has a high mortality rate. However, the study of miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer (NSCLC) is insufficient. Therefore, this study explored the differential expression of mRNA and miRNA in the plasma of NSCLC patients.

Methods

The Gene Expression Omnibus (GEO) database was used to download microarray datasets, and the differentially expressed miRNAs (DEMs) were analyzed. We predicted transcription factors and target genes of the DEMs by using FunRich software and the TargetScanHuman database, respectively. The Database for Annotation, Visualization, and Integrated Discovery (DAVID) was used for GO annotation and KEGG enrichment analysis of downstream target genes. We constructed protein-protein interaction (PPI) and DEM-hub gene networks using the STRING database and Cytoscape software. The GSE20189 dataset was used to screen out the key hub gene. Using The Cancer Genome Atlas (TCGA) and UALCAN databases to analyze the expression and prognosis of the key hub gene and DEMs. Then, GSE17681 and GSE137140 datasets were used to validate DEMs expression. Finally, the receiver operating characteristic (ROC) curve was used to verify the ability of the DEMs to distinguish lung cancer patients from healthy patients.

Results

Four upregulated candidate DEMs (hsa-miR199a-5p, hsa-miR-186-5p, hsa-miR-328-3p, and hsa-let-7d-3p) were screened from 3 databases, and 6 upstream transcription factors and 2253 downstream target genes were predicted. These genes were mainly enriched in cancer pathways and PI3k-Akt pathways. Among the top 30 hub genes, the expression of KLHL3 was consistent with the GSE20189 dataset. Except for let-7d-3p, the expression of other DEMs and KLHL3 in tissues were consistent with those in plasma. LUSC patients with high let-7d-3p expression had poor overall survival rates (OS). External validation demonstrated that the expression of hsa-miR-199a-5p and hsa-miR-186-5p in peripheral blood of NSCLC patients was higher than the healthy controls. The ROC curve confirmed that the DEMs could better distinguish lung cancer patients from healthy people.

Conclusion

The results showed that miR-199a-5p and miR-186-5p may be noninvasive diagnostic biomarkers for NSCLC patients. MiR-199a-5p-KLHL3 may be involved in the occurrence and development of NSCLC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09281-1.

Identification of MAD2L1 as a Potential Biomarker in Hepatocellular Carcinoma via Comprehensive Bioinformatics Analysis

Background

Hepatocellular carcinoma (HCC) is widely acknowledged as a malignant tumor with rapid progression, high recurrence rate, and poor prognosis. At present, there is a paucity of reliable biomarkers at the clinical level to guide the management of HCC and improve patient outcomes. Our research is aimed at assessing the prognostic value of MAD2L1 in HCC.

Methods

Four datasets, GSE121248, GSE101685, GSE85598, and GSE62232, were selected from the GEO database to analyze differentially expressed genes (DEGs) between HCC and normal liver tissues. After functional analysis, we constructed a protein-protein interaction network (PPI) for DEGs and identified core genes in this network with high connectivity with other genes. We assessed the relationship between core genes and the pathogenesis and prognosis of HCC. Finally, we explored the gene regulatory signaling mechanisms involved in HCC pathogenesis.

Results

145 DEGs were screened from the intersection of the four GEO datasets. MAD2L1 was associated with most genes according to the PPI network and was selected as a candidate gene for further study. Survival analysis suggested that high MAD2L1 expression in HCC correlated with a worse prognosis. In addition, real-time quantitative PCR (RT-qPCR), western blot (WB), and immunohistochemistry (IHC) findings suggested that the expression of MAD2L1 was abnormally increased in HCC tissues and cells compared to paraneoplastic tissues and normal hepatocytes.

Conclusion

We found that high MAD2L1 expression in HCC was significantly associated with overall patient survival and clinical features. We also explored the potential biological properties of this gene.