A review of bioinformatics tools and web servers in different microarray platforms used in cancer research

Identification and validation of biomarkers in membranous nephropathy and pan-cancer analysis

Background

Membranous nephropathy (MN) is an autoimmune disease and represents the most prevalent type of renal pathology in adult patients afflicted with nephrotic syndrome. Despite substantial evidence suggesting a possible link between MN and cancer, the precise underlying mechanisms remain elusive.

Methods

In this study, we acquired and integrated two MN datasets (comprising a single-cell dataset and a bulk RNA-seq dataset) from the Gene Expression Omnibus database for differential expression gene (DEG) analysis, hub genes were obtained by LASSO and random forest algorithms, the diagnostic ability of hub genes was assessed using ROC curves, and the degree of immune cell infiltration was evaluated using the ssGSEA function. Concurrently, we gathered pan-cancer-related genes from the TCGA and GTEx databases, to analyze the expression, mutation status, drug sensitivity and prognosis of hub genes in pan-cancer.

Results

We conducted intersections between the set of 318 senescence-related genes and the 366 DEGs, resulting in the identification of 13 senescence-related DEGs. Afterwards, we meticulously analyzed these genes using the LASSO and random forest algorithms, which ultimately led to the discovery of six hub genes through intersection (PIK3R1, CCND1, TERF2IP, SLC25A4, CAPN2, and TXN). ROC curves suggest that these hub genes have good recognition of MN. After performing correlation analysis, examining immune infiltration, and conducting a comprehensive pan-cancer investigation, we validated these six hub genes through immunohistochemical analysis using human renal biopsy tissues. The pan-cancer analysis notably accentuates the robust association between these hub genes and the prognoses of individuals afflicted by diverse cancer types, further underscoring the importance of mutations within these hub genes across various cancers.

Conclusion

This evidence indicates that these genes could potentially play a pivotal role as a critical link connecting MN and cancer. As a result, they may hold promise as valuable targets for intervention in cases of both MN and cancer.

Detection of common pathogenesis of rheumatoid arthritis and atherosclerosis via microarray data analysis

Despite extensive research reveal rheumatoid arthritis (RA) is related to atherosclerosis (AS), common pathogenesis between these two diseases still needs to be explored. In current study, we explored the common pathogenesis between rheumatoid arthritis (RA) and atherosclerosis (AS) by identifying 297 Differentially Expressed Genes (DEGs) associated with both diseases. Through KEGG and GO functional analysis, we highlighted the correlation of these DEGs with crucial biological processes such as the vesicle transport, immune system process, signaling receptor binding, chemokine signaling and many others. Employing Protein-Protein Interaction (PPI) network analysis, we elucidated the associations between DEGs, revealing three gene modules enriched in immune system process, vesicle, signaling receptor binding, Pertussis, and among others. Additionally, through CytoHubba analysis, we pinpointed 11 hub genes integral to intergrin-mediated signaling pathway, plasma membrane, phosphotyrosine binding, chemokine signaling pathway and so on. Further investigation via the TRRUST database identified two key Transcription Factors (TFs), SPI1 and RELA, closely linked with these hub genes, shedding light on their regulatory roles. Finally, leveraging the collective insights from hub genes and TFs, we proposed 10 potential drug candidates targeting the molecular mechanisms underlying RA and AS pathogenesis. Further investigation on xCell revealed that 14 types of cells were all different in both AS and RA. This study underscores the shared pathogenic mechanisms, pivotal genes, and potential therapeutic interventions bridging RA and AS, offering valuable insights for future research and clinical management strategies.

Identified S100A9 as a target for diagnosis and treatment of ulcerative colitis by bioinformatics analysis

Ulcerative colitis (UC) is a chronic, recurrent inflammatory bowel disease. UC confronts with severe challenges including the unclear pathogenesis and lack of specific diagnostic markers, demanding for identifying predictive biomarkers for UC diagnosis and treatment. We perform immune infiltration and weighted gene co-expression network analysis on gene expression profiles of active UC, inactive UC, and normal controls to identify UC related immune cell and hub genes. Neutrophils, M1 macrophages, activated dendritic cells, and activated mast cells are significantly enriched in active UC. MMP-9, CHI3L1, CXCL9, CXCL10, CXCR2 and S100A9 are identified as hub genes in active UC. Specifically, S100A9 is significantly overexpressed in mice with colitis. The receiver operating characteristic curve demonstrates the excellent performance of S100A9 expression in diagnosing active UC. Inhibition of S100A9 expression reduces DSS-induced colonic inflammation. These identified biomarkers associated with activity in UC patients enlighten the new insights of UC diagnosis and treatment.

Identification of marker genes for spinal cord injury

Introduction

Spinal cord injury (SCI) is a profoundly disabling and devastating neurological condition, significantly impacting patients' quality of life. It imposes unbearable psychological and economic pressure on both patients and their families, as well as placing a heavy burden on society.

Methods

In this study, we integrated datasets GSE5296 and GSE47681 as training groups, analyzed gene variances between sham group and SCI group mice, and conducted Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis based on the differentially expressed genes. Subsequently, we performed Weighted Gene Correlation Network Analysis (WGCNA) and Lasso regression analyses.

Results

We identified four characteristic disease genes: Icam1, Ch25h, Plaur and Tm4sf1. We examined the relationship between SCI and immune cells, and validated the expression of the identified disease-related genes in SCI rats using PCR and immunohistochemistry experiments.

Discussion

In conclusion, we have identified and verified four genes related to SCI: Icam1, Ch25h, Plaur and Tm4sf1, which could offer insights for SCI treatment.

MARTin-an open-source platform for microarray analysis

Background: Microarray technology has brought significant advancements to high-throughput analysis, particularly in the comprehensive study of biomolecular interactions involving proteins, peptides, and antibodies, as well as in the fields of gene expression and genotyping. With the ever-increasing volume and intricacy of microarray data, an accurate, reliable and reproducible analysis is essential. Furthermore, there is a high level of variation in the format of microarrays. This not only holds true between different sample types but is also due to differences in the hardware used during the production of the arrays, as well as the personal preferences of the individual users. Therefore, there is a need for transparent, broadly applicable and user-friendly image quantification techniques to extract meaningful information from these complex datasets, while also addressing the challenges posed by specific microarray and imager formats, which can flaw analysis and interpretation. Results: Here we introduce MicroArray Rastering Tool (MARTin), as a versatile tool developed primarily for the analysis of protein and peptide microarrays. Our software provides state-of-the-art methodologies, offering researchers a comprehensive tool for microarray image quantification. MARTin is independent of the microarray platform used and supports various configurations including high-density formats and printed arrays with significant x and y offsets. This is made possible by granting the user the ability to freely customize parts of the application to their specific microarray format. Thanks to built-in features like adaptive filtering and autofit, measurements can be done very efficiently and are highly reproducible. Furthermore, our tool integrates metadata management and integrity check features, providing a straightforward quality control method, along with a ready-to-use interface for in-depth data analysis. This not only promotes good scientific practice in the field of microarray analysis but also enhances the ability to explore and examine the generated data. Conclusion: MARTin has been developed to empower its users with a reliable, efficient, and intuitive tool for peptidomic and proteomic array analysis, thereby facilitating data-driven discovery across disciplines. Our software is an open-source project freely available via the GNU Affero General Public License licence on GitHub.

Single-Cell Sequencing Combined with Transcriptome Sequencing Constructs a Predictive Model of Key Genes in Multiple Sclerosis and Explores Molecular Mechanisms Related to Cellular Communication

Background

Multiple sclerosis (MS) causes chronic inflammation and demyelination of the central nervous system and comprises a class of neurodegenerative diseases in which interactions between multiple immune cell types mediate the involvement of MS development. However, the early diagnosis and treatment of MS remain challenging.

Methods

Gene expression profiles of MS patients were obtained from the Gene Expression Omnibus (GEO) database. Single-cell and intercellular communication analyses were performed to identify candidate gene sets. Predictive models were constructed using LASSO regression. Relationships between genes and immune cells were analyzed by single sample gene set enrichment analysis (ssGSEA). The molecular mechanisms of key genes were explored using gene enrichment analysis. An miRNA network was constructed to search for target miRNAs related to key genes, and related transcription factors were searched by transcriptional regulation analysis. We utilized the GeneCard database to detect the correlations between disease-regulated genes and key genes. We verified the mRNA expression of 4 key genes by reverse transcription-quantitative PCR (RT‒qPCR).

Results

Monocyte marker genes were selected as candidate gene sets. CD3D, IL2RG, MS4A6A, and NCF2 were found to be the key genes by LASSO regression. We constructed a prediction model with AUC values of 0.7569 and 0.719. The key genes were closely related to immune factors and immune cells. We explored the signaling pathways and molecular mechanisms involving the key genes by gene enrichment analysis. We obtained and visualized the miRNAs associated with the key genes using the miRcode database. We also predicted the transcription factors involved. We used validated key genes in MS patients, several of which were confirmed by RT‒qPCR.

Conclusion

The prediction model constructed with the CD3D, IL2RG, MS4A6A, and NCF2 genes has good diagnostic efficacy and provides new ideas for the diagnosis and treatment of MS.

Identification of prognostic coagulation-related signatures in clear cell renal cell carcinoma through integrated multi-omics analysis and machine learning

Clear cell renal cell carcinoma is a threat to public health with high morbidity and mortality. Clinical evidence has shown that cancer-associated thrombosis poses significant challenges to treatments, including drug resistance and difficulties in surgical decision-making in ccRCC. However, the coagulation pathway, one of the core mechanisms of cancer-associated thrombosis, recently found closely related to the tumor microenvironment and immune-related pathway, is rarely researched in ccRCC. Therefore, we integrated bulk RNA-seq data, DNA mutation and methylation data, single-cell data, and proteomic data to perform a comprehensive analysis of coagulation-related genes in ccRCC. First, we demonstrated the importance of the coagulation-related gene set by consensus clustering. Based on machine learning, we identified 5 coagulation signature genes and verified their clinical value in TCGA, ICGC, and E-MTAB-1980 databases. It's also demonstrated that the specific expression patterns of coagulation signature genes driven by CNV and methylation were closely correlated with pathways including apoptosis, immune infiltration, angiogenesis, and the construction of extracellular matrix. Moreover, we identified two types of tumor cells in single-cell data by machine learning, and the coagulation signature genes were differentially expressed in two types of tumor cells. Besides, the signature genes were proven to influence immune cells especially the differentiation of T cells. And their protein level was also validated.

S100 Calcium-Binding Protein A8 Functions as a Tumor-Promoting Factor in Renal Cell Carcinoma via Activating NF-κB Signaling Pathway

BACKGROUND

Renal cell carcinoma (RCC), arising from the renal tubular epithelium, is one of the most common types of genitourinary malignancies. Based on the Gene Expression Omnibus (GEO) database (GSE100666), S100 calcium-binding protein A8 (S100A8) was highly expressed in RCC tissues. S100A8, an inflammatory regulatory factor, has emerged as an important mediator associated with the occurrence and development of cancer.

MATERIALS AND METHODS

The Gene Expression Omnibus (GEO) database was used to identify the key genes and investigate the main signaling pathways in RCC. Human RCC samples and corresponding adjacent normal tissues were collected in our hospital. The expression of S100A8 in human RCC samples was detected using western blotting and immunohistochemical analysis. S100A8 overexpression or knockdown was mediated by using Lipofectamine 3000 in human renal cell carcinoma cell line 786-O and ACHN cells. Basic experiments, including MTT and cell apoptosis assays, were utilized for investigating the function of S100A8 in RCC. Furthermore, the levels of inflammation were also evaluated in 786-O and ACHN cells.

RESULTS

In the current study, we found that downregulation of S100A8 inhibited proliferation and promoted apoptosis in 786-O and ACHN RCC cells. Of note, S100A8 silencing downregulated the phosphorylation of NF-κB p65, thereby decreasing the levels of TNF-α, cleaved caspase1, and MMP9. By contrast, S100A8 upregulation could increase these expressions.

CONCLUSION

Overall, S100A8 knockdown restrained RCC malignant biological properties, which was associated with the deactivation of the NF-κB signaling pathway. This present study demonstrates new insights that S100A8 may be a potential therapeutic target in RCC.

IHGA: An interactive web server for large-scale and comprehensive discovery of genes of interest in hepatocellular carcinoma

Mining gene expression data is valuable for discovering novel biomarkers and therapeutic targets in hepatocellular carcinoma (HCC). Although emerging data mining tools are available for pan-cancer-related gene data analysis, few tools are dedicated to HCC. Moreover, tools specifically designed for HCC have restrictions such as small data scale and limited functionality. Therefore, we developed IHGA, a new interactive web server for discovering genes of interest in HCC on a large-scale and comprehensive basis. Integrative HCC Gene Analysis (IHGA) contains over 100 independent HCC patient-derived datasets (with over 10,000 tissue samples) and more than 90 cell models. IHGA allows users to conduct a series of large-scale and comprehensive analyses and data visualizations based on gene mRNA levels, including expression comparison, correlation analysis, clinical characteristics analysis, survival analysis, immune system interaction analysis, and drug sensitivity analysis. This method notably enhanced the richness of clinical data in IHGA. Additionally, IHGA integrates artificial intelligence (AI)-assisted gene screening based on natural language models. IHGA is free, user-friendly, and can effectively reduce time spent during data collection, organization, and analysis. In conclusion, IHGA is competitive in terms of data scale, data diversity, and functionality. It effectively alleviates the obstacles caused by HCC heterogeneity to data mining work and helps advance research on the molecular mechanisms of HCC.

PCDH7 as the key gene related to the co-occurrence of sarcopenia and osteoporosis

Sarcopenia and osteoporosis, two degenerative diseases in older patients, have become severe health problems in aging societies. Muscles and bones, the most important components of the motor system, are derived from mesodermal and ectodermal mesenchymal stem cells. The adjacent anatomical relationship between them provides the basic conditions for mechanical and chemical signals, which may contribute to the co-occurrence of sarcopenia and osteoporosis. Identifying the potential common crosstalk genes between them may provide new insights for preventing and treating their development. In this study, DEG analysis, WGCNA, and machine learning algorithms were used to identify the key crosstalk genes of sarcopenia and osteoporosis; this was then validated using independent datasets and clinical samples. Finally, four crosstalk genes (ARHGEF10, PCDH7, CST6, and ROBO3) were identified, and mRNA expression and protein levels of PCDH7 in clinical samples from patients with sarcopenia, with osteoporosis, and with both sarcopenia and osteoporosis were found to be significantly higher than those from patients without sarcopenia or osteoporosis. PCDH7 seems to be a key gene related to the development of both sarcopenia and osteoporosis.

Genome-wide transcriptomics and copy number profiling identify patient-specific CNV-lncRNA-mRNA regulatory triplets in colorectal cancer

Screening cancer genomes has provided an in-depth characterization of genetic variants such as copy number variations (CNVs) and gene expression changes of non-coding transcripts. Single-dimensional experiments are often designed to differentiate a patient cohort into various sets with the aim of identifying molecular changes among groups; however, this may be inadequate to decipher the causal relationship between molecular signatures in individual patients. To overcome this challenge with respect to personalized medicine, we implemented a patient-specific multi-dimensional integrative approach to uncover coherent signals from multiple independent platforms. In particular, we focused on the consistent gene dosage effects of CNVs for both mRNA and long non-coding RNA (lncRNA) expression in nine colorectal cancer patients. We identified 511 CNV-lncRNA-mRNA regulatory triplets associated with CNVs and aberrant expression of both mRNAs and lncRNAs. By filtering out inconsistent changes among CNVs, mRNAs, and lncRNAs, we further characterized 165 coherent motifs associated with 56 genes. In total, 108 motifs were linked with 31 copy number gains, 44 upregulated lncRNAs, and 45 upregulated mRNAs. Another 57 coherent downregulated motifs were also collected. We discuss how for many of these CNV-lncRNA-mRNA regulatory triplets, their clinical impact remains to be explored, including survival time, microsatellite instability, tumor stage, and primary tumor sites. By validating two example CNV-lncRNA-mRNA triplets with up- and down-regulation, we confirmed that individual variations in multiple dimensions are a robust tool to identify reliable molecular signals for personalized medicine. In summary, we utilized a patient-specific computational pipeline to explore the consistent CNV-driven motifs consisting of lncRNAs and mRNAs. We also identified LSM14B as a potential promoter in colorectal cancer progression, suggesting that it may serve as a target for colorectal cancer treatment.

In silico analysis revealed the potential circRNA-miRNA-mRNA regulative network of non-small cell lung cancer (NSCLC)

BACKGROUND

The primary source of death in the world is non-small cell lung cancer (NSCLC). However, NSCLCs pathophysiology is still not completely understood. The current work sought to study the differential expression of mRNAs involved in NSCLC and their interactions with miRNAs and circRNAs.

METHODS

We utilized three microarray datasets (GSE21933, GSE27262, and GSE33532) from the GEO NCBI database to identify the differentially expressed genes (DEGs) in NSCLC. We employed DAVID Functional annotation tool to investigate the underlying GO biological process, molecular functions, and KEGG pathways involved in NSCLC. We performed the Protein-protein interaction (PPI) network, MCODE, and CytoHubba analysis from Cytoscape software to identify the significant DEGs in NSCLC. We utilized miRnet to anticipate and build interaction between miRNAs and mRNAs in NSCLC and ENCORI to predict the miRNA-circRNA relationships and build the ceRNA regulatory network. Finally, we executed the gene expression and Kaplan-Meier survival analysis to validate the significant DEGs in the ceRNA network utilizing TCGA NSCLC and GEPIA data.

RESULTS

We revealed a total of 156 overlapped DEGs (47 upregulated and 109 downregulated genes) in NSCLC. The PPI network, MCODE, and CytoHubba analysis revealed 12 hub genes (cdkn3, rrm2, ccnb1, aurka, nuf2, tyms, kif11, hmmr, ccnb2, nek2, anln, and birc5) that are associated with NSCLC. We identified that these 12 genes encode 12 mRNAs that are strongly linked with 8 miRNAs, and further, we revealed that 1 circRNA was associated with this 5 miRNA. We constructed the ceRNAs network that contained 1circRNA-5miRNAs-7mRNAs. The expression of these seven significant genes in LUAD & LUSC (NSCLC) was considerably higher in the TCGA database than in normal tissues. Kaplan-Meier survival plot reveals that increased expression of these hub genes was related to a poor survival rate in LUAD.

CONCLUSION

Overall, we developed a circRNA-miRNA-mRNA regulation network to study the probable mechanism of NSCLC.

Identification of the miRNA-mRNA regulatory network associated with radiosensitivity in esophageal cancer based on integrative analysis of the TCGA and GEO data

BACKGROUND

The current study set out to identify the miRNA-mRNA regulatory networks that influence the radiosensitivity in esophageal cancer based on the The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases.

METHODS

Firstly, esophageal cancer-related miRNA-seq and mRNA-seq data were retrieved from the TCGA database, and the mRNA dataset of esophageal cancer radiotherapy was downloaded from the GEO database to analyze the differential expressed miRNAs (DEmiRNAs) and mRNAs (DEmRNAs) in radiosensitive and radioresistant samples, followed by the construction of the miRNA-mRNA regulatory network and Gene Ontology and KEGG enrichment analysis. Additionally, a prognostic risk model was constructed, and its accuracy was evaluated by means of receiver operating characteristic analysis.

RESULTS

A total of 125 DEmiRNAs and 42 DEmRNAs were closely related to the radiosensitivity in patients with esophageal cancer. Based on 47 miRNA-mRNA interactions, including 21 miRNAs and 21 mRNAs, the miRNA-mRNA regulatory network was constructed. The prognostic risk model based on 2 miRNAs (miR-132-3p and miR-576-5p) and 4 mRNAs (CAND1, ZDHHC23, AHR, and MTMR4) could accurately predict the prognosis of esophageal cancer patients. Finally, it was verified that miR-132-3p/CAND1/ZDHHC23 and miR-576-5p/AHR could affect the radiosensitivity in esophageal cancer.

CONCLUSION

Our study demonstrated that miR-132-3p/CAND1/ZDHHC23 and miR-576-5p/AHR were critical molecular pathways related to the radiosensitivity of esophageal cancer.

Transcriptomic discovery of a theranostic signature (SERPINE1/MMP3/COL1A1/SPP1) for head and neck squamous cell carcinomas and identification of antrocinol as a candidate drug

Head and neck squamous cell carcinomas (HNSCC) are prevalent malignancies with a disappointing prognosis, necessitating the search for theranostic biomarkers for better management. Based on a meta-analysis of transcriptomic data containing ten clinical datasets of HNSCC and matched nonmalignant samples, we identified SERPINE1/MMP3/COL1A1/SPP1 as essential hub genes as the potential theranostic biomarkers. Our analysis suggests these hub genes are associated with the extracellular matrix, peptidoglycans, cell migration, wound-healing processes, complement and coagulation cascades, and the AGE-RAGE signaling pathway within the tumor microenvironment. Also, these hub genes were associated with tumor-immune infiltrating cells and immunosuppressive phenotypes of HNSCC. Further investigation of The Cancer Genome Atlas (TCGA) cohorts revealed that these hub genes were associated with staging, metastasis, and poor survival in HNSCC patients. Molecular docking simulations were performed to evaluate binding activities between the hub genes and antrocinol, a novel small-molecule derivative of an anticancer phytochemical antrocin previously discovered by our group. Antrocinol showed high affinities to MMP3 and COL1A1. Notably, antrocinol presented satisfactory drug-like and ADMET properties for therapeutic applications. These results hinted at the potential of antrocinol as an anti-HNSCC candidate via targeting MMP3 and COL1A1. In conclusion, we identified hub genes: SERPINE1/MMP3/COL1A1/SPP1 as potential diagnostic biomarkers and antrocinol as a potential new drug for HNSCC.

The commonness in immune infiltration of rheumatoid arthritis and atherosclerosis: Screening for central targets via microarray data analysis

Background

Although increasing evidence has reported an increased risk of atherosclerosis (AS) in rheumatoid arthritis (RA), the communal molecular mechanism of this phenomenon is still far from being fully elucidated. Hence, this article aimed to explore the pathogenesis of RA complicated with AS.

Methods

Based on the strict inclusion/exclusion criteria, four gene datasets were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the communal differentially expressed genes (DEGs) and hub genes, comprehensive bioinformatics analysis, including functional annotation, co-expression analysis, expression validation, drug-gene prediction, and TF-mRNA-miRNA regulatory network construction, was conducted. Moreover, the immune infiltration of RA and AS was analyzed and compared based on the CIBERSORT algorithm, and the correlation between hub genes and infiltrating immune cells was evaluated in RA and AS respectively.

Results

A total of 54 upregulated and 12 downregulated communal DEGs were screened between GSE100927 and GSE55457, and functional analysis of these genes indicated that the potential pathogenesis lies in immune terms. After the protein-protein interaction (PPI) network construction, a total of six hub genes (CCR5, CCR7, IL7R, PTPRC, CD2, and CD3D) were determined as hub genes, and the subsequent comprehensive bioinformatics analysis of the hub genes re-emphasized the importance of the immune system in RA and AS. Additionally, three overlapping infiltrating immune cells were found between RA and AS based on the CIBERSORT algorithm, including upregulated memory B cells, follicular helper T cells and γδT cells.

Conclusions

Our study uncover the communal central genes and commonness in immune infiltration between RA and AS, and the analysis of six hub genes and three immune cells profile might provide new insights into potential pathogenesis therapeutic direction of RA complicated with AS.