Identification of hub genes in papillary thyroid carcinoma: robust rank aggregation and weighted gene co-expression network analysis

Proteome-Wide Multicenter Mendelian Randomization Analysis to Identify Novel Therapeutic Targets for Lung Cancer

INTRODUCTION

Lung cancer (LC) remains a leading cause of cancer mortality worldwide, underscoring the urgent need for novel therapeutic targets. The integration of Mendelian randomization (MR) with proteomic data presents a novel approach to identifying potential targets for LC treatment.

METHODS

This study utilized a proteome-wide MR analysis, leveraging publicly available data from genome-wide association studies (GWAS) and protein quantitative trait loci (pQTL) studies. We analyzed genetic association data for LC from the TRICL-ILCCO Consortium and proteomic data from the Decode cohort. The MR framework was employed to estimate the causal effects of specific proteins on LC risk, supplemented by external validation, co-localization analyses, and exploration of protein-protein interaction (PPI) networks.

RESULTS

Our analysis identified five proteins (TFPI, ICAM5, SFTPB, COL6A3, EPHB1) with significant associations to LC risk. External validation confirmed the potential therapeutic relevance of ICAM5 and SFTPB. Co-localization analyses and PPI network exploration provided further insights into the biological pathways involved and their potential mechanistic roles in LC pathogenesis.

CONCLUSION

The study highlights the power of integrating genomic and proteomic data through MR analysis to uncover novel therapeutic targets for lung cancer. The identified proteins, particularly ICAM5 and SFTPB, offer promising directions for future research and development of targeted therapies, demonstrating the potential to advance personalized medicine in lung cancer treatment.

Bioinformatic identification reveals a m6A-binding protein, IGF2BP2, as a novel tumor-promoting gene signature in thyroid carcinoma

N6-methyladenosine (m6A) modification plays a crucial role in thyroid carcinoma (THCA). Insulin-like growth factor 2 binding protein 2 (IGF2BP2) is a m6A-binding protein. We aimed to explore the effect of IGF2BP2 on the development of THCA. Differentially expressed genes (DEGs) were screened from GSE50901 and GSE60542 datasets. LinkedOmics, Genebank, and Sequence-based RNA Adenosine Methylation Site Predictor databases were employed to find potential m6A modification sites. Protein-protein interaction network and receiver-operating characteristic curves were applied to determine hub genes of THCA. ESTIMATE revealed the effect of IGF2BP2 on tumor immunity. The mRNA expression of IGF2BP2 was detected using real-time quantitative polymerase chain reaction. The viability, migration, and invasion were assessed by Cell Counting Kit-8, wound healing, and transwell assays. A total of 166 common DEGs were identified from GSE50901 and GSE60542 datasets. One m6A-related gene, IGF2BP2, was differentially expressed in THCA and selected as the research target. The hub genes (CD44, DCN, CXCL12, ICAM1, SDC4, KIT, CTGF, and FMOD) were identified with high prediction values for THCA. Subsequently, the target genes of IGF2BP2, SDC4, and ICAM1, which had potential m6A modification sites, were screened out based on the hub genes. IGF2BP2 was upregulated in THCA and IGF2BP2 expression was positively correlated with immune infiltration in THCA. Additionally, knockdown of IGF2BP2 inhibited the proliferation, invasion, and migration of THCA cells. IGF2BP2 has a contributory effect on the progression of THCA, which is a novel biomarker and a therapeutic target for THCA.

Integrated gene expression profiling and functional enrichment analyses to discover biomarkers and pathways associated with Guillain-Barré syndrome and autism spectrum disorder to identify new therapeutic targets

Guillain-Barré syndrome (GBS) is one of the most prominent and acute immune-mediated peripheral neuropathy, while autism spectrum disorders (ASD) are a group of heterogeneous neurodevelopmental disorders. The complete mechanism regarding the neuropathophysiology of these disorders is still ambiguous. Even after recent breakthroughs in molecular biology, the link between GBS and ASD remains a mystery. Therefore, we have implemented well-established bioinformatic techniques to identify potential biomarkers and drug candidates for GBS and ASD. 17 common differentially expressed genes (DEGs) were identified for these two disorders, which later guided the rest of the research. Common genes identified the protein-protein interaction (PPI) network and pathways associated with both disorders. Based on the PPI network, the constructed hub gene and module analysis network determined two common DEGs, namely CXCL9 and CXCL10, which are vital in predicting the top drug candidates. Furthermore, coregulatory networks of TF-gene and TF-miRNA were built to detect the regulatory biomolecules. Among drug candidates, imatinib had the highest docking and MM-GBSA score with the well-known chemokine receptor CXCR3 and remained stable during the 100 ns molecular dynamics simulation validated by the principal component analysis and the dynamic cross-correlation map. This study predicted the gene-based disease network for GBS and ASD and suggested prospective drug candidates. However, more in-depth research is required for clinical validation.Communicated by Ramaswamy H. Sarma.

An Exploratory Data Analysis from Ovine and Bovine RNA-Seq Identifies Pathways and Key Genes Related to Cervical Dilatation

The present study developed a review and exploration of data in public and already validated repositories. The main objective was to identify the pathways involved in ruminants' cervical dilatation, which are conserved between cattle and sheep in the follicular and luteal phases of the reproductive cycle. In cattle, 1961 genes were more differentially expressed in the follicular phase and 1560 in the luteal phase. An amount of 24 genes were considered exclusively expressed from these. A total of 18 genes were in the follicular phase and 6 genes were in the luteal phase. In sheep, 2126 genes were more differentially expressed in the follicular phase and 2469 genes were more differentially expressed in the luteal phase. Hoxb genes were identified in both species and are correlated with the PI3K/Akt pathway. PI3K/Akt was also found in both cattle and sheep, appearing prominently in the follicular and luteal phases of both species. Our analyses have pointed out that the PI3K/Akt pathway and the Hoxb genes appear in prominence in modulating mechanisms that involve estrus alterations in the cervix. PI3K/Akt appears to be an important pathway in the cervical relaxation process.

Integrative Multiomics and Regulatory Network Analyses Uncovers the Role of OAS3, TRAFD1, miR-222-3p, and miR-125b-5p in Hepatitis E Virus Infection

The hepatitis E virus (HEV) is a long-ignored virus that has spread globally with time. It ranked 6th among the top risk-ranking viruses with high zoonotic spillover potential; thus, considering its viral threats is a pressing priority. The molecular pathophysiology of HEV infection or the underlying cause is limited. Therefore, we incorporated an unbiased, systematic methodology to get insights into the biological heterogeneity associated with the HEV. Our study fetched 93 and 2016 differentially expressed genes (DEGs) from chronic HEV (CHEV) infection in kidney-transplant patients, followed by hub module selection from a weighted gene co-expression network (WGCN). Most of the hub genes identified in this study were associated with interferon (IFN) signaling pathways. Amongst the genes induced by IFNs, the 2'-5'-oligoadenylate synthase 3 (OAS3) protein was upregulated. Protein-protein interaction (PPI) modular, functional enrichment, and feed-forward loop (FFL) analyses led to the identification of two key miRNAs, i.e., miR-222-3p and miR-125b-5p, which showed a strong association with the OAS3 gene and TRAF-type zinc finger domain containing 1 (TRAFD1) transcription factor (TF) based on essential centrality measures. Further experimental studies are required to substantiate the significance of these FFL-associated genes and miRNAs with their respective functions in CHEV. To our knowledge, it is the first time that miR-222-3p has been described as a reference miRNA for use in CHEV sample analyses. In conclusion, our study has enlightened a few budding targets of HEV, which might help us understand the cellular and molecular pathways dysregulated in HEV through various factors. Thus, providing a novel insight into its pathophysiology and progression dynamics.

Identification of hub genes and transcription factor regulatory network for heart failure using RNA-seq data and robust rank aggregation analysis

Background

Heart failure (HF) is the end stage of various cardiovascular diseases with a high mortality rate. Novel diagnostic and therapeutic biomarkers for HF are urgently required. Our research aims to identify HF-related hub genes and regulatory networks using bioinformatics and validation assays.

Methods

Using four RNA-seq datasets in the Gene Expression Omnibus (GEO) database, we screened differentially expressed genes (DEGs) of HF using Removal of Unwanted Variation from RNA-seq data (RUVSeq) and the robust rank aggregation (RRA) method. Then, hub genes were recognized using the STRING database and Cytoscape software with cytoHubba plug-in. Furthermore, reliable hub genes were validated by the GEO microarray datasets and quantitative reverse transcription polymerase chain reaction (qRT-PCR) using heart tissues from patients with HF and non-failing donors (NFDs). In addition, R packages “clusterProfiler” and “GSVA” were utilized for enrichment analysis. Moreover, the transcription factor (TF)–DEG regulatory network was constructed by Cytoscape and verified in a microarray dataset.

Results

A total of 201 robust DEGs were identified in patients with HF and NFDs. STRING and Cytoscape analysis recognized six hub genes, among which ASPN, COL1A1, and FMOD were confirmed as reliable hub genes through microarray datasets and qRT-PCR validation. Functional analysis showed that the DEGs and hub genes were enriched in T-cell-mediated immune response and myocardial glucose metabolism, which were closely associated with myocardial fibrosis. In addition, the TF–DEG regulatory network was constructed, and 13 significant TF–DEG pairs were finally identified.

Conclusion

Our study integrated different RNA-seq datasets using RUVSeq and the RRA method and identified ASPN, COL1A1, and FMOD as potential diagnostic biomarkers for HF. The results provide new insights into the underlying mechanisms and effective treatments of HF.

TFPI inhibits breast cancer progression by suppressing ERK/p38 MAPK signaling pathway

BACKGROUND

Tissue factor pathway inhibitor-1 (TFPI) is a serine protease inhibitor, which is responsible for inactivating TF-induced coagulation. Recently, increasing studies revealed that TFPI was lowly expressed in tumor cells and exhibited the antitumor activity.

OBJECTIVE

The aim of this study was to explore the role and underlying molecular mechanisms of TFPI in breast cancer.

METHODS

The expression and prognostic value of TFPI were analyzed using UALCAN and Kaplan-Meier plotter website. The expression level of TFPI in breast cancer tissues and cells was examined by immunohistochemistry (IHC) and western blot analysis, respectively. Cellular proliferation was evaluated by CCK-8 and colony formation assays. Cell migration and invasion were determined by transwell assay. The methylation level of TFPI promoter was determined by methylation-specific PCR.

RESULTS

TFPI expression was significantly lower in breast cancer tissues and cells compared to normal breast tissues and normal breast cells. Patients with low TFPI levels showed worse overall survival (OS). Furthermore, overexpression of TFPI significantly inhibited the proliferation, migration and invasion of breast cancer cells. Conversely, knockdown of TFPI promoted the proliferation, migration and invasion of breast cancer cells. Mechanistically, TFPI inhibited the ERK/p38 MAPK signaling pathway in breast cancer. Moreover, DNA hypermethylation of TFPI promoter was responsible for the downregulation of TFPI in breast cancer cells.

CONCLUSION

TFPI inhibited breast cancer cell proliferation, migration and invasion through inhibition of the ERK/p38 MAPK signaling pathway, suggesting that TFPI may serve as a novel prognostic biomarker and therapeutic target for breast cancer.

Identification of biomarkers, immune infiltration landscape, and treatment targets of ischemia-reperfusion acute kidney injury at an early stage by bioinformatics methods

Background

Mechanisms underlying ischemia/reperfusion injury-acute kidney injury (IRI-AKI) are not fully elucidated. We conducted an integrative analysis of IRI-AKI by bioinformatics methods.

Methods

We screened gene expression profiles of the IRI-AKI at early phase from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were identified and enrichment pathways were conducted based on gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) database, and Gene set enrichment analysis (GSEA). Immune cell infiltration analysis was performed to reveal the change of the microenvironment cell types. We constructed protein–protein interaction (PPI), and Cytoscape with plug-ins to find hub genes and modules. We performed robust rank aggregation (RRA) to combine DEGs and analyzed the target genes for miRNA/transcription factor (TF) and drug-gene interaction networks.

Results

A total of 239 and 384 DEGs were identified in GSE87024 and GSE34351 separately, with the 73 common DEGs. Enrichment analysis revealed that the significant pathways involve mitogen-activated protein kinase (MAPK) signaling, interleukin-17, and tumor necrosis factor (TNF) signaling pathway, etc. RRA analysis detected a total of 27 common DEGs. Immune cell infiltration analysis showed the plasma cells reduced and T cells increased in IRI-AKI. We identified JUN, ATF3, FOS, EGR1, HMOX1, DDIT3, JUNB, NFKBIZ, PPP1R15A, CXCL1, ATF4, and HSPA1B as hub genes. The target genes interacted with 23 miRNAs and 116 drugs or molecular compounds such as curcumin, staurosporine, and deferoxamine.

Conclusion

Our study first focused on the early IRI-AKI adopting RRA analysis to combine DEGs in different datasets. We identified significant biomarkers and crucial pathways involved in IRI-AKI and first construct the immune landscape and detected the potential therapeutic targets of the IRI-AKI by drug-gene network.

Supplementary Information

The online version contains supplementary material available at 10.1186/s41065-022-00236-x.

Identification of prognostic biomarkers for papillary thyroid carcinoma by a weighted gene co‐expression network analysis

Aim

Methods

Results

Conclusions

Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma

Background

Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes.

Methods

RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log₂ Fold change (FC)|> 1 and adjusted p value  < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA.

Results

In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA.

Conclusion

FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03204-7.

Expression and Clinical Significance of BCL2 Interacting Protein 3 Like in Multiple Myeloma

Multiple myeloma (MM) is one of the main blood disorders threatening human health today. This study aimed to examine the expression of BCL-2/adenovirus E1B 19 kDa-interacting protein 3-like (BNIP3L) in patients with MM and explore its mechanisms in silico. Bone marrow samples (n = 36 from patients with MM and n = 12 from healthy donors) were used to conduct BNIP3L expression analysis using immunohistochemistry. Microarray or RNA sequencing data from the Sequence Read Archive, Gene Expression Omnibus, and ArrayExpress databases were used to appraise BNIP3L expression and its prognostic role in patients with MM. The co-expressed genes of BNIP3L were identified for enrichment and protein-protein interaction (PPI) analyses to determine the associated signaling pathways. Immunohistochemistry indicated that BNIP3L expression in bone marrow of patients with MM was significantly lower than that in bone marrow of healthy donors. BNIP3L mRNA expression was also significantly lower in patients with MM than in healthy donors. The overall standard mean difference (SMD) for downregulation of BNIP3L was −0.62 [−1.17, −0.06], and the area under the curve was 0.81 [0.78, 0.85] based on a total of 694 MM cases. The overall survival analysis demonstrated that BNIP3L levels could act as an independent protective indicator of MM patient survival (HR = 0.79). Moreover, 261 co-expressed genes of BNIP3L were confirmed and found to be mainly involved in the adipocytokine signaling pathway. We preliminarily proved that downregulation of BNIP3L may play an important role in the occurrence and development of MM, and the promoting cancer capacity may be related to the pathway of adipocytokine signaling pathway.

Identification of hub genes in thyroid carcinoma to predict prognosis by integrated bioinformatics analysis

The aim of this study was to identify hub genes closely related to the pathogenesis and prognosis of thyroid carcinoma (THCA) by integrated bioinformatics analysis. In this study, through differential gene expression analysis, 1916 and 665 differentially expressed genes (DEGs) were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, and 7 and 11 co-expressed modules were identified from the TCGA-THCA and GSE153659 datasets, respectively, by weighted gene co-expression network analysis. We identified 162 overlapping genes between the DEGs and co-expression module genes as candidate hub genes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the 162 overlapping DEGs identified significant functions and pathways of THCA, such as thyroid hormone generation and metabolic process. A protein-protein interaction (PPI) analysis detected the top 10 hub genes (QSOX1, WFS1, EVA1A, FSTL3, CHRDL1, FABP4, PRDM16, PPARGC1A, PPARG, COL23A1). Finally, survival analysis, clinical correlation analysis, and protein abundance validation confirmed that 3 of the 10 hub genes were associated with survival prognosis of patients with THCA, and 8 of them were associated with the clinical stages of THCA. In summary, we identified hub genes and key modules that were closely related to THCA, and validated these genes by survival analysis, clinical correlation analysis, and Human Protein Atlas image analysis. Our results provide important information that will help to elucidate the pathogenesis of THCA and identify novel candidate prognostic biomarkers and potential therapeutic targets.

Integrated analysis of transcriptomics to identify hub genes in primary Sjögren's syndrome

OBJECTIVE

The treatment of patients with primary Sjögren's syndrome is a clinical challenge. Gene expression profile analysis and comprehensive network methods for complex diseases can provide insight into molecular characteristics in the clinical context.

MATERIALS AND METHODS

We downloaded gene expression datasets from the Gene Expression Omnibus (GEO) database. We screened differentially expressed genes (DEG) between the pSS patients and the controls by the robust rank aggregation (RRA) method. We explored DEGs' potential function using gene function annotation and PPI network analysis.

RESULTS

GSE23117 GSE40611 GSE80805 and GSE127952were included, including 38 patients and 30 controls. The RRA integrated analysis determined 294 significant DEGs (241 upregulated and 53 downregulated), and the most significant gene aberrantly expressed in SS was CXCL9 (p = 6.39E-15), followed by CXCL13 (p = 1.53E-13). Immune response (GO:0006955; p = 4.29E-32) was the most significantly enriched biological process in GO (gene ontology) analysis. KEGG pathway enrichment analysis showed that cytokine-cytokine receptor interaction (hsa04060; p = 6.46E-10) and chemokine signaling pathway (hsa04062; p = 9.54E-09) were significantly enriched. We defined PTPRC, CD86, and LCP2 as the hub genes based on the PPI results.

CONCLUSION

Our integrated analysis identified gene signatures and helped understand molecular changes in pSS.

Upregulation of ATIC in multiple myeloma tissues based on tissue microarray and gene microarrays

PURPOSE

Multiple myeloma (MM) is characterized by the malignant proliferation of plasma cells, which produce a monoclonal immunoglobulin protein. The role of 5-aminoimidazole-4-carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase (ATIC) has not yet been well studied in the area of MM. Thus, in the current study, we sought to examine the expression levels, including mRNA and protein levels of ATIC in MM.

METHODS

Multiple myeloma microarray and RNA-seq data were screened from the SRA, GEO, ArrayExpress, and Oncomine databases. The mRNA level of ATIC was extracted from the high throughput data, and the prognostic value was studied. The protein level of ATIC was also detected by in-house immunohistochemistry on a tissue microarray. Potential signaling pathways were enriched with ATIC-related genes in MM.

RESULTS

Both the mRNA and protein levels of ATIC were significantly upregulated in MM samples as compared to normal samples. Furthermore, the summarized Standardized Mean Difference was 1.66 with 674 cases of MM based on 10 independent studies including the in-house tissue microarray. The overall hazard ratio of ATIC in MM was 1.7 with 1631 cases of MM based on five microarrays. In the KEGG pathway analysis, the ATIC-related genes were mainly enriched in the pathway of complement and coagulation cascades.

CONCLUSION

We provided the first evidence supporting the upregulation of ATIC may play an essential part in the tumorigenesis and development of MM. The promoting cancer capacity may be related to the pathway of complement and coagulation cascades.