Crosstalk analysis of pathways in breast cancer using a network model based on overlapping differentially expressed genes

Integrative network analysis of transcriptomics data reveals potential prognostic biomarkers for colorectal cancer

INTRODUCTION

Cross-talk among biological pathways is essential for normal biological function and plays a significant role in cancer progression. Through integrated network analysis, this study explores the significance of pathway cross-talk in colorectal cancer (CRC) development at both the pathway and gene levels.

METHODS

In this study, we integrated the gene expression data with domain knowledge to construct state-dependent pathway cross-talk networks. The significance of the genes involved in pathway cross-talk was assessed by analyzing their association with cancer hallmarks, disease-gene relation, genetic alterations, and survival analysis. We also analyzed the gene regulatory network to identify the dysregulated genes and their role in CRC progression.

RESULTS

Cross-talk was observed between immune-related pathways and pathways associated with cell communication and signaling. The PTPRC gene was identified as a mediator, facilitating interactions within the immune system and other signaling pathways. The rewired interactions of ITGA7 were identified as influential in the epithelial-mesenchymal transition in CRC. This study also highlighted the crucial link between cell communication and vascular smooth muscle contraction pathway in CRC progression. The survival analysis of identified gene clusters showed their significant prognostic value in distinguishing high-risk from low-risk CRC groups, and L1000CDS2 revealed seven potential drug molecules in CRC. Nine dysregulated genes (CTNNB1, EP300, JUN, MYC, NFKB1, RELA, SP1, STAT1, and TP53) emerge as transcription factors acting as common regulators across various pathways.

CONCLUSIONS

This study highlights the crucial role of pathway cross-talk in CRC progression and identified the potential prognostic biomarkers and potential drug molecules.

Identification of Crucial Genes and Key Functions in Type 2 Diabetic Hearts by Bioinformatic Analysis

Type 2 diabetes (T2D) patients with SARS-CoV-2 infection hospitalized develop an acute cardiovascular syndrome. It is urgent to elucidate underlying mechanisms associated with the acute cardiac injury in T2D hearts. We performed bioinformatic analysis on the expression profiles of public datasets to identify the pathogenic and prognostic genes in T2D hearts. Cardiac RNA-sequencing datasets from db/db or BKS mice (GSE161931) were updated to NCBI-Gene Expression Omnibus (NCBI-GEO), and used for the transcriptomics analyses with public datasets from NCBI-GEO of autopsy heart specimens with COVID-19 (5/6 with T2D, GSE150316), or dead healthy persons (GSE133054). Differentially expressed genes (DEGs) and overlapping homologous DEGs among the three datasets were identified using DESeq2. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes analyses were conducted for event enrichment through clusterProfile. The protein-protein interaction (PPI) network of DEGs was established and visualized by Cytoscape. The transcriptions and functions of crucial genes were further validated in db/db hearts. In total, 542 up-regulated and 485 down-regulated DEGs in mice, and 811 up-regulated and 1399 down-regulated DEGs in human were identified, respectively. There were 74 overlapping homologous DEGs among all datasets. Mitochondria inner membrane and serine-type endopeptidase activity were further identified as the top-10 GO events for overlapping DEGs. Cardiac CAPNS1 (calpain small subunit 1) was the unique crucial gene shared by both enriched events. Its transcriptional level significantly increased in T2D mice, but surprisingly decreased in T2D patients with SARS-CoV-2 infection. PPI network was constructed with 30 interactions in overlapping DEGs, including CAPNS1. The substrates Junctophilin2 (Jp2), Tnni3, and Mybpc3 in cardiac calpain/CAPNS1 pathway showed less transcriptional change, although Capns1 increased in transcription in db/db mice. Instead, cytoplasmic JP2 significantly reduced and its hydrolyzed product JP2NT exhibited nuclear translocation in myocardium. This study suggests CAPNS1 is a crucial gene in T2D hearts. Its transcriptional upregulation leads to calpain/CAPNS1-associated JP2 hydrolysis and JP2NT nuclear translocation. Therefore, attenuated cardiac CAPNS1 transcription in T2D patients with SARS-CoV-2 infection highlights a novel target in adverse prognostics and comprehensive therapy. CAPNS1 can also be explored for the molecular signaling involving the onset, progression and prognostic in T2D patients with SARS-CoV-2 infection.

Crosstalk between MMP-13, CD44, and TWIST1 and its role in regulation of EMT in patients with esophageal squamous cell carcinoma

Matrix metalloproteinases (MMPs) play key roles in epithelial-mesenchymal transition (EMT) for the development of cancer cell invasion and metastasis. MMP-13 is an extracellular matrix (ECM)-degrading enzyme that plays crucial roles in angiogenesis, cell cycle regulation, niche maintenance, and transforming squamous epithelial cells in various tissues. CD44, a transmembrane glycoprotein expressed on esophageal tumor cells, is required for EMT induction and invasion in esophageal squamous cell carcinoma (ESCC). The transcription factor TWIST1, as EMT and stemness marker, regulates MMPs expression and is identified as the downstream target of CD44. In this study, we aimed to investigate the probable interplay between the expression of key genes contributing to ESCC development, including MMP-13, TWIST1, and CD44 with clinical features for introducing novel diagnostic and therapeutic targets in the disease. The gene expression profiling of MMP-13, TWIST1, and CD44 was performed using quantitative real-time PCR in tumor tissues from 50 ESCC patients compared to corresponding margin non-tumoral tissues. Significant overexpression of MMP-13, CD44S, CD44V3, CD44V6, and TWIST1 were observed in 74%, 36%, 44%, 44%, and 52% of ESCC tumor samples, respectively. Overexpression of MMP-13 was associated with stage of tumor progression, metastasis, and tumor location (P < 0.05). There was a significant correlation between TWIST1 overexpression and grade (P < 0.05). Furthermore, overexpression of CD44 variants was associated with stage of tumor progression, grade, tumor invasion, and location (P < 0.05). The results indicated the significant correlation between concomitant expression of MMP-13/TWIST1, TWIST1/CD44, and CD44/MMP-13 with each other in a variety of clinicopathological traits, including depth of tumor invasion, tumor location, stage of tumor, and lymph node involvement in ESCC tissue samples (P < 0.05). Collectively, our results indicate that the TWIST1-CD44-MMP-13 axis is involved in tumor aggressiveness, proposing these genes as regulators of EMT, diagnostic markers, and therapeutic targets in ESCC.

Identification of Key Candidate Genes and Chemical Perturbagens in Diabetic Kidney Disease Using Integrated Bioinformatics Analysis

Globally, nearly 40 percent of all diabetic patients develop serious diabetic kidney disease (DKD). The identification of the potential early-stage biomarkers and elucidation of their underlying molecular mechanisms in DKD are required. In this study, we performed integrated bioinformatics analysis on the expression profiles GSE111154, GSE30528 and GSE30529 associated with early diabetic nephropathy (EDN), glomerular DKD (GDKD) and tubular DKD (TDKD), respectively. A total of 1,241, 318 and 280 differentially expressed genes (DEGs) were identified for GSE30258, GSE30529, and GSE111154 respectively. Subsequently, 280 upregulated and 27 downregulated DEGs shared between the three GSE datasets were identified. Further analysis of the gene expression levels conducted on the hub genes revealed SPARC (Secreted Protein Acidic And Cysteine Rich), POSTN (periostin), LUM (Lumican), KNG1 (Kininogen 1), FN1 (Fibronectin 1), VCAN (Versican) and PTPRO (Protein Tyrosine Phosphatase Receptor Type O) having potential roles in DKD progression. FN1, LUM and VCAN were identified as upregulated genes for GDKD whereas the downregulation of PTPRO was associated with all three diseases. Both POSTN and SPARC were identified as the overexpressed putative biomarkers whereas KNG1 was found as downregulated in TDKD. Additionally, we also identified two drugs, namely pidorubicine, a topoisomerase inhibitor (LINCS ID- BRD-K04548931) and Polo-like kinase inhibitor (LINCS ID- BRD-K41652870) having the validated role in reversing the differential gene expression patterns observed in the three GSE datasets used. Collectively, this study aids in the understanding of the molecular drivers, critical genes and pathways that underlie DKD initiation and progression.

Integrative analysis of highly mutated genes in hepatitis B virus-related hepatic carcinoma

Gene mutation is responsible for the development of hepatocellular carcinoma (HCC) with hepatitis B virus (HBV) infection; however, the characteristics and associated biological functions of highly mutated genes, in which the mutation frequencies are at least 5% in HCC patients with HBV infection, are not clearly evaluated. In the study, we analyzed the information regarding somatic mutation obtained by whole‐exome sequencing in 280 HBV‐related HCC tissues from public databases and published studies. Via integrative analysis, 78 genes, including TP53, TTN, MUC16, CTNNB1, and PCLO were summarized as highly mutated genes, and some of these mutated genes were further identified as cancer driver genes. Besides, we discovered that the highly mutated genes were enriched with various biological functions and pathways. The expression of many of highly mutated genes was found to be significantly altered in HBV‐related HCC, and several highly mutated genes were related to a variety of clinical factors and associated with the poor survival of the disease. Taken together, these results could enrich our understanding of highly mutated genes and their relationships with HBV‐related HCC. Some of the identified highly mutated genes might be used as novel biomarkers of disease prognosis, or as molecular targets for the treatment of HCC with HBV infection.

RankProd Combined with Genetic Algorithm Optimized Artificial Neural Network Establishes a Diagnostic and Prognostic Prediction Model that Revealed C1QTNF3 as a Biomarker for Prostate Cancer

Prostate cancer (PCa) is the most commonly diagnosed cancer in males in the Western world. Although prostate-specific antigen (PSA) has been widely used as a biomarker for PCa diagnosis, its results can be controversial. Therefore, new biomarkers are needed to enhance the clinical management of PCa. From publicly available microarray data, differentially expressed genes (DEGs) were identified by meta-analysis with RankProd. Genetic algorithm optimized artificial neural network (GA-ANN) was introduced to establish a diagnostic prediction model and to filter candidate genes. The diagnostic and prognostic capability of the prediction model and candidate genes were investigated in both GEO and TCGA datasets. Candidate genes were further validated by qPCR, Western Blot and Tissue microarray. By RankProd meta-analyses, 2306 significantly up- and 1311 down-regulated probes were found in 133 cases and 30 controls microarray data. The overall accuracy rate of the PCa diagnostic prediction model, consisting of a 15-gene signature, reached up to 100% in both the training and test dataset. The prediction model also showed good results for the diagnosis (AUC = 0.953) and prognosis (AUC of 5 years overall survival time = 0.808) of PCa in the TCGA database. The expression levels of three genes, FABP5, C1QTNF3 and LPHN3, were validated by qPCR. C1QTNF3 high expression was further validated in PCa tissue by Western Blot and Tissue microarray. In the GEO datasets, C1QTNF3 was a good predictor for the diagnosis of PCa (GSE6956: AUC = 0.791; GSE8218: AUC = 0.868; GSE26910: AUC = 0.972). In the TCGA database, C1QTNF3 was significantly associated with PCa patient recurrence free survival (P < .001, AUC = 0.57). In this study, we have developed a diagnostic and prognostic prediction model for PCa. C1QTNF3 was revealed as a promising biomarker for PCa. This approach can be applied to other high-throughput data from different platforms for the discovery of oncogenes or biomarkers in different kinds of diseases.

Identification and bioinformatics analysis of overlapping differentially expressed genes in depression, papillary thyroid cancer and uterine fibroids

It is hypothesized that there may be common characteristics between the genetic regulatory networks of different diseases. To identify these potential similarities, analysis of overlapping differentially expressed genes (DEGs) in several diseases, which are believed to be associated in traditional Chinese medicine (TCM) was performed in the present study. The gene expression profiles associated with depression, papillary thyroid carcinoma (PTC) and uterine fibroids (UF) were preliminarily analyzed using Gene Expression Omnibus 2R tools. Gene Ontology enrichment analysis, Kyoto Encyclopedia of Genes and Genomes pathway analysis and protein-protein interaction network analysis of the overlapping DEGs in depression, PTC and UF was performed. The results indicated that multiple genes, including activating transcription factor 3 and WSC domain containing 2 and the phosphoinositide 3 kinase/protein kinase b signaling pathway and its downstream effectors may be common factors associated with depression, PTC and/or UF. The neuroendocrine functions of the hypothalamic-pituitary-ovarian axis and hypothalamic-pituitary-thyroid axis were also identified as being mutually associated with depression, PTC and/or UF. However, due to the limitations of DNA microassays, it is recommended that future studies take epigenetics into consideration. Further transcriptomic, methylomic and metabolomic analyses of depression, PTC and UF are also required to identify and elucidate the key associated biomarkers. In conclusion, the results of the current study shed light on the potential genetic interconnections between depression, PTC and UF, which may be beneficial for understanding their underlying coregulatory mechanisms and contributing to the development of homeotherapy based on bioinformatics prediction.

G9A performs important roles in the progression of breast cancer through upregulating its targets

Breast cancer (BC) is the most common type of malignancy in females worldwide, however, its underlying mechanisms remain poorly understood. The present study aimed to investigate the mechanisms behind the development and progression of BC and identify potential biomarkers for it. The chromatin immunoprecipitation-DNA sequencing (ChIP-Seq) dataset GSM1642516 and gene expression dataset GSE34925 were downloaded from the Gene Expression Omnibus database. Affy and oligo packages were used for the background correction and normalization of the gene expression dataset. Based on Limma package and the criteria of a fold change >1.41 or <0.71, and a false discovery rate adjusted P-value <0.05, differentially-expressed genes (DEGs) in euchromatic histone lysine methyltransferase 2 (G9A) -knockout (KO) breast samples compared with control samples were identified. The Database for Annotation, Visualization and Integrated Analysis was used for the functional enrichment analysis of the DEGs. Bowtie 2 and model-based analysis of ChIP-Seq version 14 (macs14) were used for the mapping of raw reads and the identification of G9A binding sites (peaks), respectively. In addition, overlapping genes between the DEGs and genes in the peaks located in −3000 to 3000 bp centered in the transcription start sites (conpeaks) were screened out and microRNAs (miRNAs) believed to regulate those overlaps were identified through the TargetScan database. A total of 217 DEGs were identified in G9A-KO samples, which were mainly involved in the biological processes and pathways associated with the inflammatory response and cancer progression. A total of 10,422 peaks, containing 1,210 conpeaks involving 1,138 genes, were identified. Among the 1,138 genes, 15 were overlapped with the DEGs, and 35 miRNAs were identified to regulate those overlaps. Insulin-induced gene 1 was regulated by 9 genes in the miRNA-gene regulation network, which may indicate its importance in the progression of BC. The present study identified potential biomarkers of BC that may be useful in the diagnosis and treatment of patients with the disease.

A Novel Method for Pathway Identification Based on Attractor and Crosstalk in Polyarticular Juvenile Idiopathic Arthritis

Background

Juvenile idiopathic arthritis (JIA) is one of the most common inflammatory disorders of unknown etiology. We introduced a novel method to identify dysregulated pathways associated with polyarticular JIA (pJIA).

Material/Methods

Gene expression profiling of 61 children with pJIA and 59 healthy controls were collected from E-GEOD-13849; 300 pathways were obtained from Kyoto Encyclopedia of Genes and Genomes (KEGG) database and 787,896 protein-protein interaction sets were gathered from the Retrieval of Interacting Genes. Attractor and crosstalk were designed to complement each other to increase the integrity of pathways assessment. Then, impact factor was used to assess the interactions inter-pathways, and RP-value was used to evaluate the comprehensive influential ability of attractors.

Results

There were seven attractors with p<0.01 and 14 pathways with RP<0.01. Finally, two significantly dysfunctional pathways were found, which were related to pJIA progression: p53 signaling pathway (KEGG ID: 04115) and non-alcoholic fatty liver disease (NAFLD) (KEGG ID: 04932).

Conclusions

A novel approach that identified the dysregulated pathways in pJIA was constructed based on attractor and crosstalk. The new process is expected to be efficient in the upcoming era of medicine.

Revealing complex function, process and pathway interactions with high-throughput expression and biological annotation data

The biological relationships both between and within the functions, processes and pathways that operate within complex biological systems are only poorly characterized, making the interpretation of large scale gene expression datasets extremely challenging. Here, we present an approach that integrates gene expression and biological annotation data to identify and describe the interactions between biological functions, processes and pathways that govern a phenotype of interest. The product is a global, interconnected network, not of genes but of functions, processes and pathways, that represents the biological relationships within the system. We validated our approach on two high-throughput expression datasets describing organismal and organ development. Our findings are well supported by the available literature, confirming that developmental processes and apoptosis play key roles in cell differentiation. Furthermore, our results suggest that processes related to pluripotency and lineage commitment, which are known to be critical for development, interact mainly indirectly, through genes implicated in more general biological processes. Moreover, we provide evidence that supports the relevance of cell spatial organization in the developing liver for proper liver function. Our strategy can be viewed as an abstraction that is useful to interpret high-throughput data and devise further experiments.

Pathway Analysis Based on Attractor and Cross Talk in Colon Cancer

Colon cancer is the third and second most common cancer form in men and women worldwide. It is generally accepted that colon cancer mainly results from diet. The aim of this study was to identify core pathways which elucidated the molecular mechanisms in colon cancer. The microarray data of E-GEOD-44861 was downloaded from ArrayExpress database. All human pathways were obtained from Kyoto Encyclopedia of Genes and Genomes database. In total, 135 differential expressed genes (DEG) were identified using Linear Models for Microarray Data package. Differential pathways were identified with the method of attractor after overlapping with DEG. Pathway cross talk network (PCN) was constructed by combining protein-protein interactions and differential pathways. Cross talks of all pathways were obtained in PCN. There were 65 pathways with RankProd (RP) values < 0.05 and 16 pathways with Impact Factors (IF) values > 100. Five pathways were satisfied with P value < 0.05, RP values < 0.05, and IF values > 100, which were considered to be the most important pathways in colon cancer. In conclusion, the five pathways were identified in the center status of colon cancer, which may contribute to understanding the mechanism and development of colon cancer.