Network-based integration of mRNA and miRNA profiles reveals new target genes involved in pancreatic cancer

MISP-mediated enhancement of pancreatic cancer growth through the Wnt/β-catenin signaling pathway is suppressed by Fisetin

Pancreatic cancer is a highly malignant tumor characterized by high mortality and low survival rates. The mitotic interactor and substrate of Plk1 (MISP) is a cancer-associated protein that regulates mitotic spindle localization and is highly expressed in several malignant tumors, contributing to tumor development. However, the function and regulatory mechanisms of MISP in pancreatic cancer remain unclear. In this study, we analyzed RNA sequencing data related to pancreatic cancer from the TCGA and GEO databases, identifying MISP as a potential prognostic marker for the disease. MISP was significantly upregulated in pancreatic cancer cells and tissues compared to normal pancreatic cells and tissues. Notably, in pancreatic cancer cells, high MISP protein expression promoted cell proliferation and growth. Mechanistically, the upregulation of MISP facilitated the nuclear accumulation of β-catenin, thereby activating the Wnt/β-catenin signaling pathway and promoting pancreatic cancer growth. In search of effective inhibitors of MISP expression, we screened an FDA-approved drug library and identified Fisetin as a potential suppressor of MISP expression. Fisetin was found to downregulate the transcription factor MYB, thereby reducing MISP expression. Further experiments demonstrated that Fisetin effectively inhibited the in vitro and in vivo growth of pancreatic cancer by suppressing the MISP/Wnt/β-catenin signaling axis. In summary, our research has identified MISP as a novel therapeutic target in pancreatic cancer and uncovered its associated regulatory mechanisms.

Differential expression of long non-coding RNAs in colon cancer: Insights from transcriptomic analysis

BACKGROUND

Colon Cancer (CC) incidence has sharply grown in recent years. Long non-coding RNAs (lncRNA) are produced by a group of non-protein-coding genes, and have important functions in controlling gene expression and impacting the biological features of various malignancies including CC.

METHODS

Our research focused on examining the function of lncRNAs in the development of colon cancer. To this end, we selected and analyzed a dataset (GSE104836) from the GEO database, which contained information about the expression of mRNAs and lncRNAs in both colon cancer tissues and normal adjacent paired tumor tissues. The DESeq2 R package in Bioconductor was used to identify differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) that showed differences in expression levels. Next, by literature review of previous studies, we chose two lncRNAs (FENDRR and LINC00092) for additional studies. To validate our findings, a series of tests were performed on a total of 31 tumor tissues and normal paired adjacent tumor tissues. The lncRNA expression levels were assessed in tumor tissues as well as in surrounding normal tumor tissues.

RESULTS

The data confirmed that just two particular lncRNAs, FENDRR and LINC00092, had considerably decreased expression levels throughout all stages of cancer. In addition, the survival assay was conducted using the GEPIA2 software, revealing that a reduced expression of FENDRR is correlated with a reduced overall survival. Furthermore, our investigation using receiver operating characteristic (ROC) methodology revealed that these two lncRNAs had significant discriminatory ability between colon cancer and normal tissues. To determine the cause of the decrease in the activity of these two long non-coding RNAs (lncRNAs), we used methylation-specific PCR (MSP) to examine the methylation pattern of their promoter regions. Our investigation revealed hypermethylation in the promoter regions of FENDRR and LINC00092 within tumor tissues compared to normal adjacent tumor tissues.

CONCLUSION

Taken together, our findings revealed the lncRNAs signatures as potential therapeutic targets and molecular diagnostic biomarkers in colon cancer. Furthermore, the evidence provided substantiates the important role of promoter methylation in regulating the expression levels for both of these lncRNAs.

Investigation of the relationship between COVID-19 and pancreatic cancer using bioinformatics and systems biology approaches

BACKGROUND

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus, poses a huge threat to human health. Pancreatic cancer (PC) is a malignant tumor with high mortality. Research suggests that infection with SARS-CoV-2 may increase disease severity and risk of death in patients with pancreatic cancer, while pancreatic cancer may also increase the likelihood of contracting SARS-CoV-2, but the link is unclear.

METHODS

This study investigated the transcriptional profiles of COVID-19 and PC patients, along with their respective healthy controls, using bioinformatics and systems biology approaches to uncover the molecular mechanisms linking the 2 diseases. Specifically, gene expression data for COVID-19 and PC patients were obtained from the Gene Expression Omnibus datasets, and common differentially expressed genes (DEGs) were identified. Gene ontology and pathway enrichment analyses were performed on the common DEGs to elucidate the regulatory relationships between the diseases. Additionally, hub genes were identified by constructing a protein-protein interaction network from the shared DEGs. Using these hub genes, we conducted regulatory network analyses of microRNA/transcription factors-genes relationships, and predicted potential drugs for treating COVID-19 and PC.

RESULTS

A total of 1722 and 2979 DEGs were identified from the transcriptome data of PC (GSE119794) and COVID-19 (GSE196822), respectively. Among these, 236 common DEGs were found between COVID-19 and PC based on protein-protein interaction analysis. Functional enrichment analysis indicated that these shared DEGs were involved in pathways related to viral genome replication and tumorigenesis. Additionally, 10 hub genes, including extra spindle pole bodies like 1, holliday junction recognition protein, marker of proliferation Ki-67, kinesin family member 4A, cyclin-dependent kinase 1, topoisomerase II alpha, cyclin B2, ubiquitin-conjugating enzyme E2 C, aurora kinase B, and targeting protein for Xklp2, were identified. Regulatory network analysis revealed 42 transcription factors and 23 microRNAs as transcriptional regulatory signals. Importantly, lucanthone, etoposide, troglitazone, resveratrol, calcitriol, ciclopirox, dasatinib, enterolactone, methotrexate, and irinotecan emerged as potential therapeutic agents against both COVID-19 and PC.

CONCLUSION

This study unveils potential shared pathogenic mechanisms between PC and COVID-19, offering novel insights for future research and therapeutic strategies for the treatment of PC and SARS-CoV-2 infection.

IFNα-induced BST2+ tumor-associated macrophages facilitate immunosuppression and tumor growth in pancreatic cancer by ERK-CXCL7 signaling

Pancreatic ductal adenocarcinoma (PDAC) features an immunosuppressive tumor microenvironment (TME) that resists immunotherapy. Tumor-associated macrophages, abundant in the TME, modulate T cell responses. Bone marrow stromal antigen 2-positive (BST2+) macrophages increase in KrasG12D/+; Trp53R172H/+; Pdx1-Cre mouse models during PDAC progression. However, their role in PDAC remains elusive. Our findings reveal a negative correlation between BST2+ macrophage levels and PDAC patient prognosis. Moreover, an increased ratio of exhausted CD8+ T cells is observed in tumors with up-regulated BST2+ macrophages. Mechanistically, BST2+ macrophages secrete CXCL7 through the ERK pathway and bind with CXCR2 to activate the AKT/mTOR pathway, promoting CD8+ T cell exhaustion. The combined blockade of CXCL7 and programmed death-ligand 1 successfully decelerates tumor growth. Additionally, cGAS-STING pathway activation in macrophages induces interferon (IFN)α synthesis leading to BST2 overexpression in the PDAC TME. This study provides insights into IFNα-induced BST2+ macrophages driving an immune-suppressive TME through ERK-CXCL7 signaling to regulate CD8+ T cell exhaustion in PDAC.

Uncovering the link between human endogenous retroviruses, inflammatory pathways, and gastric cancer development

BACKGROUND

Endogenous retroviruses, previously deemed "junk" DNA, have gained attention in recent scientific studies. These inherited genomic elements are now recognized for their potential roles in diseases, especially cancer, highlighting their value as potential diagnostic or therapeutic targets.

OBJECTIVE

This research aims to explore the association between human endogenous retroviruses (HERV) and gastric cancer, focusing on discerning HERV expression patterns and understanding their implications in gastric cancer pathology.

METHODS

A quantitative analysis of HERV expression was conducted, employing Support Vector Machine (SVM) and AdaBoost algorithms to identify discriminative HERVs. The co-regulation network between protein-coding genes and HERVs was constructed using the Weighted Gene Co-expression Network Analysis (WGCNA).

RESULTS

Three distinct HERVs (LTR16A|72|451, LTR91|636|874, LTR27D|87|222) were identified as significantly different. Strong correlations were found between HERVs, and gene sets enriched in the inflammatory pathway.

CONCLUSIONS

HERVs appear to influence abnormal inflammatory responses, suggesting a pivotal role in gastric cancer development.

ITGA3 acts as a purity-independent biomarker of both immunotherapy and chemotherapy resistance in pancreatic cancer: bioinformatics and experimental analysis

Contribution of integrin superfamily genes to treatment resistance remains uncertain. Genome patterns of thirty integrin superfamily genes were analyzed of using bulk and single-cell RNA sequencing, mutation, copy number, methylation, clinical information, immune cell infiltration, and drug sensitivity data. To select the integrins that are most strongly associated with treatment resistance in pancreatic cancer, a purity-independent RNA regulation network including integrins were constructed using machine learning. The integrin superfamily genes exhibit extensive dysregulated expression, genome alterations, epigenetic modifications, immune cell infiltration, and drug sensitivity, as evidenced by multi-omics data. However, their heterogeneity varies among different cancers. After constructing a three-gene (TMEM80, EIF4EBP1, and ITGA3) purity-independent Cox regression model using machine learning, ITGA3 was identified as a critical integrin subunit gene in pancreatic cancer. ITGA3 is involved in the molecular transformation from the classical to the basal subtype in pancreatic cancer. Elevated ITGA3 expression correlated with a malignant phenotype characterized by higher PD-L1 expression and reduced CD8+ T cell infiltration, resulting in unfavorable outcomes in patients receiving either chemotherapy or immunotherapy. Our findings suggest that ITGA3 is an important integrin in pancreatic cancer, contributing to chemotherapy resistance and immune checkpoint blockade therapy resistance.

Metastatic phenotype and immunosuppressive tumour microenvironment in pancreatic ductal adenocarcinoma: Key role of the urokinase plasminogen activator (PLAU)

Background

Previous studies have revealed the role of dysregulated urokinase plasminogen activator (encoded by PLAU) expression and activity in several pathways associated with cancer progression. However, systematic investigation into the association of PLAU expression with factors that modulate PDAC (pancreatic ductal adenocarcinoma) progression is lacking, such as those affecting stromal (pancreatic stellate cell, PSC)-cancer cell interactions, tumour immunity, PDAC subtypes and clinical outcomes from potential PLAU inhibition.

Methods

This study used an integrated bioinformatics approach to identify prognostic markers correlated with PLAU expression using different transcriptomics, proteomics, and clinical data sets. We then determined the association of dysregulated PLAU and correlated signatures with oncogenic pathways, metastatic phenotypes, stroma, immunosuppressive tumour microenvironment (TME) and clinical outcome. Finally, using an in vivo orthotopic model of pancreatic cancer, we confirmed the predicted effect of inhibiting PLAU on tumour growth and metastasis.

Results

Our analyses revealed that PLAU upregulation is not only associated with numerous other prognostic markers but also associated with the activation of various oncogenic signalling pathways, aggressive phenotypes relevant to PDAC growth and metastasis, such as proliferation, epithelial-mesenchymal transition (EMT), stemness, hypoxia, extracellular cell matrix (ECM) degradation, upregulation of stromal signatures, and immune suppression in the tumour microenvironment (TME). Moreover, the upregulation of PLAU was directly connected with signalling pathways known to mediate PSC-cancer cell interactions. Furthermore, PLAU upregulation was associated with the aggressive basal/squamous phenotype of PDAC and significantly reduced overall survival, indicating that this subset of patients may benefit from therapeutic interventions to inhibit PLAU activity. Our studies with a clinically relevant orthotopic pancreatic model showed that even short-term PLAU inhibition is sufficient to significantly halt tumour growth and, importantly, eliminate visible metastasis.

Conclusion

Elevated PLAU correlates with increased aggressive phenotypes, stromal score, and immune suppression in PDAC. PLAU upregulation is also closely associated with the basal subtype type of PDAC; patients with this subtype are at high risk of mortality from the disease and may benefit from therapeutic targeting of PLAU.

Monocarboxylate Transporters Are Involved in Extracellular Matrix Remodelling in Pancreatic Ductal Adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy with a five-year survival rate of <8%. PDAC is characterised by desmoplasia with an abundant extracellular matrix (ECM) rendering current therapies ineffective. Monocarboxylate transporters (MCTs) are key regulators of cellular metabolism and are upregulated in different cancers; however, their role in PDAC desmoplasia is little understood. Here, we investigated MCT and ECM gene expression in primary PDAC patient biopsies using RNA-sequencing data obtained from Gene Expression Omnibus. We generated a hypernetwork model from these data to investigate whether a causal relationship exists between MCTs and ECMs. Our analysis of stromal and epithelial tissues (n = 189) revealed nine differentially expressed MCTs, including the upregulation of SLC16A2/6/10 and the non-coding SLC16A1-AS1, and 502 ECMs, including collagens, laminins, and ECM remodelling enzymes (false discovery rate < 0.05). A causal hypernetwork analysis demonstrated a bidirectional relationship between MCTs and ECMs; four MCT and 255 ECM-related transcripts correlated with 90% of the differentially expressed ECMs (n = 376) and MCTs (n = 7), respectively. The hypernetwork model was robust, established by iterated sampling, direct path analysis, validation by an independent dataset, and random forests. This transcriptomic analysis highlights the role of MCTs in PDAC desmoplasia via associations with ECMs, opening novel treatment pathways to improve patient survival.

Dysregulation of Human Somatic piRNA Expression in Parkinson's Disease Subtypes and Stages

Piwi interacting RNAs (piRNAs) are small non-coding single-stranded RNA species 20–31 nucleotides in size generated from distinct loci. In germline tissues, piRNAs are amplified via a “ping-pong cycle” to produce secondary piRNAs, which act in transposon silencing. In contrast, the role of somatic-derived piRNAs remains obscure. Here, we investigated the identity and distribution of piRNAs in human somatic tissues to determine their function and potential role in Parkinson’s disease (PD). Human datasets were curated from the Gene Expression Omnibus (GEO) database and a workflow was developed to identify piRNAs, which revealed 902 somatic piRNAs of which 527 were expressed in the brain. These were mainly derived from chromosomes 1, 11, and 19 compared to the germline tissues, which were from 15 and 19. Approximately 20% of somatic piRNAs mapped to transposon 3′ untranslated regions (UTRs), but a large proportion were sensed to the transcript in contrast to germline piRNAs. Gene set enrichment analysis suggested that somatic piRNAs function in neurodegenerative disease. piRNAs undergo dysregulation in different PD subtypes (PD and Parkinson’s disease dementia (PDD)) and stages (premotor and motor). piR-has-92056, piR-hsa-150797, piR-hsa-347751, piR-hsa-1909905, piR-hsa-2476630, and piR-hsa-2834636 from blood small extracellular vesicles were identified as novel biomarkers for PD diagnosis using a sparse partial least square discriminant analysis (sPLS-DA) (accuracy: 92%, AUC = 0.89). This study highlights a role for piRNAs in PD and provides tools for novel biomarker development.

Single-Cell Transcriptomics Reveals the Expression of Aging- and Senescence-Associated Genes in Distinct Cancer Cell Populations

The human aging process is associated with molecular changes and cellular degeneration, resulting in a significant increase in cancer incidence with age. Despite their potential correlation, the relationship between cancer- and ageing-related transcriptional changes is largely unknown. In this study, we aimed to analyze aging-associated transcriptional patterns in publicly available bulk mRNA-seq and single-cell RNA-seq (scRNA-seq) datasets for chronic myelogenous leukemia (CML), colorectal cancer (CRC), hepatocellular carcinoma (HCC), lung cancer (LC), and pancreatic ductal adenocarcinoma (PDAC). Indeed, we detected that various aging/senescence-induced genes (ASIGs) were upregulated in malignant diseases compared to healthy control samples. To elucidate the importance of ASIGs during cell development, pseudotime analyses were performed, which revealed a late enrichment of distinct cancer-specific ASIG signatures. Notably, we were able to demonstrate that all cancer entities analyzed in this study comprised cell populations expressing ASIGs. While only minor correlations were detected between ASIGs and transcriptome-wide changes in PDAC, a high proportion of ASIGs was induced in CML, CRC, HCC, and LC samples. These unique cellular subpopulations could serve as a basis for future studies on the role of aging and senescence in human malignancies.

Tumor-Infiltrating B- and T-Cell Repertoire in Pancreatic Cancer Associated With Host and Tumor Features

Background

Infiltrating B and T cells have been observed in several tumor tissues, including pancreatic ductal adenocarcinoma (PDAC). The majority known PDAC risk factors point to a chronic inflammatory process leading to different forms of immunological infiltration. Understanding pancreatic tumor infiltration may lead to improved knowledge of this devastating disease.

Methods

We extracted the immunoglobulins (IGs) and T cell receptors (TCRs) from RNA-sequencing of 144 PDAC from TCGA and 180 pancreatic normal tissue from GTEx. We used Shannon entropy to find differences in IG/TCR diversity. We performed a clonotype analysis considering the IG clone definition (same V and J segments, same CDR3 length, and 90% nucleotide identity between CDR3s) to study differences among the tumor samples. Finally, we performed an association analysis to find host and tumor factors associated with the IG/TCR.

Results

PDAC presented a richer and more diverse IG and TCR infiltration than normal pancreatic tissue. A higher IG infiltration was present in heavy smokers and females and it was associated with better overall survival. In addition, specific IG clonotypes classified samples with better prognosis explaining 24% of the prognosis phenotypic variance. On the other hand, a larger TCR infiltration was present in patients with previous history of diabetes and was associated with lower nonantigen load.

Conclusions

Our findings support PDAC subtyping according to its immune repertoire landscape with a potential impact on the understanding of the inflammatory basis of PDAC risk factors as well as the design of treatment options and prognosis monitoring.

Integrated Analysis of Microarray and RNA-Seq Data for the Identification of Hub Genes and Networks Involved in the Pancreatic Cancer

Pancreatic cancer (PaCa) is the seventh most fatal malignancy, with more than 90% mortality rate within the first year of diagnosis. Its treatment can be improved the identification of specific therapeutic targets and their relevant pathways. Therefore, the objective of this study is to identify cancer specific biomarkers, therapeutic targets, and their associated pathways involved in the PaCa progression. RNA-seq and microarray datasets were obtained from public repositories such as the European Bioinformatics Institute (EBI) and Gene Expression Omnibus (GEO) databases. Differential gene expression (DE) analysis of data was performed to identify significant differentially expressed genes (DEGs) in PaCa cells in comparison to the normal cells. Gene co-expression network analysis was performed to identify the modules co-expressed genes, which are strongly associated with PaCa and as well as the identification of hub genes in the modules. The key underlaying pathways were obtained from the enrichment analysis of hub genes and studied in the context of PaCa progression. The significant pathways, hub genes, and their expression profile were validated against The Cancer Genome Atlas (TCGA) data, and key biomarkers and therapeutic targets with hub genes were determined. Important hub genes identified included ITGA1, ITGA2, ITGB1, ITGB3, MET, LAMB1, VEGFA, PTK2, and TGFβ1. Enrichment analysis characterizes the involvement of hub genes in multiple pathways. Important ones that are determined are ECM–receptor interaction and focal adhesion pathways. The interaction of overexpressed surface proteins of these pathways with extracellular molecules initiates multiple signaling cascades including stress fiber and lamellipodia formation, PI3K-Akt, MAPK, JAK/STAT, and Wnt signaling pathways. Identified biomarkers may have a strong influence on the PaCa early stage development and progression. Further, analysis of these pathways and hub genes can help in the identification of putative therapeutic targets and development of effective therapies for PaCa.

DLX6-AS1 accelerates cell proliferation through regulating miR-497-5p/SNCG pathway in prostate cancer

Prostate cancer (PCa) has become the second leading cause of cancer-related mortality in males worldwide. Although the long noncoding RNA DLX6-AS1 has been recognized to be an oncogene in multiple cancers, the biological function and regulatory mechanism of DLX6-AS1 in prostate cancer are still obscure. In the present study, we observed that DLX6-AS1 was significantly upregulated in PCa tissues and cells. Knockdown of DLX6-AS1 inhibited PCa progression by suppressing cell proliferation and accelerating cell apoptosis. Molecular mechanism exploration indicated that DLX6-AS1 acted as a sponge for miR-497-5p and synuclein gamma (SNCG) was a downstream target gene of miR-497-5p. In addition, there was a negative correlation between DLX6-AS1 and miR-497-5p in PCa tissues. Rescue assays showed that SNCG overexpression could partially recover DLX6-AS1 knockdown-mediated inhibition of progression in PCa. Furthermore, xenograft tumor model was established to determine the role of DLX6-AS1 in PCa tumor growth and the results suggested that DLX6-AS1 could facilitate tumor growth by regulating SNCG in vivo. In conclusion, our study investigated the biological function and underlying mechanism of DLX6-AS1 in PCa and validated that DLX6-AS1 functioned as an oncogene through miR-497-5p/SNCG axis.

Accurate and efficient detection of gene fusions from RNA sequencing data

The identification of gene fusions from RNA sequencing data is a routine task in cancer research and precision oncology. However, despite the availability of many computational tools, fusion detection remains challenging. Existing methods suffer from poor prediction accuracy and are computationally demanding. We developed Arriba, a novel fusion detection algorithm with high sensitivity and short runtime. When applied to a large collection of published pancreatic cancer samples (n = 803), Arriba identified a variety of driver fusions, many of which affected druggable proteins, including ALK, BRAF, FGFR2, NRG1, NTRK1, NTRK3, RET, and ROS1. The fusions were significantly associated with KRAS wild-type tumors and involved proteins stimulating the MAPK signaling pathway, suggesting that they substitute for activating mutations in KRAS In addition, we confirmed the transforming potential of two novel fusions, RRBP1-RAF1 and RASGRP1-ATP1A1, in cellular assays. These results show Arriba's utility in both basic cancer research and clinical translation.

The miR-1224-5p/ELF3 Axis Regulates Malignant Behaviors of Pancreatic Cancer via PI3K/AKT/Notch Signaling Pathways

Purpose

Aberrant expression of microRNAs contributes to the progression of pancreatic cancer by targeting downstream genes. A novel regulatory axis, miR-1224-5p/ELF3, was identified by bioinformatic analysis and experimental verification. Studies of the underlying molecular mechanisms behind this axis lead to a better understanding of the development of pancreatic cancer.

Materials and Methods

The differential expression of miR-1224-5p and ELF3 was verified based on Gene Expression Omnibus (GEO) datasets and clinical samples. The relationship between miR-1224-5p and ELF3 was demonstrated by luciferase assay and Western blot. The related signaling pathways of the miR-1224-5p/ELF3 axis in pancreatic cancer were investigated by gene set enrichment analysis (GSEA) and verified by Western blot. An analysis between ELF3 expression and immune infiltration was performed. Cellular and animal experiments were utilized to explore the effects of miR-1224-5p and ELF3 in pancreatic cancer.

Results

Suppressed expression of miR-1224-5p in pancreatic tumor tissues and cancer cells was identified first. Furthermore, miR-1224-5p is correlated with clinicopathological features, and decreased expression of miR-1224-5p indicates poor prognosis. miR-1224-5p serves as a tumor suppressor and inhibits malignant behaviors of pancreatic cancer based on in vivo and in vitro assays. The putative target gene ELF3 was predicted by bioinformatic analysis and confirmed by dual-luciferase reporter assay. Overexpression of ELF3 can improve the malignant behaviors of pancreatic cancer and demonstrates poor prognosis and advanced clinical stage. The inhibitory role of miR-1224-5p in pancreatic cancer is manifested by its direct targeting of ELF3. A negative correlation between ELF3 expression and immune cell infiltration was identified, suggesting an immunosuppressive state resulting from ELF3 overexpression. The PI3K/AKT/Notch signaling pathways and epithelial-to-mesenchymal transition (EMT) are important underlying mechanisms.

Conclusion

The miR-1224-5p/ELF3 axis may serve as a new diagnostic, therapeutic, and prognostic biomarker in pancreatic cancer. The related PI3K/AKT/Notch/EMT signaling pathways greatly promote the elucidation of the progression of pancreatic cancer.

Two-tier combinatorial structure to integrate various gene co-expression networks of prostate cancer

Advances in DNA sequencing technologies enable researchers to integrate various biological datasets in order to reveal hidden relations at the molecular level. In this study, we present a two-tiered combinatorial structure (TTCS) to integrate gene co-expression networks (GCNs) that are inferred from microarray gene expression, RNA-Seq and miRNA-target gene data. In the initial phase of TTCS, we derive GCNs by using gene network inference (GNI) algorithms for each dataset. In the first and second integration phases, we use straightforward methods: intersection, union and simple majority voting to combine GCNs. We use overlap, topological and biological analyses in performance evaluation and investigate the integration effects of GCNs separately for all phases. Our results prove that the first integration phase has limited contribution on performance. However, combining the biological datasets in the second phase significantly enhances the overlap and topological performance analyses.