Integrative Analysis of MicroRNA and Gene Interactions for Revealing Candidate Signatures in Prostate Cancer

The receptor tyrosine kinase IGF1R and its associated GPCRs are co-regulated by the noncoding RNA NEAT1 in Alzheimer's disease

The study is based on the complexity of Insulin like growth factor receptor (IGF1R) signaling and its regulation by noncoding RNAs (ncRNAs). IGF1R signaling is an important cascade in Alzheimer's disease (AD); however, its regulation and roles are poorly understood. Due to the presence of β-arrestin and GPCR Receptor Kinase binding sites, this protein has been termed a 'functional hybrid', as it can take part in both kinase and GPCR signaling pathways, further adding to its complexity. The objective of this study is to understand the underlying ncRNA regulation controlling IGF1R and GPCRs in AD to find commonalities in the network. We found through data mining that 45 GPCRs were reportedly deregulated in AD and built clusters based on GO/KEGG pathways to show shared functionality with IGF1R. Eight miRs were further discovered that could coregulate IGF1R and GPCRs. We validated their expression in an AD cell model and probed for common lncRNAs downstream that could regulate these miRs. Seven such candidates were identified and further validated. A combined network comprising IGF1R with nine GPCRs, eight miRs, and seven lncRNAs was created to visualize the interconnectivity within pathways. Betweenness centrality analysis showed a cluster of NEAT1, hsa-miR-15a-5p, hsa-miR-16-5p, and IGF1R to be crucial form a competitive endogenous RNA-based (ceRNA) tetrad that could relay information within the network, which was further validated by cell-based studies. NEAT1 emerged as a master regulator that could alter the levels of IGF1R and associated GPCRs. This combined bioinformatics and experimental study for the first time explored the regulation of IGF1R through ncRNAs from the perspective of neurodegeneration.

ORC6 acts as an effective prognostic predictor for non‑small cell lung cancer and is closely associated with tumor progression

Origin recognition complexes (ORCs) are vital in the control of DNA replication and the progression of the cell cycle, however the precise function and mechanism of ORC6 in non-small cell lung cancer (NSCLC) is still not well understood. The present study used bioinformatics methods to assess the predictive significance of ORC6 expression in NSCLC. Moreover, the expression of ORC6 was further evaluated using reverse transcription-quantitative PCR and western blotting, and its functional significance in lung cancer was assessed via knockdown experiments using small interfering RNA. A significant association was demonstrated between the expression of ORC6 and the clinical features of NSCLC. In particular, elevated levels of ORC6 were significantly strongly correlated with an unfavorable prognosis. Multivariate analysis demonstrated that increased ORC6 expression independently contributed to the risk of overall survival (HR 1.304; P=0.015) in individuals diagnosed with NSCLC. Analysis of Kaplan-Meier plots demonstrated that ORC6 expression served as a valuable indicator for diagnosing and predicting the prognosis of NSCLC. Moreover, in vitro studies demonstrated that modified ORC6 expression had a significant impact on the proliferation, migration and metastasis of NSCLC cells. NSCLC cell lines (H1299 and mH1650) exhibited markedly higher ORC6 expression than normal lung cell lines. The results of the present study indicated a strong association between the expression of ORC6 and the clinicopathological characteristics of NSCLC, which suggested its potential as a reliable biomarker for predicting NSCLC. Furthermore, ORC6 may have important therapeutic implications in the management of NSCLC.

Prognostic microRNAs as biomarkers for prostate cancer

OBJECTIVE

Prostate cancer is the second largest cancer, most commonly diagnosed in men. Several studies reveal that miRNAs (microRNAs) are involved in various stages of prostate cancer. miRNAs are a family of small non-coding RNA species that have been implicated in the post-transcriptional regulation of gene expression. The present in silico study aims at identifying miRNA biomarkers that are significantly associated with the regulation of genes involved in prostate cancer.

METHODS

Dataset of miRNA and mRNA of prostate adenocarcinoma patients and controls was downloaded from The Cancer Genome Atlas (TCGA), and differential gene expression analysis was carried out. ROC and Kaplan-Meier survival analyses were performed on differentially expressed miRNAs. Pathway analysis was carried out for significant miRNAs, and protein-protein interaction of involved genes and miRNAs was examined.

RESULTS

A total of 185 miRNAs were differentially expressed between the patients and the control. ROC and Kaplan-Meier survival analysis showed that the two miRNAs hsa-mir-133b and hsa-mir-17-5p were found to be significantly associated with prostate cancer prognosis. HAS2 and EPHA10 gene targets of identified miRNA were also differentially expressed. A protein-protein interaction (PPI) network was constructed, and the HAS2 gene was found to be interacting with the epidermal growth factor receptor (EGFR).

CONCLUSION

This study highlights the potential of hsa-mir-133b and hsa-mir-17-5p miRNAs as biomarkers for the prognosis of prostate cancer. However, further experimental studies are required to validate this finding.

WSB1 Involvement in Prostate Cancer Progression

Prostate cancer (PC) is polygenic disease involving many genes, and more importantly a host of gene-gene interactions, including transcriptional factors. The WSB1 gene is a transcriptional target of numerous oncoproteins, and its dysregulation can contribute to tumor progression by abnormal activation of targeted oncogenes. Using data from the Cancer Genome Atlas, we tested the possible involvement of WSB1 in PC progression. A multi-dimensional scaling (MDS) model was applied to clarify the association of WSB1 expression with other key genes, such as c-myc, ERG, Enhancer of Zeste 1 and 2 (EHZ1 and EZH2), WNT10a, and WNT 10b. An increased WSB1 expression was associated with higher PC grades and with a worse prognosis. It was also positively related to EZH1, EZH2, WNT10a, and WNT10b. Moreover, MDS showed the central role of WSB1 in influencing the other target genes by its central location on the map. Our study is the first to show a link between WSB1 expression and other genes involved in PC progression, suggesting a novel role for WSB1 in PC progression. This network between WSB1 and EZH2 through WNT/β-catenin may have an important role in PC progression, as suggested by the association between high WSB1 expression and unfavorable prognosis in our analysis.

CDC20 Is Regulated by the Histone Methyltransferase, KMT5A, in Castration-Resistant Prostate Cancer

The methyltransferase KMT5A has been proposed as an oncogene in prostate cancer and therefore represents a putative therapeutic target. To confirm this hypothesis, we have performed a microarray study on a prostate cancer cell line model of androgen independence following KMT5A knockdown in the presence of the transcriptionally active androgen receptor (AR) to understand which genes and cellular processes are regulated by KMT5A in the presence of an active AR. We observed that 301 genes were down-regulated whilst 408 were up-regulated when KMT5A expression was reduced. KEGG pathway and gene ontology analysis revealed that apoptosis and DNA damage signalling were up-regulated in response to KMT5A knockdown whilst protein folding and RNA splicing were down-regulated. Under these conditions, the top non-AR regulated gene was found to be CDC20, a key regulator of the spindle assembly checkpoint with an oncogenic role in several cancer types. Further investigation revealed that KMT5A regulates CDC20 in a methyltransferase-dependent manner to modulate histone H4K20 methylation within its promoter region and indirectly via the p53 signalling pathway. A positive correlation between KMT5A and CDC20 expression was also observed in clinical prostate cancer samples, further supporting this association. Therefore, we conclude that KMT5A is a valid therapeutic target for the treatment of prostate cancer and CDC20 could potentially be utilised as a biomarker for effective therapeutic targeting.

Construction of the novel immune risk scoring system related to CD8+ T cells in uterine corpus endometrial carcinoma

BACKGROUND

Uterine corpus endometrial carcinoma (UCEC) is a gynecological malignant tumor with high incidence and poor prognosis. Although immunotherapy has brought significant survival benefits to advanced UCEC patients, traditional evaluation indicators cannot accurately identify all potential beneficiaries of immunotherapy. Consequently, it is necessary to construct a new scoring system to predict patient prognosis and responsiveness of immunotherapy.

METHODS

CIBERSORT combined with weighted gene co-expression network analysis (WGCNA), non-negative matrix factorization (NMF), and random forest algorithms to screen the module associated with CD8⁺ T cells, and key genes related to prognosis were selected out by univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses to develop the novel immune risk score (NIRS). Kaplan-Meier (K-M) analysis was used to compare the difference of survival between high- and low- NIRS groups. We  also explored the correlations between NIRS, immune infiltration and immunotherapy, and three external validation sets were used to verify the predictive performance of NIRS. Furthermore, clinical subgroup analysis, mutation analysis, differential expression of immune checkpoints, and drug sensitivity analysis were performed to generate individualized treatments for patients with different risk scores. Finally, gene set variation analysis (GSVA) was conducted to explore the biological functions of NIRS, and qRT-PCR was applied to verify the differential expressions of three trait genes at cellular and tissue levels.

RESULTS

Among the modules clustered by WGCNA, the magenta module was most positively associated with CD8⁺ T cells. Three genes (CTSW, CD3D and CD48) were selected to construct NIRS after multiple screening procedures. NIRS was confirmed as an independent prognostic factor of UCEC, and patients with high NIRS had significantly worse prognosis compared to those with low NIRS. The high NIRS group showed lower levels of infiltrated immune cells, gene mutations, and expression of multiple immune checkpoints, indicating reduced sensitivity to immunotherapy. Three module genes were identified as protective factors positively correlated with the level of CD8⁺ T cells.

CONCLUSIONS

In this study, we constructed NIRS as a novel predictive signature of UCEC. NIRS not only differentiates patients with distinct prognoses and immune responsiveness, but also guides their therapeutic regimens.

ORC6, a novel prognostic biomarker, correlates with T regulatory cell infiltration in prostate adenocarcinoma: a pan-cancer analysis

BACKGROUND

The origin recognition complex (ORC), a six-subunit DNA-binding complex, participates in DNA replication in cancer cells. Specifically in prostate cancers, ORC participates the androgen receptor (AR) regulated genomic amplification and tumor proliferation throughout the entire cell cycle. Of note, ORC6, the smallest subunit of ORC, has been reported to be dysregulated in some types of cancers (including prostate cancer), however, its prognostic and immunological significances remain yet to be elucidated.

METHODS

In the current study, we comprehensively investigated the potential prognostic and immunological role of ORC6 in 33 human tumors using multiple databases, such as TCGA, Genotype-Tissue Expression, CCLE, UCSC Xena, cBioPortal, Human Protein Atlas, GeneCards, STRING, MSigDB, TISIDB, and TIMER2 databases.

RESULTS

ORC6 expression was significantly upregulated in 29 types of cancers compared to the corresponding normal adjacent tissues. ORC6 overexpression correlated with higher stage and worse prognostic outcomes in most cancer types analyzed. Additionally, ORC6 was involved in the cell cycle pathway, DNA replication, and mismatch repair pathways in most tumor types. A negative correlation was observed between the tumor endothelial cell infiltration and ORC6 expression in almost all tumors, whereas the immune infiltration of T regulatory cell was noted to be statistically positively correlated with the expression of ORC6 in prostate cancer tissues. Furthermore, in most tumor types, immunosuppression-related genes, especially TGFBR1 and PD-L1 (CD274), exhibited a specific correlation with the expression of ORC6.

CONCLUSIONS

This comprehensive pan-cancer analysis revealed that ORC6 expression serves as a prognostic biomarker and that ORC6 is involved in the regulation of various biological pathways, the tumor microenvironment, and the immunosuppression status in several human cancers, suggesting its potential diagnostic, prognostic, and therapeutic value in pan-cancer, especially in prostate adenocarcinoma.

Transcriptome-Wide Analysis of microRNA-mRNA Correlations in Tissue Identifies microRNA Targeting Determinants

MicroRNAs are small RNAs that regulate gene expression through complementary base pairing with their target mRNAs. A substantial understanding of microRNA target recognition and repression mechanisms has been reached using diverse empirical and bioinformatic approaches, primarily in vitro biochemical or cell culture perturbation settings. We sought to determine if rules of microRNA target efficacy could be inferred from extensive gene expression data of human tissues. A transcriptome-wide assessment of all the microRNA-mRNA canonical interactions' efficacy was performed using a normalized Spearman correlation (Z-score) between the abundance of the transcripts in the PRAD-TCGA dataset tissues (RNA-seq mRNAs and small RNA-seq for microRNAs, 546 samples). Using the Z-score of correlation as a surrogate marker of microRNA target efficacy, we confirmed hallmarks of microRNAs, such as repression of their targets, the hierarchy of preference for gene regions (3'UTR > CDS > 5'UTR), and seed length (6 mer < 7 mer < 8 mer), as well as the contribution of the 3'-supplementary pairing at nucleotides 13-16 of the microRNA. Interactions mediated by 6 mer + supplementary showed similar inferred repression as 7 mer sites, suggesting that the 6 mer + supplementary sites may be relevant in vivo. However, aggregated 7 mer-A1 seeds appear more repressive than 7 mer-m8 seeds, while similar when pairing possibilities at the 3'-supplementary sites. We then examined the 3'-supplementary pairing using 39 microRNAs with Z-score-inferred repressive 3'-supplementary interactions. The approach was sensitive to the offset of the bridge between seed and 3'-supplementary pairing sites, and the pattern of offset-associated repression found supports previous findings. The 39 microRNAs with effective repressive 3'supplementary sites show low GC content at positions 13-16. Our study suggests that the transcriptome-wide analysis of microRNA-mRNA correlations may uncover hints of microRNA targeting determinants. Finally, we provide a bioinformatic tool to identify microRNA-mRNA candidate interactions based on the sequence complementarity of the seed and 3'-supplementary regions.

Systemic analysis of the DNA replication regulator origin recognition complex in lung adenocarcinomas identifies prognostic and expression significance

BACKGROUND

DNA replication alteration is a hallmark of patients with lung adenocarcinoma (LUAD) and is frequently observed in LUAD progression. Origin recognition complex (ORC) 1, ORC2, ORC3, ORC4, ORC5, and ORC6 form a replication-initiator complex to mediate DNA replication, which plays a key role in carcinogenesis, while their roles in LUAD remain poorly understood.

METHODS

The mRNA and protein expression of ORCs was confirmed by the GEPIA, HPA, CPTAC, and TCGA databases. The protein-protein interaction network was analyzed by the GeneMANIA database. Functional enrichment was confirmed by the Metascape database. The effects of ORCs on immune infiltration were validated by the TIMER database. The prognostic significance of ORCs in LUAD was confirmed by the KM-plot and GENT2 databases. DNA alteration and protein structure were determined in the cBioProtal and PDB databases. Moreover, the protein expression and prognostic value of ORCs were confirmed in our LUAD data sets by immunohistochemistry (IHC) staining.

RESULTS

ORC mRNA and protein were significantly increased in patients with LUAD compared with corresponding normal tissue samples. The results of IHC staining analysis were similar result to those of the above bioinformatics analysis. Furthermore, ORC1 and ORC6 had significant prognostic values for LUAD patients. Furthermore, the ORC cooperatively promoted LUAD development by driving DNA replication, cellular senescence, and metabolic processes.

CONCLUSION

The ORC, especially ORC1/6, has important prognostic and expression significance for LUAD patients.

An overview of prostate cancer (PCa) diagnosis: Potential role of miRNAs

Prostate cancer is the second most frequently diagnosed cancer among men worldwide, with the estimated sixth leading cause of cancer death. Despite major advancements in clinical biology and imaging, digital rectal examination (DRE), prostate-specific antigen (PSA), and biopsies indication remain the keystone for screening. Several kits are used to detect genomic changes and non-coding RNAs in the sample. However, its indication remains controversial for screening purposes. There is an urged need for non-invasive biomarkers to implement precision medicine. Recent research shows that miRNAs have an important role in the diagnostic, prognostic, and therapeutic agents as non-invasive biomarkers. Though prostate cancer data remains controversial in other cancer types, such as breast cancer, miR-21 expression is upregulated. Here, we reported a prolonged revision of miRNAs as prostate cancer prognostic, diagnostic, and predictive tools, including data on androgen receptor (AR) signaling, epithelial-mesenchymal transition (EMT) process, and cancer stem cells (CSCs) regulation. The combined utilization of miRNAs with other tests will help patients and clinicians to select the most appropriate personalized treatment and to avoid overdiagnosis and unnecessary biopsies. Future clinical applications of our reported novel miRNAs have a substantial role in the primary diagnosis of prostate cancer to help treatment decisions.

The Potential of MicroRNAs as Non-Invasive Prostate Cancer Biomarkers: A Systematic Literature Review Based on a Machine Learning Approach

Simple Summary

Prostate cancer (PCa) is the most common cancer in men worldwide. Screening and diagnosis are based on prostate-specific antigen (PSA) blood testing and digital rectal examination. Nevertheless, these methods are not specific and have a high risk of mistaken results. This has led to overtreatment and unnecessary radical therapy; thus, better prognostic tools are urgently needed. In this view, microRNAs (miRs) appear as potential non-invasive biomarkers for PCa diagnosis, prognosis, and therapy. As the scientific literature available in this field is huge and very often controversial, we identified and discussed three topics that characterize the investigated research area by combining the big data from the literature together with a novel machine learning approach. By analyzing the papers clustered into these topics we have offered a deeper understanding of the current research, which helps to contribute to the advancement of this research field.

Abstract

Background: Prostate cancer (PCa) is the second leading cause of cancer-related deaths in men. Although the prostate-specific antigen (PSA) test is used in clinical practice for screening and/or early detection of PCa, it is not specific, thus resulting in high false-positive rates. MicroRNAs (miRs) provide an opportunity as biomarkers for diagnosis, prognosis, and recurrence of PCa. Because the size of the literature on it is increasing and often controversial, this study aims to consolidate the state-of-art of relevant published research. Methods: A Systematic Literature Review (SLR) approach was applied to analyze a set of 213 scientific publications through a text mining method that makes use of the Latent Dirichlet Allocation (LDA) algorithm. Results and Conclusions: The result of this activity, performed through the MySLR digital platform, allowed us to identify a set of three relevant topics characterizing the investigated research area. We analyzed and discussed all the papers clustered into them. We highlighted that several miRs are associated with PCa progression, and that their detection in patients’ urine seems to be the more reliable and promising non-invasive tool for PCa diagnosis. Finally, we proposed some future research directions to help future scientists advance the field further.

SMARCE1 promotes neuroblastoma tumorigenesis through assisting MYCN-mediated transcriptional activation

SMARCE1 gene, encoding a core subunit of SWI/SNF chromatin remodeling complex, is situated on chromosome 17q21-ter region that is frequently gained in neuroblastoma. However, its role in the tumorigenesis remains unknown. Here, we showed that high expression of SMARCE1 was associated with poor prognosis of patients with neuroblastoma, especially those with MYCN amplification. Knockdown of SMARCE1 reduced proliferation, colony formation, and tumorigenicity of neuroblastoma cells. Mechanistically, SMARCE1 directly interacted with MYCN, which was necessary for MYCN-mediated transcriptional activation of downstream target genes including PLK1, ODC1, and E2F2. Overexpression of PLK1, ODC1 or E2F2 significantly reversed the inhibiting effect of SMARCE1 knockdown on the proliferation, colony formation, and tumorigenicity of MYCN-amplified neuroblastoma cells. Moreover, we revealed that MYCN directly regulated SMARCE1 transcription through binding to a non-canonical E-box of SMARCE1 promoter, thus enhancing SMARCE1-MYCN cooperativity. These findings establish SMARCE1 is a critical oncogenic factor in neuroblastoma and provide a new potential target for treatment of neuroblastoma with 17q21-ter gain and MYCN amplification.

Identification of target and pathway of aspirin combined with Lipitor treatment in prostate cancer through integrated bioinformatics analysis

PURPOSE

Our previous studies have confirmed that aspirin combined with Lipitor inhibited the development of prostate cancer (PCa), but the mechanisms need to be comprehensively expounded. The study aims to screen out the hub genes of combination therapy and to explore their association with the pathogenesis and prognosis of PCa.

METHODS

Gene expressions were quantified by RNA sequencing (RNA-seq). Altered biological function, pathways of differentially expressed genes (DEGs), protein-protein interaction network, the filtering of hub genes, gene co-expression and the pathogenesis and prognosis were revealed by bioinformatics analysis. The correlation between hub gene expression and patient survival was validated by Kaplan-Meier. The effects of silent DNA replication and sister chromatid cohesion 1 (siDSCC1) combined with Lipitor and aspirin on DSCC1 expression, viability, invasion and migration of PCa cells were detected by qRT-PCR, Wound healing and transwell assays.

RESULTS

157 overlapped DEGs involved in FoxO, PI3K-Akt and p53 signaling pathways were identified. Ten hub genes (NEIL3, CDC7, DSCC1, CDC25C, PRIM1, MCM10, FBXO5, DTL, SERPINE1, EXO1) were verified to be correlated with the pathology and prognosis of PCa. DSCC1 silencing not only inhibited the viability, migration and invasion of PCa cells, but also strengthened the suppressing effects of Lipitor and aspirin alone or in combination on PCa cells.

CONCLUSION

The enrichment pathways and targets of Lipitor combined with aspirin in PCa are discovered, and DSCC1 silencing can potentiate the effect of Lipitor combined with aspirin in the treatment of PCa.

An Eleven-microRNA Signature Related to Tumor-Associated Macrophages Predicts Prognosis of Breast Cancer

The dysregulation of microRNAs (miRNAs) has been known to play important roles in tumor development and progression. However, the understanding of the involvement of miRNAs in regulating tumor-associated macrophages (TAMs) and how these TAM-related miRNAs (TRMs) modulate cancer progression is still in its infancy. This study aims to explore the prognostic value of TRMs in breast cancer via the construction of a novel TRM signature. Potential TRMs were identified from the literature, and their prognostic value was evaluated using 1063 cases in The Cancer Genome Atlas Breast Cancer database. The TRM signature was further validated in the external Gene Expression Omnibus GSE22220 dataset. Gene sets enrichment analyses were performed to gain insight into the biological functions of this TRM signature. An eleven-TRM signature consisting of mir-21, mir-24-2, mir-125a, mir-221, mir-22, mir-501, mir-365b, mir-660, mir-146a, let-7b and mir-31 was constructed. This signature significantly differentiated the high-risk group from the low-risk in terms of overall survival (OS)/ distant-relapse free survival (DRFS) (p value < 0.001). The prognostic value of the signature was further enhanced by incorporating other independent prognostic factors in a nomogram-based prediction model, yielding the highest AUC of 0.79 (95% CI: 0.72−0.86) at 5-year OS. Enrichment analyses confirmed that the differentially expressed genes were mainly involved in immune-related pathways such as adaptive immune response, humoral immune response and Th1 and Th2 cell differentiation. This eleven-TRM signature has great potential as a prognostic factor for breast cancer patients besides unravelling the dysregulated immune pathways in high-risk breast cancer.

Network Approaches for Charting the Transcriptomic and Epigenetic Landscape of the Developmental Origins of Health and Disease

The early developmental phase is of critical importance for human health and disease later in life. To decipher the molecular mechanisms at play, current biomedical research is increasingly relying on large quantities of diverse omics data. The integration and interpretation of the different datasets pose a critical challenge towards the holistic understanding of the complex biological processes that are involved in early development. In this review, we outline the major transcriptomic and epigenetic processes and the respective datasets that are most relevant for studying the periconceptional period. We cover both basic data processing and analysis steps, as well as more advanced data integration methods. A particular focus is given to network-based methods. Finally, we review the medical applications of such integrative analyses.

Identification of Pathologic and Prognostic Genes in Prostate Cancer Based on Database Mining

Background: Prostate cancer (PCa) is an epithelial malignant tumor that occurs in the urinary system with high incidence and is the second most common cancer among men in the world. Thus, it is important to screen out potential key biomarkers for the pathogenesis and prognosis of PCa. The present study aimed to identify potential biomarkers to reveal the underlying molecular mechanisms.

Methods: Differentially expressed genes (DEGs) between PCa tissues and matched normal tissues from The Cancer Genome Atlas Prostate Adenocarcinoma (TCGA-PRAD) dataset were screened out by R software. Weighted gene co-expression network analysis was performed primarily to identify statistically significant genes for clinical manifestations. Protein–protein interaction (PPI) network analysis and network screening were performed based on the STRING database in conjunction with Cytoscape software. Hub genes were then screened out by Cytoscape in conjunction with stepwise algorithm and multivariate Cox regression analysis to construct a risk model. Gene expression in different clinical manifestations and survival analysis correlated with the expression of hub genes were performed. Moreover, the protein expression of hub genes was validated by the Human Protein Atlas database.

Results: A total of 1,621 DEGs (870 downregulated genes and 751 upregulated genes) were identified from the TCGA-PRAD dataset. Eight prognostic genes [BUB1, KIF2C, CCNA2, CDC20, CCNB2, PBK, RRM2, and CDC45] and four hub genes (BUB1, KIF2C, CDC20, and PBK) potentially correlated with the pathogenesis of PCa were identified. A prognostic model with good predictive power for survival was constructed and was validated by the dataset in GSE21032. The survival analysis demonstrated that the expression of RRM2 was statistically significant to the prognosis of PCa, indicating that RRM2 may potentially play an important role in the PCa progression.

Conclusion: The present study implied that RRM2 was associated with prognosis and could be used as a potential therapeutic target for PCa clinical treatment.

Construction of a Redox-Related Prognostic Model with Predictive Value in Survival and Therapeutic Response for Patients with Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) represents the most common histological subtype of lung cancer. Redox plays a significant role in oncogenesis and antitumor immunity. In this study, we aimed to investigate the prognostic redox-associated genes and construct a redox-based prognostic signature for LUAD.

Materials and Methods

A discovery cohort containing 479 LUAD samples from The Cancer Genome Atlas (TCGA) was analyzed. We identified prognostic redox-associated genes by weighted correlation network analysis (WGCNA) and univariate Cox regression analysis to construct a prognostic model via least absolute shrinkage and selection operator (LASSO)-multivariate Cox regression analyses. The performance of the redox-based model was validated in the TCGA cohort and an independent cohort of 456 samples by Cox regression analyses, log-rank test, and receiver operating characteristic (ROC) curves. Correlations of the model with clinicopathological variables and lymphocyte infiltration were assessed. Gene set enrichment analysis (GSEA) was used to clarify the underlying mechanism of the prognostic model. We constructed a nomogram based on the model and created calibration curves to show the accordance between actual survival and predicted survival of the nomogram.

Results

Stepwise analyses identified 6 prognostic redox-associated genes of LUAD and constructed a prognostic model that performed well in both the discovery and validation cohorts. The model was found to be associated with tumor stage, mutation of TP53 and EGFR, and lymphocyte infiltration. The model was mainly involved in the regulation of the cell cycle, DNA replication and repair, NADH metabolism, and the p53 signaling pathway. Calibration curves showed the high predictive accuracy of the nomogram.

Conclusions

This study explored the role of redox-associated genes in LUAD and constructed a prognostic model of LUAD. The signature was also associated with tumor progression and therapeutic response to immunotherapy. These findings contributed to uncovering the underlying mechanism and discovering novel prognostic predictor of LUAD.

Role of IGF2 in the Study of Development and Evolution of Prostate Cancer

Prostate Cancer (PC) is commonly known as one of the most frequent tumors among males. A significant problem of this tumor is that in early stages most of the cases course as indolent forms, so an active surveillance will anticipate the appearance of aggressive stages. One of the main strategies in medical and biomedical research is to find non-invasive biomarkers for improving monitoring and performing a more precise follow-up of diseases like PC. Here we report the relevant role of IGF2 and miR-93-5p as non-invasive biomarker for PC. This event could improve current medical strategies in PC.

A regulatory miRNA-mRNA network is associated with transplantation response in acute kidney injury

Background

Acute kidney injury (AKI) is a life-threatening complication characterized by rapid decline in renal function, which frequently occurs after transplantation surgery. However, the molecular mechanism underlying the development of post-transplant (post-Tx) AKI still remains unknown. An increasing number of studies have demonstrated that certain microRNAs (miRNAs) exert crucial functions in AKI. The present study sought to elucidate the molecular mechanisms in post-Tx AKI by constructing a regulatory miRNA–mRNA network.

Results

Based on two datasets (GSE53771 and GSE53769), three key modules, which contained 55 mRNAs, 76 mRNAs, and 151 miRNAs, were identified by performing weighted gene co-expression network analysis (WGCNA). The miRDIP v4.1 was applied to predict the interactions of key module mRNAs and miRNAs, and the miRNA–mRNA pairs with confidence of more than 0.2 were selected to construct a regulatory miRNA–mRNA network by Cytoscape. The miRNA–mRNA network consisted of 82 nodes (48 mRNAs and 34 miRNAs) and 125 edges. Two miRNAs (miR-203a-3p and miR-205-5p) and ERBB4 with higher node degrees compared with other nodes might play a central role in post-Tx AKI. Additionally, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis indicated that this network was mainly involved in kidney-/renal-related functions and PI3K–Akt/HIF-1/Ras/MAPK signaling pathways.

Conclusion

We constructed a regulatory miRNA–mRNA network to provide novel insights into post-Tx AKI development, which might help discover new biomarkers or therapeutic drugs for enhancing the ability for early prediction and intervention and decreasing mortality rate of AKI after transplantation.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-021-00363-y.

Regulation of the Effect of Physical Activity Through MicroRNAs in Breast Cancer

Physical activity and exercise can induce beneficial molecular and biological regulations that have been associated with an incidence of various diseases, including breast cancer. Recent studies demonstrated that the potential links between physical activity-induced circulating microRNAs (miRNAs) and cancer risk and progression. Here, we investigated whether altered miRNAs by exercise could influence breast cancer progression. After primary searching in PubMed and reviewing the full-text papers, candidate miRNAs altered by exercise in breast cancer were identified. Analysis of expression profiles and clinical outcomes of altered miRNAs using The Cancer Genome Atlas datasets showed altered miRNAs expressions were significantly associated with the patient's prognosis, whereas prognostic values of each miRNA varied in different stages and subtypes. In addition, altered miRNAs profiles regulated various target genes and key signaling pathways in tumorigenesis, including pathways in cancer and the PI3K-Akt signaling pathway; however, miRNAs regulated the expression of target genes differently according to tumor stages and subtypes. These results indicate that circulating miRNAs are promising noninvasive stable biomarkers for early detection, diagnosis, prognosis, and monitoring the response to clinical therapies of breast cancer. Moreover, stages and subtype-stratified approaches for breast cancer progression would be needed to evaluate the prognostic value of miRNAs for biomarkers and therapeutic targets.

Expression and Prognostic Role of E2F2 in Hepatocellular Carcinoma

Introduction

Hepatocellular carcinoma (HCC) is a common clinical malignancy. Recent studies reported that E2F transcription factor 2 (E2F2) plays a significant role in tumor progression. However, its expression and biological function in HCC are still unclear. Therefore, we explored the relationship between E2F2 expression and tumor progression in HCC.

Methods

In this study, we utilized some online tools to explore the E2F2 expression in pan-carcinoma and HCC. The association of E2F2 expression with the clinical characteristics and prognosis of HCC was further studied. In addition, we explored the co-expressed genes of E2F2 and mined the positively/negatively corrected significant genes and excavated the possible functions. Meanwhile, the hub gene set was constructed based on protein-protein interaction (PPI) network, and the relationship between E2F2 and immunity was discovered.

Results

We observed that the expression level of E2F2 was generally upregulated in HCC. However, E2F2 expression was not significantly different between HCC and normal tissues in regard to the disease stage 4. Furthermore, we also observed the poor prognosis in patients with high E2F2 expression. The co-expressed genes of E2F2 were identified and further detected. Thereafter, we identified the positively/negatively corrected significant genes and constructed the hub gene network of E2F2 based on PPI network. We also found that E2F2 expression was positively correlated with the infiltration levels of CD4+ T, CD8+ T cells, macrophages, neutrophils, and dendritic cells.

Conclusion

Our findings suggested that E2F2 could be a potential prognostic factor for HCC, which could provide a therapeutic target for the molecular treatment of HCC.

[Identification and validation of hub genes in prostate cancer progression based on weighted gene co-expression network analysis]

OBJECTIVE

To identify the key hub genes in prostate cancer metastasis based on weighted gene co-expression network analysis (WGCNA) and verify the identified genes.

METHODS

Whole-genome chip data GSE6919 of prostate cancer study were analyzed using principal component analysis (PCA), and the differentially expressed genes (DEGs) were analyzed using R software. WGCNA was performed to construct a gene co-expression network for screening the key genes. TCGA database was used to explore the expressions of the DEGs and their association with the prognosis. To validate the results, we designed siRNA fragments targeting the metastasis-related gene HNRNPA2B1, and observed its effect on growth, apoptosis, clone formation, migration and invasion of prostate cancer cell lines using MTT assay, flow cytometry, clone formation assay, and Transwell assay.

RESULTS

PCA analysis showed obvious clustering of significant DEGs in metastatic cancer group. The modules obtained by WGCNA analysis in metastasis group involved stem cell differentiation, amino acid metabolism and immune response. Further screening of the genes identified 3 genes related with prostate cancer occurrence (BDH1, PAK4 and EXTL3) and another 3 with prostate cancer metastasis (NKTR, CTBP2 and HNRNPA2B1), which were shown to have differential expressions in TCGA database and were correlated with the patient's overall survival. In the cell experiment, PC3 and LNCap cells transfected with the siRNA fragment targeting HNRNPA2B1 showed obvious growth inhibition with increased cell apoptosis, lowered clone formation ability, and suppressed capacities for migration and invasion.

CONCLUSION

We identified 3 hub genes related with the occurrence (BDH1, PAK4 and EXTL3) and another 3 with metastasis of prostate cancer (NKTR, CTBP2 and HNRNPA2B1) using WGCNA, which provides a new approach for studying the regulatory mechanisms of prostate cancer.

SOCS3 is Related to Cell Proliferation in Neuronal Tissue: An Integrated Analysis of Bioinformatics and Experiments

Glioma is the most common primary malignant tumor that occurs in the central nervous system. Gliomas are subdivided according to a combination of microscopic morphological, molecular, and genetic factors. Glioblastoma (GBM) is the most aggressive malignant tumor; however, efficient therapies or specific target molecules for GBM have not been developed. We accessed RNA-seq and clinical data from The Cancer Genome Atlas, the Chinese Glioma Genome Atlas, and the GSE16011 dataset, and identified differentially expressed genes (DEGs) that were common to both GBM and lower-grade glioma (LGG) in three independent cohorts. The biological functions of common DEGs were examined using NetworkAnalyst. To evaluate the prognostic performance of common DEGs, we performed Kaplan-Meier and Cox regression analyses. We investigated the function of SOCS3 in the central nervous system using three GBM cell lines as well as zebrafish embryos. There were 168 upregulated genes and 50 downregulated genes that were commom to both GBM and LGG. Through survival analyses, we found that SOCS3 was the only prognostic gene in all cohorts. Inhibition of SOCS3 using siRNA decreased the proliferation of GBM cell lines. We also found that the zebrafish ortholog, socs3b, was associated with brain development through the regulation of cell proliferation in neuronal tissue. While additional mechanistic studies are necessary, our results suggest that SOCS3 is an important biomarker for glioma and that SOCS3 is related to the proliferation of neuronal tissue.

A Multimodal Affinity Fusion Network for Predicting the Survival of Breast Cancer Patients

Accurate survival prediction of breast cancer holds significant meaning for improving patient care. Approaches using multiple heterogeneous modalities such as gene expression, copy number alteration, and clinical data have showed significant advantages over those with only one modality for patient survival prediction. However, existing survival prediction methods tend to ignore the structured information between patients and multimodal data. We propose a multimodal data fusion model based on a novel multimodal affinity fusion network (MAFN) for survival prediction of breast cancer by integrating gene expression, copy number alteration, and clinical data. First, a stack-based shallow self-attention network is utilized to guide the amplification of tiny lesion regions on the original data, which locates and enhances the survival-related features. Then, an affinity fusion module is proposed to map the structured information between patients and multimodal data. The module endows the network with a stronger fusion feature representation and discrimination capability. Finally, the fusion feature embedding and a specific feature embedding from a triple modal network are fused to make the classification of long-term survival or short-term survival for each patient. As expected, the evaluation results on comprehensive performance indicate that MAFN achieves better predictive performance than existing methods. Additionally, our method can be extended to the survival prediction of other cancer diseases, providing a new strategy for other diseases prognosis.

Overexpression of miR-20a-5p in Tumor Epithelium Is an Independent Negative Prognostic Indicator in Prostate Cancer-A Multi-Institutional Study

Simple Summary

MicroRNAs (miRs) have critical regulatory roles in cell functions, and are involved in prostate cancer tumorigenesis. miR-20a-5p is a member of the oncogenic miR-17-92 cluster. Overexpressed miR-20a-5p has been shown to increase both cell proliferation and cell migration in cancers. The aim of our cohort study was to evaluate the prognostic role of miR-20a-5p in prostate cancer. We found miR-20a-5p associated with biochemical failure in tumor epithelium and tumor stroma. In the multivariable analysis miR-20a-5p in tumor epithelium was found to be an independent prognostic predictor for biochemical failure. In the functional studies, migration and invasion were significantly increased in miR-20a-5p transfected prostate cancer cell lines. In conclusion, high miR-20a-5p expression in tumor epithelium is a negative independent prognostic factor for biochemical failure in prostate cancer.

Abstract

Objective: assessing the prognostic role of miR-20a-5p, in terms of clinical outcome, in a large multi-institutional cohort study. Methods: Tissue microarrays from 535 patients’ prostatectomy specimens were constructed. In situ hybridization was performed to assess the expression level of miR-20a-5p in different tissue subregions: tumor stroma (TS) and tumor epithelium (TE). In vitro analysis was performed on prostate cancer cell lines. Results: A high miR-20a-5p expression was found negatively in association with biochemical failure in TE, TS and TE + TS (p = 0.001, p = 0.003 and p = 0.001, respectively). Multivariable analysis confirmed that high miR-20a-5p expression in TE independently predicts dismal prognosis for biochemical failure (HR = 1.56, 95% CI: 1.10–2.21, p = 0.014). Both DU145 and PC3 cells exhibited increased migration ability after transient overexpression of miR-20a-5p, as well as significant elevation of invasion in DU145 cells. Conclusion: A high miR-20a-5p expression in tumor epithelium is an independent negative predictor for biochemical prostate cancer recurrence.

G2 and S phase-expressed-1 acts as a putative tumor promoter in cervical cancer by enhancing Wnt/β-catenin signaling via modulation of GSK-3β

G2 and S phase-expressed-1 (GTSE1) is currently identified as a key regulator of carcinogenesis. However, the involvement of GTSE1 in cervical cancer is unclear. The aims of this work were to explore the relationship between GTSE1 and cervical cancer. Our data elucidated high GTSE1 expression in cervical cancer tissue, which predicted a poor prognosis in cervical cancer patients. GTSE1 knockdown had tumor-suppressive effects in cervical cancer cells by inhibiting cell proliferative and invasive abilities. GTSE1 knockdown decreased the level of phosphorylated glycogen synthase kinase-3β (GSK-3β) and active β-catenin, resulted in inactivation of Wnt/β-catenin signaling. Suppression of GSK-3β remarkably abolished the GTSE1-knockdown-induced inhibitory effects on Wnt/β-catenin signaling. Suppression of Wnt/β-catenin signaling abolished the GTSE1-overexpression-induced oncogenic effects. Notably, GTSE1 knockdown impeded the in vivo tumorigenicity of cervical cancer cells. In short, this work demonstrates that GTSE1 is overexpressed in cervical cancer and GTSE1 suppression exerts a tumor-inhibiting role in cervical cancer by down-regulating Wnt/β-catenin signaling. Our work underlines a crucial relevance between GTSE1 and cervical cancer progression and suggests GTSE1 as a promising therapeutic target for cervical cancer.

ORC6, Negatively Regulated by miR-1-3p, Promotes Proliferation, Migration, and Invasion of Hepatocellular Carcinoma Cells

Background

In recent years, microRNA-1-3p (miR-1-3p) has been linked to the progression of multiple cancers, whereas little is known about its role in hepatocellular carcinoma (HCC). Herein, we investigated the function of miR-1-3p in HCC, and its regulatory function on origin recognition complex subunit 6 (ORC6).

Methods

Quantitative real-time polymerase chain reaction (qRT-PCR) was performed for detecting the expression levels of miR-1-3p and ORC6 mRNA in HCC samples and cell lines. ORC6 expression at the protein level was quantified by Western blot. After gain-of-function and loss-of-function models were established, cell counting kit-8 (CCK-8) assays, Transwell assays, flow cytometry, and 5-Ethynyl-2′-deoxyuridine (EdU) assay were performed for examining cell proliferation, migration, invasion, cell cycle, and apoptosis. The targeting relationship between miR-1-3p and ORC6 was confirmed with bioinformatic analysis and dual-luciferase reporter assays.

Results

The expression of miR-1-3p was reduced in HCC samples and cell lines. Overexpression of miR-1-3p suppressed the proliferation, migration, and invasion, and induced cell-cycle arrest and apoptosis of HCC cells, whereas the opposite effects were induced by miR-1-3p inhibition. ORC6 is identified as a novel target of miR-1-3p, the expression of which is negatively correlated with miR-1-3p expression in HCC tissues. ORC6 overexpression facilitated the proliferation, migration, invasion, and cell cycle progression, and reduced apoptosis of HCC cells, whereas the opposite effects were induced by ORC6 knockdown. What is more, ORC6 overexpression counteracted the biological functions of miR-1-3p in HCC cells.

Conclusion

MiR-1-3p targets ORC6 to suppress the proliferation, migration, invasion, and cell cycle progression, and promote apoptosis of HCC cells.

RPL19 Is a Prognostic Biomarker and Promotes Tumor Progression in Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most common malignancies, and the therapeutic outcome remains undesirable due to its recurrence and metastasis. Gene dysregulation plays a pivotal role in the occurrence and progression of cancer, and the molecular mechanisms are largely unknown.

Methods

The differentially expressed genes of HCC screened from the GSE39791 dataset were used to conduct weighted gene co-expression network analysis. The selected hub genes were validated in The Cancer Genome Atlas (TCGA) database and 11 HCC datasets from the Gene Expression Omnibus (GEO) database. Then, a tissue microarray comprising 90 HCC specimens and 90 adjacent normal specimens was used to validate the hub genes. Moreover, the Hallmark, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases were used to identify enriched pathways. Then, we conducted the immune infiltration analysis.

Results

A total of 17 co-expression modules were obtained by weighted gene co-expression network analysis. The green, blue, and purple modules were the most relevant to HCC samples. Four hub genes, RPL19, RPL35A, RPL27A, and RPS12, were identified. Interestingly, we found that all four genes were highly expressed in HCC and that their high expression was related to a poor prognosis by analyzing the TCGA and GEO databases. Furthermore, we investigated RPL19 in HCC tissue microarrays and demonstrated that RPL19 was overexpressed in tumor tissues compared with non-tumor tissues (p = 0.016). Moreover, overexpression of RPL19 predicted a poor prognosis in hepatocellular carcinoma (p < 0.0007). Then, enrichment analysis revealed that cell cycle pathways were significantly enriched, and bile acid metabolism-related pathways were significantly down-regulated when RPL19 was highly expressed. Furthermore, immune infiltration analysis showed that immune response was suppressed.

Conclusion

Our study demonstrates that RPL19 may play an important role in promoting tumor progression and is correlated with a poor prognosis in HCC. RPL19 may serve as a promising biomarker and therapeutic target for the precise diagnosis and treatment of HCC in the future.

Identification and analysis of genes associated with epithelial ovarian cancer by integrated bioinformatics methods

Background

Though considerable efforts have been made to improve the treatment of epithelial ovarian cancer (EOC), the prognosis of patients has remained poor. Identifying differentially expressed genes (DEGs) involved in EOC progression and exploiting them as novel biomarkers or therapeutic targets is of great value.

Methods

Overlapping DEGs were screened out from three independent gene expression omnibus (GEO) datasets and were subjected to Gene ontology (GO) and Kyoto encyclopedia of genes and genomes (KEGG) pathway enrichment analyses. The protein-protein interactions (PPI) network of DEGs was constructed based on the STRING database. The expression of hub genes was validated in GEPIA and GEO. The relationship of hub genes expression with tumor stage and overall survival and progression-free survival of EOC patients was investigated using the cancer genome atlas data.

Results

A total of 306 DEGs were identified, including 265 up-regulated and 41 down-regulated. Through PPI network analysis, the top 20 genes were screened out, among which 4 hub genes, which were not researched in depth so far, were selected after literature retrieval, including CDC45, CDCA5, KIF4A, ESPL1. The four genes were up-regulated in EOC tissues compared with normal tissues, but their expression decreased gradually with the continuous progression of EOC. Survival curves illustrated that patients with a lower level of CDCA5 and ESPL1 had better overall survival and progression-free survival statistically.

Conclusion

Two hub genes, CDCA5 and ESPL1, identified as probably playing tumor-promotive roles, have great potential to be utilized as novel therapeutic targets for EOC treatment.

Identification of MicroRNAs as Viable Aggressiveness Biomarkers for Prostate Cancer

MiRNAs play a relevant role in PC (prostate cancer) by the regulation in the expression of several pathways’ AR (androgen receptor), cellular cycle, apoptosis, MET (mesenchymal epithelium transition), or metastasis. Here, we report the role of several miRNAs’ expression patterns, such as miR-93-5p, miR-23c, miR-210-3p, miR-221-3p, miR-592, miR-141, miR-375, and miR-130b, with relevance in processes like cell proliferation and MET. Using Trizol^® extraction protocol and TaqMan™ specific probes for amplification, we performed miRNAs’ analysis of 159 PC fresh tissues and 60 plasmas from peripheral blood samples. We had clinical data from all samples including PSA, Gleason, TNM, and D’Amico risk. Moreover, a bioinformatic analysis in TCGA (The Cancer Genome Atlas) was included to analyze the effect of the most relevant miRNAs according to aggressiveness in an extensive cohort (n = 531). We found that miR-210-3p, miR-23c, miR-592, and miR-93-5p are the most suitable biomarkers for PC aggressiveness and diagnosis, respectively. In fact, according with our results, miR-93-5p seems the most promising non-invasive biomarker for PC. To sum up, miR-210-3p, miR-23c, miR-592, and miR-93-5p miRNAs are suggested to be potential biomarkers for PC risk stratification that could be included in non-invasive strategies such as liquid biopsy in precision medicine for PC management.

Identification of Key MicroRNAs and Mechanisms in Prostate Cancer Evolution Based on Biomarker Prioritization Model and Carcinogenic Survey

Background: Prostate cancer (PCa) is occurred with increasing incidence and heterogeneous pathogenesis. Although clinical strategies are accumulated for PCa prevention, there is still a lack of sensitive biomarkers for the holistic management in PCa occurrence and progression. Based on systems biology and artificial intelligence, translational informatics provides new perspectives for PCa biomarker prioritization and carcinogenic survey.

Methods: In this study, gene expression and miRNA-mRNA association data were integrated to construct conditional networks specific to PCa occurrence and progression, respectively. Based on network modeling, hub miRNAs with significantly strong single-line regulatory power were topologically identified and those shared by the condition-specific network systems were chosen as candidate biomarkers for computational validation and functional enrichment analysis.

Results: Nine miRNAs, i.e., hsa-miR-1-3p, hsa-miR-125b-5p, hsa-miR-145-5p, hsa-miR-182-5p, hsa-miR-198, hsa-miR-22-3p, hsa-miR-24-3p, hsa-miR-34a-5p, and hsa-miR-499a-5p, were prioritized as key players for PCa management. Most of these miRNAs achieved high AUC values (AUC > 0.70) in differentiating different prostate samples. Among them, seven of the miRNAs have been previously reported as PCa biomarkers, which indicated the performance of the proposed model. The remaining hsa-miR-22-3p and hsa-miR-499a-5p could serve as novel candidates for PCa predicting and monitoring. In particular, key miRNA-mRNA regulations were extracted for pathogenetic understanding. Here hsa-miR-145-5p was selected as the case and hsa-miR-145-5p/NDRG2/AR and hsa-miR-145-5p/KLF5/AR axis were found to be putative mechanisms during PCa evolution. In addition, Wnt signaling, prostate cancer, microRNAs in cancer etc. were significantly enriched by the identified miRNAs-mRNAs, demonstrating the functional role of the identified miRNAs in PCa genesis.

Conclusion: Biomarker miRNAs together with the associated miRNA-mRNA relations were computationally identified and analyzed for PCa management and carcinogenic deciphering. Further experimental and clinical validations using low-throughput techniques and human samples are expected for future translational studies.

Integrative analysis of mRNA and miRNA sequencing data for gliomas of various grades

Background

The purpose of this study was to characterize subtype-specific patterns of mRNA and miRNA expression of gliomas using The Cancer Genome Atlas (TCGA) data to search for genetic determinants that predict prognosis in terms of overall survival and to create interaction networks for grade 2 and 3 (G2 and G3) astrocytomas, oligodendrogliomas and grade 4 (G4) glioblastoma multiforme. Based on open-access TCGA data, 5 groups were formed: astrocytoma G2 (n = 58), astrocytoma G3 (n = 128), oligodendroglioma G2 (n = 102), oligodendroglioma G3 (n = 72) and glioblastoma G4 (n = 564); normal samples of brain tissue were also analysed (n = 15). Data of patient age, sex, survival and expression patterns of mRNA and miRNA were extracted for each sample. After stratification of the data into groups, a differential analysis of expression was carried out, genes and miRNAs that affect overall survival were identified and gene set enrichment analysis (GSEA) and interaction analysis were performed.

Results

A total of 939 samples of glial tumours were analysed, for which subtype-specific expression profiles of genes and miRNAs were identified and networks of mRNA-miRNA interactions were constructed. Genes whose aberrant expression level was associated with survival were determined, and pairwise correlations between differential gene expression (DEG) and differential miRNA expression (DE miRNA) were calculated.

Conclusions

The developed panel of genes and miRNAs allowed us to differentiate glioma subtypes and evaluate prognosis in terms of the overall survival of patients. The regulatory miRNA-mRNA pairs unique to the five glioma subtypes identified in this study can stimulate the development of new therapeutic approaches based on subtype-specific mechanisms of oncogenesis.

Improving existing analysis pipeline to identify and analyze cancer driver genes using multi-omics data

The cumulative of genes carrying mutations is vital for the establishment and development of cancer. However, this driver gene exploring research line has selected and used types of tools and models of analysis unsystematically and discretely. Also, the previous studies may have neglected low-frequency drivers and seldom predicted subgroup specificities of identified driver genes. In this study, we presented an improved driver gene identification and analysis pipeline that comprises the four most widely focused analyses for driver genes: enrichment analysis, clinical feature association with expression profiles of identified driver genes as well as with their functional modules, and patient stratification by existing advanced computational tools integrating multi-omics data. The improved pipeline's general usability was demonstrated straightforwardly for breast cancer, validated by some independent databases. Accordingly, 31 validated driver genes, including four novel ones, were discovered. Subsequently, we detected cancer-related significantly enriched gene ontology terms and pathways, probable drug targets, two co-expressed modules associated significantly with several clinical features, such as number of positive lymph nodes, Nottingham prognostic index, and tumor stage, and two biologically distinct groups of BRCA patients. Data and source code of the case study can be downloaded at https://github.com/hauldhut/drivergene.

Three-gene prognostic biomarkers for seminoma identified by weighted gene co-expression network analysis

Testicular germ cell tumors (TGCTs) are common in young males, and seminoma accounts for a large proportion of TGCTs. However, there are limited records on the exploration of novel biomarkers for seminoma. Hence, we aimed to identify new biomarkers associated with overall survival in seminoma. mRNA-seq and clinical traits of TGCTs were downloaded from UCSC XENA and analyzed by weighted gene co-expression network analysis. After intersection with differentially expressed genes in GSE8607, common genes were subjected to protein-protein interaction (PPI) network construction and enrichment analyses. Then, the top 10 common genes were investigated by Kaplan–Meier (KM) survival analyses and univariate Cox regression analyses. Ultimately, TYROBP, CD68, and ITGAM were considered three prognostic biomarkers in seminoma. Based on correlation analysis between these genes and immune infiltrates, we suggest that the three biomarkers influence the survival of seminoma patients, possibly through regulating the infiltration of immune cells. In conclusion, our study demonstrated that TYROBP, CD68, and ITGAM could be regarded as prognostic biomarkers and therapeutic targets for seminoma patients.

Identification of Prognostic Model and Biomarkers for Cancer Stem Cell Characteristics in Glioblastoma by Network Analysis of Multi-Omics Data and Stemness Indices

The progression of most human cancers mainly involves the gradual accumulation of the loss of differentiated phenotypes and the sequential acquisition of progenitor and stem cell-like features. Glioblastoma multiforme (GBM) stem cells (GSCs), characterized by self-renewal and therapeutic resistance, play vital roles in GBM. However, a comprehensive understanding of GBM stemness remains elusive. Two stemness indices, mRNAsi and EREG-mRNAsi, were employed to comprehensively analyze GBM stemness. We observed that mRNAsi was significantly related to multi-omics parameters (such as mutant status, sample type, transcriptomics, and molecular subtype). Moreover, potential mechanisms and candidate compounds targeting the GBM stemness signature were illuminated. By combining weighted gene co-expression network analysis with differential analysis, we obtained 18 stemness-related genes, 10 of which were significantly related to survival. Moreover, we obtained a prediction model from both two independent cancer databases that was not only an independent clinical outcome predictor but could also accurately predict the clinical parameters of GBM. Survival analysis and experimental data confirmed that the five hub genes (CHI3L2, FSTL3, RPA3, RRM2, and YTHDF2) could be used as markers for poor prognosis of GBM. Mechanistically, the effect of inhibiting the proliferation of GSCs was attributed to the reduction of the ratio of CD133 and the suppression of the invasiveness of GSCs. The results based on an in vivo xenograft model are consistent with the finding that knockdown of the hub gene inhibits the growth of GSCs in vitro. Our approach could be applied to facilitate the development of objective diagnostic and targeted treatment tools to quantify cancer stemness in clinical tumors, and perhaps lead considerable benefits that could predict tumor prognosis, identify new stemness-related targets and targeted therapies, or improve targeted therapy sensitivity. The five genes identified in this study are expected to be the targets of GBM stem cell therapy.

MiR-93/miR-375: Diagnostic Potential, Aggressiveness Correlation and Common Target Genes in Prostate Cancer

Dysregulation of miRNAs has a fundamental role in the initiation, development and progression of prostate cancer (PCa). The potential of miRNA in gene therapy and diagnostic applications is well documented. To further improve miRNAs' ability to distinguish between PCa and benign prostatic hyperplasia (BPH) patients, nine miRNA (-21, -27b, -93, -141, -205, -221, -182, -375 and let-7a) with the highest reported differentiation power were chosen and for the first time used in comparative studies of serum and prostate tissue samples. Spearman correlations and response operating characteristic (ROC) analyses were applied to assess the capability of the miRNAs present in serum to discriminate between PCa and BPH patients. The present study clearly demonstrates that miR-93 and miR-375 could be taken into consideration as single blood-based non-invasive molecules to distinguish PCa from BPH patients. We indicate that these two miRNAs have six common, PCa-related, target genes (CCND2, MAP3K2, MXI1, PAFAH1B1, YOD1, ZFYVE26) that share the molecular function of protein binding (GO:0005515 term). A high diagnostic value of the new serum derived miR-182 (AUC = 0.881, 95% confidence interval, CI = 0.816-0.946, p < 0.0001, sensitivity and specificity were 85% and 79%, respectively) is also described.

Identification of Prognostic Biomarkers Associated with Cancer Stem Cell Features in Prostate Adenocarcinoma

Background

Prostate adenocarcinoma (PRAD) is the second most common malignancy in males and the fifth leading cause of cancer mortality. Cancer stem cells (CSCs) play an important role in the occurrence and development of PRAD, however, the prognostic biomarkers associated with CSC features have not been identified in PRAD.

Material/Methods

In order to identify the prognostic stemness-related genes (SRGs) of PRAD, the RNA sequencing data of patients with PRAD were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between the mRNAsi and tumorigenesis, overall survival (OS), prostate-specific antigen (PSA) value, and Gleason score were also established by nonparametric test and Kaplan-Meier survival analysis. The SRGs were identified as the overlapped DEGs of PRAD-associated DEGs and the mRNAsi-associated DEGs. Based on the prognostic SRGs, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve and the risk score.

Results

A total of 6005 PRAD-associated DEGs and 2462 mRNAsi-associated DEGs were identified. The mRNAsi was significantly upregulated in PRAD and associated with the PSA value and Gleason score. A total of 1631 SRGs were identified, with 36 prognostic SRGs screened by the univariate Cox analysis. Based on the prognostic SRGs, the predict model was constructed with the AUC of 0.986. Moreover, the risk score of the model was proved to be an independent prognostic factor, indicating its significant applicability.

Conclusions

Our data demonstrate the mRNAsi as a reliable index for the tumorigenesis, PSA value, and Gleason score of PRAD. Additionally, this study provides a well-applied model for predicting the OS for patients with PRAD based on prognostic SRGs.