Identification of Biomarkers Correlated with the TNM Staging and Overall Survival of Patients with Bladder Cancer

Identification of hub genes in bladder transitional cell carcinoma through ceRNA network construction integrated with gene network analysis

Bladder transitional cell carcinoma (BTCC) forms more than 90% of bladder cancer cases. It brings challenges to the early diagnosis and therapy of BTCC, due to lack of efficient screening biomarkers. We used weighted gene co-expression network analysis (WGCNA) combined competing endogenous RNA (ceRNA) network construction depending on TCGA datasets to investigate potential hub genes and regulatory pathways associated with occurrence and progression of BTCC. We further used real-time polymerase chain reaction (RT-PCR) to validate the relative expression genes correlated with BTCC. By WGCNA, the gene co-expression module with 11 genes was found corelated with BTCC tumour stage and prognosis after survival analyses. Ultimately, we put 100 highly stage-related genes into the above constructed ceRNA network and then constructed another new network. Among them, all elements in AC112721.1/LINC00473/AC128709.1-hsa-mir-195-RECK and LINC00460-hsa-mir-429-ZFPM2 axes were simultaneously corelated with overall survival. RT-PCR showed that AKAP12 was downregulated in tumour tissues. The hub genes screened out in the present study may provide ideals for further treatment on BTCC.

Identification of Breast Cancer Subtypes by Integrating Genomic Analysis with the Immune Microenvironment

Objectives: We aim to identify the breast cancer (BC) subtype clusters and the crucial gene classifier prognostic signatures by integrating genomic analysis with the tumor immune microenvironment (TME). Methods: Data sets of BC were derived from the Cancer Genome Atlas (TCGA), METABRIC, and Gene Expression Omnibus (GEO) databases. Unsupervised consensus clustering was carried out to obtain the subtype clusters of BC patients. Weighted gene coexpression network analysis (WGCNA), least absolute shrinkage and selection operator (LASSO), and univariate and multivariate regression analysis were employed to obtain the gene classifier signatures and their biological functions, which were validated by the BC dataset from the METABRIC database. Additionally, to evaluate the overall survival rates of BC patients, Kaplan-Meier survival analysis was carried out. Moreover, to assess how BC subtype clusters are related to the TME, single-cell analysis was performed. Finally, the drug sensitivity and the immune cell infiltration for different phenotypes of BC patients were also calculated by the CIBERSORT and ESTIMATE algorithms. Results : TCGA-BC samples were divided into three subtype clusters, S1, S2, and S3, among which the prognosis of S2 was poor and that of S1 and S3 were better. Three key pathways and 10 crucial prognostic-related gene signatures are screened. Finally, single-cell analysis suggests that S1 samples have the most types of immune cells, S2 with more sensitivity to tumor treatment drugs are enriched with more neutrophils, and more multilymphoid progenitor cells are involved in subtype cluster S3. Conclusions: Our novelty was to identify the BC subtype clusters and the gene classifier signatures employing a large-amount dataset combined with multiple bioinformatics methods. All of the results provide a basis for clinical precision treatment of BC.

Identification of a novel defined inflammation-related long noncoding RNA signature contributes to predicting prognosis and distinction between the cold and hot tumors in bladder cancer

Purpose

Bladder cancer (BLCA) is one of the most frequently diagnosed urological malignancies and is the 4th most common cancer in men worldwide. Molecular targets expressed in bladder cancer (BLCA) are usually used for developing targeted drug treatments. However, poor prognosis and poor immunotherapy efficacy remain major challenges for BLCA. Numerous studies have shown that long non-coding RNAs (LncRNAs) play an important role in the development of cancer. However, the role of lncRNAs related to inflammation in BLCA and their prognostic value remain unclear. Therefore, this study is aimed to explore new potential biomarkers that can predict cancer prognosis.

Methods

We downloaded BLCA-related RNA sequencing data from The Cancer Genome Atlas (TCGA) and searched for inflammation-related prognostic long non-coding RNAs (lncRNAs) by univariate Cox (uniCox) regression and co-expression analysis. We used the least absolute shrinkage and selection operator (LASSO) analysis to construct an inflammation-related lncRNA prognosis risk model. Samples were divided into high-risk score (HRS) group and low-risk score (LRS) group based on the median value of risk scores. The independent variable factors were identified by univariate Cox (uni-Cox) and multivariate Cox (multi-Cox) regression analyses, and receiver operating characteristic (ROC) curves were used to compare the role of different factors in predicting outcomes. Nomogram and Calibration Plot were generated by the R package rms to analyze whether the prediction results are correct and show good consistency. Correlation coefficients were calculated by Pearson analysis. The Kaplan-Meier method was used to assess the prognostic value. The expression of 7 lncRNAs related with inflammation was also confirmed by qRT-PCR in BLCA cell lines. Kyoto Encyclopedia of Gene and Genome (KEGG) pathways that were significantly enriched (P < 0.05) in each risk group were identified by the GSEA software. The R package pRRophetic was used to predict the IC50 of common chemotherapeutic agents. TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC and CIBERSORT were applied to quantify the relative proportions of infiltrating immune cells. We also used package ggpubr to evaluate TME scores and immune checkpoint activation in LRS and HRS populations. R package GSEABase was used to analyze the activity of immune cells or immune function. Different clusters of principal component analysis (PCA), t-distribution random neighborhood embedding (t-SNE), and Kaplan-Meier survival were analyzed using R package Rtsne’s. The R package ConsensesClusterPlus was used to class the inflammation-related lncRNAs.

Results

In this study, a model containing 7 inflammation-related lncRNAs was constructed. The calibration plot of the model was consistent with the prognosis prediction outcomes. The 1-, 3-, and 5-year ROC curve (AUC) were 0.699, 0.689, and 0.699, respectively. High-risk patients were enriched in lncRNAs related with tumor invasion and immunity, and had higher levels of immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 2 and 3 and cluster 1, respectively, which indicated that hot tumors are more susceptible to immunotherapy.

Conclusion

Our study showed that inflammation-related LncRNAs are closely related with BLCA, and inflammation-related lncRNA can accurately predict patient prognosis and effectively differentiate between hot and cold tumors, thus improving individualized immunotherapy for BLCA patients. Therefore, this study provides an effective predictive model and a new therapeutic target for the prognosis and clinical treatment of BLCA, thus facilitating the development of individualized tumor therapy.

Predictive Value of Gene Databases in Discovering New Biomarkers and New Therapeutic Targets in Lung Cancer

Background and Objectives: The molecular mechanisms of lung cancer are still unclear. Investigation of immune cell infiltration (ICI) and the hub gene will facilitate the identification of specific biomarkers. Materials and Methods: Key modules of ICI and immune cell-associated differential genes, as well as ICI profiles, were identified using lung cancer microarray data from the single sample gene set enrichment analysis (ssGSEA) and weighted gene co-expression network analysis (WGCNA) in the gene expression omnibus (GEO) database. Protein–protein interaction networks were used to identify hub genes. The receiver operating characteristic (ROC) curve was used to assess the diagnostic significance of the hub genes, and survival analysis was performed using gene expression profiling interactive analysis (GEPIA). Results: Significant changes in ICI were found in lung cancer tissues versus adjacent normal tissues. WGCNA results showed the highest correlation of yellow and blue modules with ICI. Protein–protein interaction networks identified four hub genes, namely CENPF, AURKA, PBK, and CCNB1. The lung adenocarcinoma patients in the low hub gene expression group showed higher overall survival and longer median survival than the high expression group. They were associated with a decreased risk of lung cancer in patients, indicating their potential role as cancer suppressor genes and potential targets for future therapeutic development. Conclusions: CENPF, AURKA, PBK, and CCNB1 show great potential as biomarkers and immunotherapeutic targets specific to lung cancer. Lung cancer patients’ prognoses are often foreseen using matched prognostic models, and genes CENPF, AURKA, PBK, and CCNB1 in lung cancer may serve as therapeutic targets, which require further investigations.

In-depth systems biological evaluation of bovine alveolar macrophages suggests novel insights into molecular mechanisms underlying Mycobacterium bovis infection

OBJECTIVE

Bovine tuberculosis (bTB) is a chronic respiratory infectious disease of domestic livestock caused by intracellular Mycobacterium bovis infection, which causes ~$3 billion in annual losses to global agriculture. Providing novel tools for bTB managements requires a comprehensive understanding of the molecular regulatory mechanisms underlying the M. bovis infection. Nevertheless, a combination of different bioinformatics and systems biology methods was used in this study in order to clearly understand the molecular regulatory mechanisms of bTB, especially the immunomodulatory mechanisms of M. bovis infection.

METHODS

RNA-seq data were retrieved and processed from 78 (39 non-infected control vs. 39 M. bovis-infected samples) bovine alveolar macrophages (bAMs). Next, weighted gene co-expression network analysis (WGCNA) was performed to identify the co-expression modules in non-infected control bAMs as reference set. The WGCNA module preservation approach was then used to identify non-preserved modules between non-infected controls and M. bovis-infected samples (test set). Additionally, functional enrichment analysis was used to investigate the biological behavior of the non-preserved modules and to identify bTB-specific non-preserved modules. Co-expressed hub genes were identified based on module membership (MM) criteria of WGCNA in the non-preserved modules and then integrated with protein-protein interaction (PPI) networks to identify co-expressed hub genes/transcription factors (TFs) with the highest maximal clique centrality (MCC) score (hub-central genes).

RESULTS

As result, WGCNA analysis led to the identification of 21 modules in the non-infected control bAMs (reference set), among which the topological properties of 14 modules were altered in the M. bovis-infected bAMs (test set). Interestingly, 7 of the 14 non-preserved modules were directly related to the molecular mechanisms underlying the host immune response, immunosuppressive mechanisms of M. bovis, and bTB development. Moreover, among the co-expressed hub genes and TFs of the bTB-specific non-preserved modules, 260 genes/TFs had double centrality in both co-expression and PPI networks and played a crucial role in bAMs-M. bovis interactions. Some of these hub-central genes/TFs, including PSMC4, SRC, BCL2L1, VPS11, MDM2, IRF1, CDKN1A, NLRP3, TLR2, MMP9, ZAP70, LCK, TNF, CCL4, MMP1, CTLA4, ITK, IL6, IL1A, IL1B, CCL20, CD3E, NFKB1, EDN1, STAT1, TIMP1, PTGS2, TNFAIP3, BIRC3, MAPK8, VEGFA, VPS18, ICAM1, TBK1, CTSS, IL10, ACAA1, VPS33B, and HIF1A, had potential targets for inducing immunomodulatory mechanisms by M. bovis to evade the host defense response.

CONCLUSION

The present study provides an in-depth insight into the molecular regulatory mechanisms behind M. bovis infection through biological investigation of the candidate non-preserved modules directly related to bTB development. Furthermore, several hub-central genes/TFs were identified that were significant in determining the fate of M. bovis infection and could be promising targets for developing novel anti-bTB therapies and diagnosis strategies.

Lamin B2 contributes to the proliferation of bladder cancer cells via activating the expression of cell division cycle‑associated protein 3

Bladder cancer is the most common malignant tumor of the urinary system, and in China it is first among urogenital system tumors. More therapeutic targets are still urgently required to combat this disease. Lamin B2 (LMNB2) is a type of nuclear lamina filament protein, which is involved in multiple cellular processes, and known as an oncogene affecting the progression of multiple types of cancers. Although the multiple effects of LMNB2 on cancer progression have been elucidated, its possible role in bladder cancer remains unclear. In the present study, it was determined that LMNB2 expression was upregulated in human bladder cancer tissues, and its expression was correlated with the prognosis and the clinical features, including tumor stage (P=0.001) and recurrence (P=0.006) of patients with bladder cancer. In addition, it was further revealed that LMNB2 depletion inhibited bladder cancer cell proliferation, stimulated cell cycle arrest and apoptosis in vitro, and suppressed tumor growth of bladder cancer cells in mice. Furthermore, the present data revealed that LMNB2 promoted the proliferation of bladder cancer cells via transcriptional activation of CDCA3 expression. Therefore, the role of LMNB2 in bladder cancer progression was demonstrated, and may serve as a promising therapeutic target for bladder cancer treatment.

Cell Division Cycle-Associated Protein 3 (CDCA3) Is a Potential Biomarker for Clinical Prognosis and Immunotherapy in Pan-Cancer

CDCA3 is an essential regulator in cell mitosis and can regulate many physiological and pathological processes in the human body by stimulating certain proteins such as cell cycle regulatory proteins, transcription factors, and signal transduction molecules. Although several studies have shown that dysregulation of CDCA3 is a common phenomenon in human cancers, no systematic pan-cancer analysis has been performed. In this study, we comprehensively investigated the role of CDCA3 in 33 human cancer types by utilizing multiple cancer-related databases and bioinformatics analysis tools, including TCGA, GTEx, GEPIA, TIMER, STRING, Metascape, and Cytoscape. Evidence from bioinformatics databases shows that CDCA3 is overexpressed in almost all human cancer types, and its overexpression is significantly associated with survival in patients with more than ten cancer types. CDCA3 expression positively correlates with immune cell infiltration levels in multiple human cancer types. Furthermore, the results of the GSEA analysis revealed that overexpression of CDCA3 may promote the malignant progression of cancer by activating various oncogenic signaling pathways in human cancers. In conclusion, our pan-cancer analysis provides a comprehensive overview of the oncogenic role of CDCA3 in multiple human cancer types, suggesting that CDCA3 may serve as a potential therapeutic target and prognostic biomarker in multiple human cancer types.

An Integrated Bioinformatics Analysis towards the Identification of Diagnostic, Prognostic, and Predictive Key Biomarkers for Urinary Bladder Cancer

Simple Summary

Bladder cancer is evidently a challenge as far as its prognosis and treatment are concerned. The investigation of potential biomarkers and therapeutic targets is indispensable and still in progress. Most studies attempt to identify differential signatures between distinct molecular tumor subtypes. Therefore, keeping in mind the heterogeneity of urinary bladder tumors, we attempted to identify a consensus gene-related signature between the common expression profile of bladder cancer and control samples. In the quest for substantive features, we were able to identify key hub genes, whose signatures could hold diagnostic, prognostic, or therapeutic significance, but, primarily, could contribute to a better understanding of urinary bladder cancer biology.

Abstract

Bladder cancer (BCa) is one of the most prevalent cancers worldwide and accounts for high morbidity and mortality. This study intended to elucidate potential key biomarkers related to the occurrence, development, and prognosis of BCa through an integrated bioinformatics analysis. In this context, a systematic meta-analysis, integrating 18 microarray gene expression datasets from the GEO repository into a merged meta-dataset, identified 815 robust differentially expressed genes (DEGs). The key hub genes resulted from DEG-based protein–protein interaction and weighted gene co-expression network analyses were screened for their differential expression in urine and blood plasma samples of BCa patients. Subsequently, they were tested for their prognostic value, and a three-gene signature model, including COL3A1, FOXM1, and PLK4, was built. In addition, they were tested for their predictive value regarding muscle-invasive BCa patients’ response to neoadjuvant chemotherapy. A six-gene signature model, including ANXA5, CD44, NCAM1, SPP1, CDCA8, and KIF14, was developed. In conclusion, this study identified nine key biomarker genes, namely ANXA5, CDT1, COL3A1, SPP1, VEGFA, CDCA8, HJURP, TOP2A, and COL6A1, which were differentially expressed in urine or blood of BCa patients, held a prognostic or predictive value, and were immunohistochemically validated. These biomarkers may be of significance as prognostic and therapeutic targets for BCa.

Bioinformatics Analysis of mRNAs and miRNAs for Identifying Potential Biomarkers in Lung Adenosquamous Carcinoma

Background

Lung adenosquamous carcinoma (LASC) is a special type of lung cancer. LASC is a malignant tumor with strong aggressiveness and a poor prognosis. Previous studies have revealed that microRNAs (miRNAs) are widely involved in the development of tumors by targeting mRNA. This study is aimed at identifying the key mRNAs and miRNAs of LASC and constructing miRNA-mRNA networks for deeply comprehending the latent molecular mechanisms.

Methods

mRNA dataset (GSE51852) and miRNA dataset (GSE51853) were extracted and downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) were picked out by the GEO2R web tool. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were conducted in the DAVID database. The protein-protein interaction (PPI) network was performed and analyzed by using the STRING database and Cytoscape software, respectively. TransmiR v2.0 was applied to predict potential transcription factors of miRNAs. The target genes of DEMs were predicted in the miRWalk database.

Results

In comparison to normal tissues, a total of 1458 DEGs (511 upregulated and 947 downregulated) and 13 DEMs (5 upregulated and 8 downregulated) were screened out in LASC tissues. The PPI network of the DEGs displayed five key modules and seventeen hub genes. Six target genes of the DEMs were predicted, and five essential miRNA-mRNA regulatory pairs were established. Ensuingly, CENPF, one of the target genes, was also the hub genes of GSE51852, which was obtained from MCODE and cytoHubba and regulated by hsa-miR-205.

Conclusions

We constructed the miRNA-mRNA regulatory pairs, which are helpful to study the potential regulatory mechanisms and find out promising diagnosis biomarkers and therapeutic targets for LASC.

Identification of biomarkers for early diagnosis of multiple myeloma by weighted gene co-expression network analysis and their clinical relevance

BACKGROUND

Multiple myeloma is an incurable hematologic malignancy, its early diagnosis is important. However, the biomarker for early diagnosis is limited; hence more need to be identified. The present study aimed to explore the easily tested new biomarker in multiple myeloma by weighted gene co-expression network analysis (WGCNA).

METHODS

Differentially expressed genes (DEGs) were screened using GSE47552. WGCNA was used to screen hub genes. Subsequently. Hub genes of multiple myeloma were obtained by intersection of DEGs and WGCNA. We used the T-test to screen highly expressed genes. Then, the diagnostic value of key genes was evaluated by the receiver operating characteristic (ROC) curve. Finally, expression levels of key genes were tested and proved by RT-PCR.

RESULTS

278 DEGs were screened by Limma package. Three modules were most significantly correlated with multiple myeloma. 238 key genes were screened after the intersection of WGCNA with DEGs. In addition, SNORNA is rarely studied in multiple myeloma, and ROC curve analysis in our prediction model showed that SNORA71A had a good prediction effect (p = 0.07). The expression of SNORA71A was increased in samples of multiple myeloma (P = 0.05). RT-PCR results showed that SNORA71A was upregulated in 51 patient specimens compared to the healthy group (P < 0.05). Linear correlation analysis showed that creatinine was positively correlated with SNORA71A (r = 0.49 P = 0.0002).

CONCLUSIONS

This study found that SNORA71A was up-regulated and associated with the clinical stages in multiple myeloma; it suggests that SNORA71A could be used as a novel biomarker for early diagnosis and a potential therapeutic target in multiple myeloma.

An Oxidative Stress-Related Genes Signature for Predicting Survival in Bladder Cancer: Based on TCGA Database and Bioinformatics

Background

Oxidative stress (OS) responses have been linked to oncogenesis and tumor progression and have recently been regarded as a potential strategy for tumor therapy. However, OS-related therapeutic targets have not been identified to date in the bladder cancer (BC).

Methods

The mRNA expression and clinical data of BC were downloaded from the public database. Prognostic risk score signature was constructed using LASSO Cox regression analysis. External validation was performed in GSE15307 cohort. ESTIMATE, CIBERSORT, and ssGSEA algorithm were used to analyze immune cell infiltration and immune microenvironment. Next, functional enrichment analysis was performed to elucidate the mechanism underlying the signature. Additionally, we performed a nomogram to forecast the survival rate of individual BC patients.

Results

An OS-related genes (OSRGs) signature was constructed. Overall survival was lower in the high-risk group than in the low-risk group, according to survival analyses. The area under the curve (AUC) of ROC curves further validated the prognostic signature’s strong prediction performance in these two cohorts. The risk score was verified as an independent risk factor for BC by independent prognostic analysis. Moreover, as compared to TNM stage alone, a nomogram that integrated the risk score with TNM stage showed a much superior predictive value. Immune infiltration and tumor microenvironment studies indicated that immune cells and functions may play a significant role in carcinogenesis and development. The levels of expression of prognostic genes were shown to be substantially linked with drug sensitivity.

Conclusion

We developed a novel OSRGs signature for predicting overall survival and impacting the immune status in patients with BC. New nomogram can help clinicians predict the survival rate of BC patients. These findings shed new light on the potential usage of OSRGs signature in BC patients.

A 7-gene signature predicts the prognosis of patients with bladder cancer

The biomarkers have an important guiding role in prognosis and treatment of patients with bladder cancer (BC). The aim of the present study was to identify and evaluate a prognostic gene signature in BC patients. The gene expression profiles of BC samples and the corresponding clinicopathological data were downloaded from GEO and TCGA. The differentially expressed genes (DEGs) were identified by R software. Univariate Cox regression and the least absolute shrinkage and selection operator (LASSO) Cox regression were applied to construct the prognostic score model. A nomogram was established with the identified prognostic factors to predict the overall survival rates of BC patients. The discriminatory and predictive capacity of the nomogram was evaluated based on the concordance index (C‐index), calibration curves and decision curve analysis (DCA). A 7-gene signature (KLRB1, PLAC9, SETBP1, NR2F1, GRHL2, ANXA1 and APOL1) was identified from 285 DEGs by univariate and LASSO Cox regression analyses. Univariate and multivariate Cox regression analyses showed that age, lymphovascular invasion, lymphatic metastasis, metastasis and the 7-gene signature risk score was an independent predictor of BC patient prognosis. A nomogram that integrated these independent prognostic factors was constructed. The C-index (0.73, CI 95%, 0.693–0.767) and calibration curve demonstrated the good performance of the nomogram. DCA of the nomogram further showed that this model exhibited good net benefit. The combined 7-gene signature could serve as a biomarker for predicting BC prognosis. The nomogram built by risk score and other clinical factors could be an effective tool for predicting the prognosis of patients with BC.

Prognostic value of CDCA3 in kidney renal papillary cell carcinoma

Kidney renal papillary cell carcinoma (KIRP) is a type of low-grade malignant renal cell carcinoma. Huge challenges remain in the treatment of KIRP. Cell division cycle associated 3 (CDCA3) participates in human physiological and pathological processes. However, its role in KIRP has not been established. Here, we evaluated the prognostic value of CDCA3 in KIRP using a comprehensive bioinformatics approach. Data for CDCA3 expression in KIRP were obtained from online database. Different expression genes between high and low CDCA3 expression groups were identified and evaluated by performing Gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses. A gene set enrichment analysis was performed to elucidate the function and pathway differences between the different. Differences in immune cell infiltration between low and high CDCA3 expression groups were analyzed by a single-sample GSEA method for immune cells. A protein-protein interaction network was generated and hub genes were identified. UALCAN was used to analyze associations between the mRNA expression levels of CDCA3 in KIRP tissues with clinicopathologic parameters. The diagnostic efficacy of CDCA3 for KIRP was analyzed by ROC analysis. Logistic regression was used to analyze relationships between the clinicopathological characteristics and CDCA3 expression. Our results indicated that high CDCA3 mRNA expression is significantly associated with some clinicopathologic parameters in KIRP patients High CDCA3 mRNA expression associated with a shorter overall survival, progression-free interval, and disease-special survival. Taken together, CDCA3 is a potential target for the development of anti-KIRP therapeutics and is an efficient prognostic marker.

Identification of hub genes in bladder cancer based on weighted gene co-expression network analysis from TCGA database

Background

Muscular invasive bladder cancer (MIBC) is a common malignant tumor in the world. Because of their heterogeneity in prognosis and response to treatment, biomarkers that can predict survival or help make treatment decisions in patients with MIBC are essential for individualized treatment.

Aim

We aimed to integrate bioinformatics research methods to identify a set of effective biomarkers capable of predicting, diagnosing, and treating MIBC. To provide a new theoretical basis for the diagnosis and treatment of bladder cancer.

Methods and results

Gene expression profiles and clinical data of MIBC were obtained by downloading from the Cancer Genome Atlas database. A dataset of 129 MIBC cases and controls was included. 2084 up‐regulated genes and 2961 down‐regulated genes were identified by differentially expressed gene (DEG) analysis. Then, gene ontology analysis was performed to explore the biological functions of DEGs, respectively. The up‐regulated DEGs are mainly enriched in epidermal cell differentiation, mitotic nuclear division, and so forth. They are also involved in the cell cycle, p53 signaling pathway, PPAR signaling pathway, and so forth. The weighted gene co‐expression network analysis yielded five modules related to pathological stages and grading, of which blue and turquoise were the most relevant modules for MIBC. Next, Using Kaplan–Meier survival analysis to identify further hub genes, the screening criteria at p ≤ .05, we found CNKSR1, HIP1R, CFL2, TPM1, CSRP1, SYNM, POPDC2, PJA2, and RBBP8NL genes associated with the progression and prognosis of MIBC patients. Finally, immunohistochemistry experiments further confirmed that CNKSR1 plays a vital role in the tumorigenic context of MIBC.

Conclusion

The research suggests that CNKSR1, POPDC2, and PJA2 may be novel biomarkers as therapeutic targets for MIBC, especially we used immunohistochemical further to validate CNKSR1 as a therapeutic target for MIBC which may help to improve the prognosis for MIBC.

Long Noncoding RNA LINC00707 Accelerates Tumorigenesis and Progression of Bladder Cancer via Targeting miR-145/CDCA3 Regulatory Loop

INTRODUCTION

Growing studies reveal that long noncoding RNA is involved in oncogenesis and progression. Previous studies have demonstrated that long intergenic noncoding RNA 00707 (LINC00707) stimulated tumor progress in numerous neoplasm types; however, the function of LINC00707 in bladder cancer (BC) was not yet clear. Our researches aimed to determine whether LINC00707 was dysregulated in BC and further study its biological functions.

METHODS

LINC00707 levels in BC tissues and cells were measured using reverse transcription-PCR (RT-PCR), and the associations between the levels of LINC00707 and clinicopathological features and the months of survival were also examined. Then, Cell Counting Kit-8 assays, flow cytometry, colony formation assays, and Transwell assays were applied for the assessment of the impact of LINC00707 on the abilities of BC cells. The interaction between LINC00707 and miR-145 or cell division cycle associated 3 was determined by luciferase reporter system and RT-PCR. Protein expressions of Wnt/β-catenin signaling were examined using RT-PCR and Western blot.

RESULTS

We found that LINC00707 expressions were notably upregulated in BC samples and cells. Higher expressions of LINC00707 were associated with T stage, grade, and shorter overall survival in BC patients. LINC00707 was also an independent prognostic factor for BC. In vitro assays confirmed that silencing LINC00707 expressions suppressed cell proliferation, colony formation, and metastasis. Mechanistic studies elucidated that LINC00707 was directly targeted to miR-145/CDCA3. Western blot assays revealed that Wnt/β-catenin signaling was inactivated by LINC00707 knockdown.

CONCLUSION

Our work offers new insight into the function of LINC00707 in the tumorigenesis of BC.

Identification of collagen genes related to immune infiltration and epithelial-mesenchymal transition in glioma

Background

Gliomas account for the majority of fatal primary brain tumors, and there is much room for research in the underlying pathogenesis, the multistep progression of glioma, and how to improve survival. In our study, we aimed to identify potential biomarkers or therapeutic targets of glioma and study the mechanism underlying the tumor progression.

Methods

We downloaded the microarray datasets (GSE43378 and GSE7696) from the Gene Expression Omnibus (GEO) database. Then, we used weighted gene co-expression network analysis (WGCNA) to screen potential biomarkers or therapeutic targets related to the tumor progression. ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumors using Expression data) algorithm and TIMER (Tumor Immune Estimation Resource) database were used to analyze the correlation between the selected genes and the tumor microenvironment. Real-time reverse transcription polymerase chain reaction was used to measure the selected gene. Transwell and wound healing assays were used to measure the cell migration and invasion capacity. Western blotting was used to test the expression of epithelial-mesenchymal transition (EMT) related markers.

Results

We identified specific module genes that were positively correlated with the WHO grade but negatively correlated with OS of glioma. Importantly, we identified that 6 collagen genes (COL1A1, COL1A2, COL3A1, COL4A1, COL4A2, and COL5A2) could regulate the immunosuppressive microenvironment of glioma. Moreover, we found that these collagen genes were significantly involved in the EMT process of glioma. Finally, taking COL3A1 as a further research object, the results showed that knockdown of COL3A1 significantly inhibited the migration, invasion, and EMT process of SHG44 and A172 cells.

Conclusions

In summary, our study demonstrated that collagen genes play an important role in regulating the immunosuppressive microenvironment and EMT process of glioma and could serve as potential therapeutic targets for glioma management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01982-0.

The inhibitory effect of silencing CDCA3 on migration and proliferation in bladder urothelial carcinoma

Background

CDCA3 is an important component of the E3 ligase complex with SKP1 and CUL1, which could regulate the progress of cell mitosis. CDCA3 has been widely identified as a proto-oncogene in multiple human cancers, however, its role in promoting human bladder urothelial carcinoma has not been fully elucidated.

Methods

Bioinformatic methods were used to analyze the expression level of CDCA3 in human bladder urothelial carcinoma tissues and the relationship between its expression level and key clinical characteristics. In vitro studies were performed to validate the specific functions of CDCA3 in regulating cell proliferation, cell migration and cell cycle process. Alterations of related proteins was investigated by western blot assays. In vivo studies were constructed to validate whether silencing CDCA3 could inhibit the proliferation rate in mice model.

Results

Bioinformatic analysis revealed that CDCA3 was significantly up-regulated in bladder urothelial carcinoma samples and was related to key clinical characteristics, such as tumor grade and metastasis. Moreover, patients who had higher expression level of CDCA3 tend to show a shorter life span. In vitro studies revealed that silencing CDCA3 could impair the migration ability of tumor cells via down-regulating EMT-related proteins such as MMP9 and Vimentin and inhibit tumor cell growth via arresting cells in the G1 cell cycle phase through regulating cell cycle related proteins like p21. In vivo study confirmed that silencing CDCA3 could inhibit the proliferation of bladder urothelial carcinoma cells.

Conclusions

CDCA3 is an important oncogene that could strengthen the migration ability of bladder urothelial carcinoma cells and accelerate tumor cell growth via regulating cell cycle progress and is a potential biomarker of bladder urothelial carcinoma.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01969-x.

miR-133b inhibits cell proliferation, migration, and invasion of lung adenocarcinoma by targeting CDCA8

OBJECTIVE

Lung adenocarcinoma (LUAD) is the most common type of lung cancer. This study aims to explore the mechanism by which CDCA8 regulates cell proliferation, invasion, and migration of LUAD, and to generate novel insights into targeted therapy of LUAD.

METHODS

Expression profiles of mature microRNAs (miRNAs) and mRNAs, along with clinical data of LUAD were downloaded from TCGA database for differential analysis and survival analysis to mine differentially expressed mRNAs. qRT-PCR was used to detect the expression of CDCA8 and miR-133b in LUAD cell lines, and western blot was used to detect protein expression. The effects of CDCA8 on the proliferation, migration, and invasion of LUAD cells were detected by CCK-8 assay, scratch healing assay, and Transwell assay. Bioinformatics predicted the target miRNA of CDCA8, and dual-luciferase reporter gene assay was used to verify the binding relationship between miR-133b and CDCA8.

RESULTS

Data from TCGA-LUAD showed that CDCA8 was significantly overexpressed in LUAD tissue, while its upstream miRNA (miR-133b) was significantly lowly expressed. The result of dual-luciferase test showed that miR-133b targeted CDCA8. The results of in vitro functional experiments showed that overexpression of CDCA8 could promote the proliferation, invasion, and migration of LUAD cells, and miR-133b could reverse this promotion by targeting CDCA8.

CONCLUSION

This study found that CDCA8 was a carcinogenic factor in LUAD cells and it was regulated by upstream miR-133b. miR-133b could inhibit proliferation, invasion, and migration of LUAD cells by targeting CDCA8.

KIF4A promotes the development of bladder cancer by transcriptionally activating the expression of CDCA3

Bladder cancer (BC) is among the most common urinary system tumors with a high morbidity and mortality worldwide. Despite advancements being made in the diagnosis and treatment of bladder cancer, targeted therapy remains the most promising treatment, and novel therapeutic targets are urgently required in to improve the outcomes of patients with BC. Kinesin family member 4A (KIF4A) is a plus-end directed motor protein involved in the regulation of multiple cellular processes, such as mitosis and axon growth. Notably, KIF4A plays important roles in tumor growth and progression, and its expression is associated with the prognosis of several types of cancer. However, the potential role and molecular mechanisms of KIF4A in bladder cancer development remain unclear. The present study demonstrated that KIF4A was highly expressed in human BC tissues, and its expression was associated with patient clinicopathological characteristics, such as tumor stage (P=0.012) and with the prognosis of patients with BC. It was further found that KIF4A promoted the cell proliferation of bladder cancer both in vitro and in vivo. On the whole, the data presented herein provide evidence that KIF4A promotes the development of BC through the transcriptional activation of the expression of CDCA3. The present study indicates the involvement of KIF4A in the progression of BC and suggests that KIF4A may be a promising therapeutic target for the treatment of BC.

Identification of circRNA-miRNA-mRNA Regulatory Network in Bladder Cancer by Integrated Analysis

INTRODUCTION

Bladder cancer (BC) is a common malignant tumor in the urinary system with high mortality and recurrence rates. This study sought to identify crucial circular RNAs (circRNAs) associated with BC.

METHODS

The mRNA, miRNA, and circRNA expression profiles of BC were downloaded from GEO database. The differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and circRNAs (DEcircRNAs) were identified using bioinformatics method. Combining circRNA-miRNA pairs with miRNA-mRNA pairs, the competing endogenous RNA (ceRNA; DEcircRNA-DEmi-RNA-DEmRNA) regulatory network was constructed. Functional annotation of host gene of DEcircRNAs and DEmRNAs in ceRNA regulatory network were performed. qRT-PCR validation was performed.

RESULTS

A total of 4,003 DEmRNAs, 25 DEmiRNAs, and 119 DEcircRNAs were obtained. The ceRNA network contained 18 circRNA-miRNA pairs and 699 mi-RNA-mRNA pairs, including 17 circRNAs, 4 miRNAs, and 624 mRNAs. Functional annotation of DEmRNAs in ceRNA regulatory network revealed that these DEmRNAs were significantly enriched in glycerolipid metabolism, p53 signaling pathway, and oocyte meiosis. Except for hsa_circ_0028173, expression of the others in the qRT-PCR results was consistent with that in our integrated analysis, generally.

CONCLUSION

We speculate that hsa_circ_0008035/hsa-miR-107/MSRB3 and hsa_circ_0028173/hsa-miR-338-3p/TPX2/GATA3 interaction pairs may play a vital role in BC.

Co-expression network-based identification of biomarkers correlated with the lymph node metastasis of patients with head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is ranked as one of the most frequent malignancies worldwide with a high risk of lymph node metastasis, which serves as a main reason for cancer deaths. Identification of the potential biomarkers for lymph node metastasis in HNSCC patients may contribute to personalized treatment and better therapeutic effect. In the present study, GSE30788 microarray data and corresponding clinical parameters were downloaded from Gene Expression Omnibus (GEO) and Weighted Gene Co-expression Network Analysis (WGCNA) was performed to investigate significant modules associated with clinical traits. As a result, the genes in the blue module were determined as candidate genes related with HNSCC lymph node metastasis and ten hub genes were selected from the PPI network. Further analysis validated the close associations of hub gene expression with lymph node metastasis of HNSCC patients. Furthermore, survival analysis suggested the level of Loricrin (LOR) was statistically significantly associated with the disease-free survival of HNSCC patients, indicating the potential of utilizing it as prognosis predictor. Overall, our study conducted a co-expression network-based analysis to investigate significant genes underlying HNSCC metastasis, providing promising biomarkers and therapeutic targets.

Overexpression of CENPF is associated with progression and poor prognosis of lung adenocarcinoma

Background and aim: The molecular signatures of lung adenocarcinoma (LUAD) are not well understood. Centromere protein F (CENPF) has been shown to promote oncogenesis in many cancers; however, its role in LUAD has not been illustrated. We explored the role of CENPF in LUAD. Methods: CENPF expression level was investigated in public online database firstly, the prognosis of CENPF in LUAD were also assessed by Kaplan-Meier analysis. Then quantitative reverse transcription-polymerase chain reaction (qRT-PCR) was performed using 13 matched pairs of clinical LUAD tissue samples. Subsequently, the impact of CENPF expression on cell proliferation, cell cycle, apoptosis, colony formation was investigated by 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl tetrazolium bromide (MTT), flow cytometric analysis and colony formation assay, respectively. Finally, experimental xenograft lung cancer model of nude mice armpit of right forelimb to determine the effect of CENPF on LUAD tumorigenesis. Results: CENPF mRNA expression was significantly elevated in LUAD tissues compared with adjacent non-tumor lung tissues in Gene Expression Profiling Interactive Analysis (GEPIA) (P < 0.001). Up-regulated CENPF was remarkably positively associated with pathological stage, relapse free survival (RFS) as well as overall survival (OS) of LUAD patients. Besides, CENPF knockdown greatly suppressed A549 cell proliferation, induced S phase arrest, promoted apoptosis and decreased colony numbers of LUAD cells. Furthermore, knockdown of CENPF significantly inhibited the tumor growth of the LUAD cells in an experimental xenograft lung cancer model of nude mice armpit of right forelimb. Conclusion: Taken together, these results demonstrated that CENPF may serve as a potential biomarker of prognostic relevance and a potential therapeutic target for LUAD.

TCox: Correlation-Based Regularization Applied to Colorectal Cancer Survival Data

Colorectal cancer (CRC) is one of the leading causes of mortality and morbidity in the world. Being a heterogeneous disease, cancer therapy and prognosis represent a significant challenge to medical care. The molecular information improves the accuracy with which patients are classified and treated since similar pathologies may show different clinical outcomes and other responses to treatment. However, the high dimensionality of gene expression data makes the selection of novel genes a problematic task. We propose TCox, a novel penalization function for Cox models, which promotes the selection of genes that have distinct correlation patterns in normal vs. tumor tissues. We compare TCox to other regularized survival models, Elastic Net, HubCox, and OrphanCox. Gene expression and clinical data of CRC and normal (TCGA) patients are used for model evaluation. Each model is tested 100 times. Within a specific run, eighteen of the features selected by TCox are also selected by the other survival regression models tested, therefore undoubtedly being crucial players in the survival of colorectal cancer patients. Moreover, the TCox model exclusively selects genes able to categorize patients into significant risk groups. Our work demonstrates the ability of the proposed weighted regularizer TCox to disclose novel molecular drivers in CRC survival by accounting for correlation-based network information from both tumor and normal tissue. The results presented support the relevance of network information for biomarker identification in high-dimensional gene expression data and foster new directions for the development of network-based feature selection methods in precision oncology.

Developing new ceramide analogs and identifying novel sphingolipid-controlled genes against a virus-associated lymphoma

Primary effusion lymphoma (PEL) is an aggressive malignancy with poor prognosis even under chemotherapy. Kaposi sarcoma-associated herpesvirus (KSHV), one of the human oncogenic viruses, is the principal causative agent. Currently, there is no specific treatment for PEL; therefore, developing new therapies is of great importance. Sphingolipid metabolism plays an important role in determining the fate of tumor cells. Our previous studies have demonstrated that there is a correlation between sphingolipid metabolism and KSHV+ tumor cell survival. To further develop sphingolipid metabolism-targeted therapy, after screening a series of newly synthesized ceramide analogs, here, we have identified compounds with effective anti-PEL activity. These compounds induce significant PEL apoptosis, cell-cycle arrest, and intracellular ceramide production through regulation of ceramide synthesizing or ceramide metabolizing enzymes and dramatically suppress tumor progression without visible toxicity in vivo. These new compounds also increase viral lytic gene expression in PEL cells. Our comparative transcriptomic analysis revealed their mechanisms of action for inducing PEL cell death and identified a subset of novel cellular genes, including AURKA and CDCA3, controlled by sphingolipid metabolism, and required for PEL survival with functional validation. These data provide the framework for the development of promising sphingolipid-based therapies against this virus-associated malignancy.

[Spindle assembly checkpoint complex-related genes TTK and MAD2L1 are over-expressed in lung adenocarcinoma: a big data and bioinformatics analysis]

OBJECTIVE

To screen the key genes related to the prognosis of lung adenocarcinoma through big data analysis and explore their clinical value and potential mechanism.

METHODS

We analyzed GSE18842, GSE27262, and GSE33532 gene expression profile data obtained from the Gene Expression Omnibus (GEO). Bioinformatics methods were used to screen the differentially expressed genes in lung adenocarcinoma tissues and KEGG and GO enrichment analysis was performed, followed by PPI interaction network analysis, module analysis, differential expression analysis, and prognosis analysis. The expressions of MAD2L1 and TTK by immunohistochemistry were verified in 35 non-small cell lung cancer specimens and paired adjacent tissues.

RESULTS

We identified a total of 256 genes that showed significant differential expressions in lung adenocarcinoma, including 66 up-regulated and 190 down-regulated genes. Thirty-two up-regulated core genes were screened by functional analysis, and among them 29 were shown to significantly correlate with a poor prognosis of patients with lung adenocarcinoma. All the 29 genes were highly expressed in lung adenocarcinoma tissues compared with normal lung tissues and were mainly enriched in cell cycle pathways. Seven of these key genes were closely related to the spindle assembly checkpoint (SAC) complex and responsible for regulating cell behavior in G2/M phase. We selected SAC-related proteins TTK and MAD2L1 to test their expressions in clinical tumor samples, and detected their overexpression in lung adenocarcinoma tissues as compared with the adjacent tissues.

CONCLUSIONS

Seven SAC complex-related genes, including TTK and MAD2L1, are overexpressed in lung adenocarcinoma tissues with close correlation with the prognosis of the patients.

Integrated analysis of circRNA-miRNA-mRNA network reveals potential prognostic biomarkers for radiotherapies with X-rays and carbon ions in non-small cell lung cancer

Background

This work was aimed at exploring the regulatory network of non-coding RNA (ncRNA) especially circular RNA (circRNA) and microRNA (miRNA), in the sensitivity of non-small cell lung cancer (NSCLC) cells to low linear energy transfer (LET) X-ray and high-LET carbon ion irradiations.

Methods

The radioresistant NSCLC cell line A549-R11 was obtained from its parental cell line A549 through irradiation with X-rays of 2.0 Gy per fraction for 30 times. The sensitivities of A549, A549-R11 and H1299 cells exposed to X-rays and carbon ions were verified using the colony formation assay. A comprehensive circRNA-miRNA-mRNA network was constructed through the sequencing data in parental A549, acquired radioresistant A549-R11 and intrinsic radioresistant H1299 cells, and the network was further optimized according to the prognostic results from the TCGA and GEO databases.

Results

Based on high-throughput sequencing of circRNAs, we found that 40 circRNAs were up-regulated while 184 circRNAs were down-regulated in the intersection of the sets of A549-R11 and H1299 cells. Subsequently, a circRNA- miRNA-mRNA network, including 14 interactive pairs and 8 circRNAs, 4 overall survival-associated miRNAs, and 4 mRNAs, was constructed through the high-throughput data screening and bioinformatics methods.

Conclusions

Our results provide a complete understanding to the regulatory mechanism of the sensitivities to low-LET X-ray and high-LET carbon ion irradiations, and might be helpful to screen potential biomarkers for predicting the Carbon-ion radiotherapy (CIRT) and X-ray radiotherapy responses in NSCLC.

PUF60/AURKA Axis Contributes to Tumor Progression and Malignant Phenotypes in Bladder Cancer

Abnormal expression or mutation of RNA splicing proteins are widely observed in human cancers. Here, we identified poly(U) binding splicing factor 60 (PUF60) as one of the most differentially expressed genes out of 97 RNA splicing proteins between normal and bladder cancer tissues by bioinformatics analysis of TCGA bladder cancer expression data. The expression of PUF60 was significantly higher in tumor tissues, while high PUF60 expression was associated with malignant phenotypes of bladder cancer and shorter survival time. Moreover, we identified aurora kinase A (AURKA) as a new downstream target of PUF60 in bladder cancer cells. PUF60 knockdown significantly inhibited cell viability and colony formation capacity in bladder cancer cells, whereas AURKA overexpression reversed this inhibition effect. Overexpression of PUF60 significantly promoted cell viability and colony formation in bladder cancer cells, while treatment with AURKA specific inhibitor reversed this promotive effect. Mechanistically, PUF60 specifically bound to the AURKA promoter, thereby activating its transcription and expression. Furthermore, we showed that there was a significant positive correlation between PUF60 and AURKA expression in bladder cancer tissues, and PUF60 and AURKA expression contributed to tumor progression and malignant phenotypes in the patients with bladder cancer. Collectively, these results indicate that the PUF60/AURKA axis plays a key role in regulating tumorigenesis and progression of bladder cancer, and may be a potential prognostic biomarker and therapeutic target for bladder cancer patients.

Screening and identification of biomarkers associated with the diagnosis and prognosis of lung adenocarcinoma

BACKGROUND

In this study, we aimed to identify the pathogenesis and prognostic biomarkers of lung adenocarcinoma (LUAD).

METHODS

Differentially expressed mRNAs (DEmRNAs) and single nucleotide polymorphism (SNP) mutant genes were screened. In addition, enrichment and protein-protein interaction (PPI) network analyses of the SNP-mutated genes were performed. Thereafter, the correlation between gene mutation and expression was analyzed. Finally, the mutated genes associated with LUAD prognosis were validated on the basis of The Cancer Genome Atlas (TCGA) database.

RESULTS

A total of 2502 DEmRNAs were initially screened in this study. We identified 756 SNP-mutated genes from more than 30 cases. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis revealed that the mutated genes involved in LUAD were mainly associated with the ECM-receptor interaction, focal adhesion, and calcium signaling pathways. Tumor protein p53 (TP53) and neurexin 1 (NRXN1) with the higher degree were chosen as the hub genes in the PPI network. In addition, the correlation analysis revealed six genes, including assembly factor for spindle microtubules (ASPM), centromere protein F (CENPF), contactin 3 (CNTN3), catenin delta 2 (CTNND2), PKHD1 like 1 (PKHD1L1), and semaphorin 6D (SEMA6D), and three SNP mutations at ASPM rs368020495, CENPF rs762653487, and PKHD1L1 rs768349010 sites that were found to be associated with LUAD prognosis. Further validation showed that among the aforementioned six mutated genes, CENPF was upregulated and SEMA6D was downregulated.

CONCLUSION

CENPF, SEMA6D, TP53, and NRXN1 were found to be closely associated with the development of LUAD.

Matrix Metalloproteinase 11 is a Potential Biomarker in Bladder Cancer Diagnosis and Prognosis

Purpose

Bladder cancer is one of the leading causes of cancer death all over the world, and half of patients are diagnosed at advanced stages with poor therapeutic response. Thus, developing new biomarkers for bladder cancer diagnosis and prognosis is urgently needed.

Materials and Methods

Bioinformatic and gene ontology (GO) analysis were employed to screen highly upregulated and secretory tumor markers in the TCGA BLCA cohort. IHC in tissue microarray and ELISA in cancer cell culture medium were used to validate the expression of putative biomarkers in bladder cancer. Bisulfite sequencing was used to detect DNA methylation status in the promoter of putative genes.

Results

In this study, MMP11 is first identified as one of the most differentially expressed genes (DEGs) in bladder cancer by meta-analysis in a TCGA bladder cancer cohort. The strong upregulation of MMP11 is confirmed at protein levels in both bladder cancer patients and cell lines. Mechanistic studies reveal that MMP11 promoter hypomethylation, but not genomic amplification or mutation, accounts for its enhanced expression in bladder cancer both in vitro and in vivo. Moreover, clinicopathological analysis indicates that MMP11 upregulation is associated with the tumor progression and poor survival in bladder cancer patients.

Discussion

These findings suggest that MMP11, as a secretory protein, is a promising biomarker for diagnosis and prognosis in bladder cancer.

A novel risk score based on a combined signature of 10 immune system genes to predict bladder cancer prognosis

Bladder cancer (BC) is a common internal malignant tumor with a poor prognosis worldwide. There is an urgent need to better understand the pathogenesis and progression of BC and to find useful biomarkers for diagnosis and prognosis. This study was aimed at developing a potential immunogenomic prognostic signature for BC patients. To identify possible immune-system-related genes (IRGs) whose parameters predict the survival of BC patients, we chose 371 BC patients and analyzed differentially expressed IRGs from The Cancer Genome Atlas (TCGA) datasets. We then derived a 10-IRG formula, including MMP9, RBP7, PDGFRA, AHNAK, OAS1, OLR1, RAC3, SLIT2, IGF1, and AGTR1, to estimate BC prognosis. To validate the mRNA levels of these IRGs, we performed quantitative PCR and found that the expression of these genes almost matched the corresponding mRNA expression levels in TCGA. Furthermore, we validated the prognostic value of the new risk model using two external datasets from Gene Expression Omnibus: GSE13507 (n = 165) and GSE32894 (n = 224). Our data pointed to a significant correlation between the risk model and patients' prognosis. Bioinformatic analysis revealed that products of the IRGs have possible effects on tumor immune processes such as an inflammatory response and cytokine-cytokine receptor interaction. Finally, assessment of the clinical value of the immune-system-based risk signature showed that several of these IRGs were differentially expressed between patients with different clinical characteristics: a high risk score positively correlated with female sex, advanced tumor stage, more advanced T stage, and lymph node metastasis. This immunogenomic signature may represents a reliable prognostic tool for BC and can help to design an individualized immunotherapy.

The diagnostic and prognostic value of cell division cycle associated gene family in Hepatocellular Carcinoma

Cell division cycle associated (CDCA) gene family plays an important role in cells. However, some researchers revealed that overexpression of CDCAs might contribute to the tumor progression in several cancers. Here, we analyzed the role of this gene family in hepatocellular carcinoma (HCC). We used several web tools and found that most of CDCAs were highly expressed in tumor tissues compared to the paracancer tissues in HCC. We then used RT-qPCR to confirm our results. The results showed that CDCA2, CDCA3, CDCA5 and CDCA8 were up-regulated in HCC. We also found that these genes were associated with poor overall survival and relapse free survival except CDCA7. The functional analysis showed that this gene family might take part in many processes, including cell division, apoptosis, DNA damage and DNA repair, which might contribute to the tumor progression. The KEGG pathway analysis showed that these genes participated in several important pathways such as PI3K-Akt signaling pathway and hippo signaling pathway. In conclusion, our findings suggested that CDCA2, CDCA3, CDCA4, CDCA5, and CDCA8 might have potential diagnostic and prognostic values for hepatocellular carcinoma.

The Potential of Five Immune-Related Prognostic Genes to Predict Survival and Response to Immune Checkpoint Inhibitors for Soft Tissue Sarcomas Based on Multi-Omic Study

Low response rates to immunotherapy have been reported in soft tissue sarcoma (STS). There are few predictive biomarkers of response, and the tumor immune microenvironment associated with progression and prognosis remains unclear in STS. Gene expression data from the Cancer Genome Atlas were used to identify the immune-related prognostic genes (IRPGs) and construct the immune gene-related prognostic model (IGRPM). The tumor immune microenvironment was characterized to reveal differences between patients with different prognoses. Furthermore, somatic mutation data and DNA methylation data were analyzed to understand the underlying mechanism leading to different prognoses. The IGRPM was constructed using five IRPGs (IFIH1, CTSG, STC2, SECTM1, and BIRC5). Two groups (high- and low-risk patients) were identified based on the risk score. Low-risk patients with higher overall survival time had higher immune scores, more immune cell infiltration (e.g., CD8 T cell and activated natural killer cells), higher expression of immune-stimulating molecules, higher stimulating cytokines and corresponding receptors, higher innate immunity molecules, and stronger antigen-presenting capacity. However, inhibition of immunity was observed in low-risk patients owing to the higher expression of immune checkpoint molecules and inhibiting cytokines. High-risk patients had high tumor mutation burden, which did not significantly influence survival. Gene set enrichment analysis further revealed that pathways of cell cycle and cancers were activated in high-risk patients. DNA methylation analysis indicated that relative high methylation was associated with better overall survival. Finally, the age, mitotic counts, and risk scores were independent prognostic factors for STS. Five IRPGs performed well in risk stratification of patients and are candidate biomarkers for predicting response to immunotherapy. Differences observed through the multi-omic study of patients with different prognoses may reveal the underlying mechanism of the development and progression of STS, and thereby improve treatment.

Rh type C-glycoprotein functions as a novel tumor suppressor gene by inhibiting tumorigenicity and metastasis in head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC), a major histologic subtype of head and neck cancer, presents great mortality and morbidity worldwide. The aim of this study is to discover new potential biomarkers closely correlated with HNSCC progression. In this study, weighted gene co-expression network analysis was applied to construct a co-expression network, and the brown module was identified as the most correlated with HNSCC progression. Hub gene identification combined with survival analyses determined RHCG as a candidate biomarker for cancer progression and prognosis prediction. Further experimental results proved that RHCG was aberrantly downregulated in HNSCC tissues and cell lines. Moreover, decreased RHCG expression was shown to be associated with advanced stage and dismal prognosis in HNSCC patients. Functional assays revealed that RHCG could inhibit cell viability, clonogenicity, cell migration in vitro and suppress tumor formation in vivo. Further bioinformatics study demonstrated that DNA promoter hypermethylation of RHCG could lead to its downregulation and serve as potential prognostic maker in HNSCC. Our study reveals that RHCG acts as a tumor suppressor gene that plays a crucial role in inhibiting tumorigenicity and metastasis in HNSCC, which will shed light on the potential diagnostic and therapeutic strategies for HNSCC.

Weighted gene co-expression network analysis can sort cancer-associated fibroblast-specific markers promoting bladder cancer progression

The role of cancer-associated fibroblasts (CAFs) has been thoroughly investigated in tumour microenvironments but not in bladder urothelial carcinoma (BLCA). The cell fraction of CAFs gradually increased with BLCA progression. Weighted gene co-expression network analysis (WGCNA) revealed a specific gene expression module of CAFs that are relevant to cancer progression and survival status. Fifteen key genes of the module were consistent with a fibroblast signature in single-cell RNA sequencing, functionally related to the extracellular matrix, and significant in survival analysis and tumour staging. A comparison of the luminal-infiltrated versus luminal-papillary subtypes and fibroblast versus urothelial carcinoma cell lines and immunohistochemical data analysis demonstrated that the key genes were specifically expressed in CAFs. Moreover, these genes are highly correlated with previously reported CAF markers. In summary, CAFs play a major role in the progression of BLCA, and the 15 key genes act as BLCA-specific CAF markers and can predict CAF changes. WGCNA can, therefore, be used to sort CAF-specific gene set in cancer tissues.

Bioinformatics analysis shows that TOP2A functions as a key candidate gene in the progression of cervical cancer

Cervical cancer (CC) is one of the most prevalent types of cancer affecting females worldwide. However, the molecular mechanisms underlying the development and progression of CC remains to be elucidated. Taking the high incidence and mortality rates amongst women into consideration, the identification of novel biomarkers to prevent CC is of great significance and required to improve diagnosis. Using three raw microarray datasets from the Gene Expression Omnibus database, 188 differentially expressed genes (DEGs) were identified. Gene Ontology and pathway analyses were performed on the DEGs. Through protein-protein interaction network construction and module analysis, eight hub genes [cell division cycle 6, cyclin-dependent kinase 1 (CDK1), cell division control protein 45, budding uninhibited by benzimidazoles 1 (BUB1), DNA topoisomerase II α (TOP2A) and minichromosome maintenance complex component 4, CCNB2 and CCNB1] were identified, but only TOP2A was considered a prognostic factor in survival analysis. There were strong positive correlations between TOP2A and BUB1 (P<0.0001, rs=0.635), CDK1 (P<0.0001, rs=0.511), centromere protein F (CENPF) (P<0.0001, rs=0.677), Rac GTPase activating protein 1 (RACGAP1) (P<0.0001, rs=0.612), F-box protein 5 (FBXO5) (P<0.0001, rs=0.585) and BUB1 mitotic checkpoint serine/threonine kinase B (BUB1B) (P<0.0001, rs=0.584). Additionally, BUB1, CDK1, CENPF, RACGAP1, FBXO5 and BUB1B are all potentially suitable candidate targets for the diagnosis and treatment of CC. In conclusion, the present study identified TOP2A as a potential tumor oncogene and a biomarker for the prognosis of CC.

AEBP1 is a Novel Oncogene: Mechanisms of Action and Signaling Pathways

Adipocyte enhancer-binding protein 1 (AEBP1) is a transcriptional repressor involved in the regulation of critical biological processes including adipogenesis, mammary gland development, inflammation, macrophage cholesterol homeostasis, and atherogenesis. Several years ago, we first reported the ability of AEBP1 to exert a positive control over the canonical NF-κB pathway. Indeed, AEBP1 positively regulates NF-κB activity via its direct interaction with IκBα, a key NF-κB inhibitor. AEBP1 overexpression results in uncontrollable activation of NF-κB, which may have severe pathogenic outcomes. Recently, the regulatory relationship between AEBP1 and NF-κB pathway has been of great interest to many researchers primarily due to the implication of NF-κB signaling in critical cellular processes such as inflammation and cancer. Since constitutive activation of NF-κB is widely implicated in carcinogenesis, AEBP1 overexpression is associated with tumor development and progression. Recent studies sought to explore the effects of the overexpression of AEBP1, as a potential oncogene, in different types of cancer. In this review, we analyze the effects of AEBP1 overexpression in a variety of malignancies (e.g., breast cancer, glioblastoma, bladder cancer, gastric cancer, colorectal cancer, ovarian cancer, and skin cancer), with a specific focus on the AEBP1-mediated control over the canonical NF-κB pathway. We also underscore the ability of AEBP1 to regulate crucial cancer-related events like cell proliferation and apoptosis in light of other key pathways (e.g., PI3K-Akt, sonic hedgehog (Shh), p53, parthanatos (PARP-1), and PTEN). Identifying AEBP1 as a potential biomarker for cancer prognosis may lead to a novel therapeutic target for the prevention and/or treatment of various types of cancer.

Identifying multiple collagen gene family members as potential gastric cancer biomarkers using integrated bioinformatics analysis

Background

Gastric cancer is one of the most common malignant cancers worldwide. Despite substantial developments in therapeutic strategies, the five-year survival rate remains low. Therefore, novel biomarkers and therapeutic targets involved in the progression of gastric tumors need to be identified.

Methods

We obtained the mRNA microarray datasets GSE65801, GSE54129 and GSE79973 from the Gene Expression Omnibus database to acquire differentially expressed genes (DEGs). We used the Database for Annotation, Visualization, and Integrated Discovery (DAVID) to analyze DEG pathways and functions, and the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape to obtain the protein-protein interaction (PPI) network. Next, we validated the hub gene expression levels using the Oncomine database and Gene Expression Profiling Interactive Analysis (GEPIA), and conducted stage expression and survival analysis.

Results

From the three microarray datasets, we identified nine major hub genes: COL1A1, COL1A2, COL3A1, COL5A2, COL4A1, FN1, COL5A1, COL4A2, and COL6A3.

Conclusion

Our study identified COL1A1 and COL1A2 as potential gastric cancer prognostic biomarkers.

Loss of Spry1 reduces growth of BRAFV600-mutant cutaneous melanoma and improves response to targeted therapy

Mitogen-activated protein kinase (MAPK) pathway activation is a central step in BRAF^V600-mutant cutaneous melanoma (CM) pathogenesis. In the last years, Spry1 has been frequently described as an upstream regulator of MAPK signaling pathway. However, its specific role in BRAF^V600-mutant CM is still poorly defined. Here, we report that Spry1 knockdown (Spry1^KO) in three BRAF^V600-mutant CM cell lines markedly induced cell cycle arrest and apoptosis, repressed cell proliferation in vitro, and impaired tumor growth in vivo. Furthermore, our findings indicated that Spry1^KO reduced the expression of several markers of epithelial–mesenchymal transition, such as MMP-2 both in vitro and in vivo. These effects were associated with a sustained and deleterious phosphorylation of ERK1/2. In addition, p38 activation along with an increase in basal ROS levels were found in Spry1^KO clones compared to parental CM cell lines, suggesting that BRAF^V600-mutant CM may restrain the activity of Spry1 to avoid oncogenic stress and to enable tumor growth. Consistent with this hypothesis, treatment with the BRAF inhibitor (BRAFi) vemurafenib down-regulated Spry1 levels in parental CM cell lines, indicating that Spry1 expression is sustained by the MAPK/ERK signaling pathway in a positive feedback loop that safeguards cells from the potentially toxic effects of ERK1/2 hyperactivation. Disruption of this feedback loop rendered Spry1^KO cells more susceptible to apoptosis and markedly improved response to BRAFi both in vitro and in vivo, as a consequence of the detrimental effect of ERK1/2 hyperactivation observed upon Spry1 abrogation. Therefore, targeting Spry1 might offer a treatment strategy for BRAF^V600-mutant CM by inducing the toxic effects of ERK-mediated signaling.

Knockdown of CDCA8 inhibits the proliferation and enhances the apoptosis of bladder cancer cells

Bladder cancer is a tumour of the urinary system with high mortality, and there is also a great lack of therapeutic targets in the clinic. Cell division cycle associated 8 (CDCA8), an important component of the vertebrate chromosomal passenger complex, is highly expressed in various tumours and promotes tumour development. However, the role of CDCA8 in bladder cancer is not fully understood. This study aimed to reveal the function of CDCA8 in bladder cancer by determining the relationship between CDCA8 expression and proliferation, metastasis and apoptosis of bladder cancer cells. Firstly, we studied the mRNA expression of CDCA8 through the Gene Expression Omnibus (GEO) and the Cancer Genome Atlas (TCGA) databases and analysed the correlation between CDCA8 expression and prognosis of patients with bladder cancer. We also verified CDCA8 expression in bladder cancer tissues by immunohistochemistry. In addition, CDCA8 expression was inhibited in bladder cancer T24 and 5637 cells, and the effects of CDCA8 on the proliferation, migration and invasion of bladder cancer cell lines were investigated using cell counting kit-8, colony formation, cell cycle, apoptosis, wound healing and Transwell invasion assays. Results showed that CDCA8 was highly expressed in bladder cancer compared with normal tissues, and the high CDCA8 expression was significantly correlated with the poor prognosis of patients. Inhibiting CDCA8 expression inhibited the proliferation, migration and invasion of T24 and 5637 cells and induced the apoptosis of bladder cancer cells. CDCA8 was involved in the regulation of the growth cycle of bladder cancer cells. Bioinformatics-based mechanism analysis revealed that high CDCA8 expression may affect the cell cycle and P53 signalling pathways. In conclusion, our results suggest that CDCA8 is highly expressed in bladder cancer and can promote tumour development. Hence, CDCA8 may serve as an effective therapeutic target for treatment of bladder cancer.

LGALS4 as a Prognostic Factor in Urothelial Carcinoma of Bladder Affects Cell Functions

BACKGROUND

To identify the hub genes related to urothelial carcinoma of the bladder prognosis and to understand their underlying mechanism.

METHODS

The expression profiles of 18 pairs of urothelial carcinoma of the bladder patient tissue and paired adjacent tissue obtained from the Cancer Genome Atlas were performed. Weighted gene coexpression network analysis was employed to screen gene modules and hub genes with significant differential expressions in urothelial carcinoma of the bladder. The hub genes expression in urothelial carcinoma of the bladder tissues was validated by reverse transcription-quantitative polymerase chain reaction. The overall survival curve and disease-free survival curve of prognostic factor (LGALS4) were plotted using the Kaplan-Meier method. Furthermore, LGALS4 messenger RNA and protein expression were also assessed in 2 urothelial carcinoma of the bladder cell lines (T24 and 5637) by quantitative reverse transcription-polymerase chain reaction and Western blot. The functions of urothelial carcinoma of the bladder cells with transfected pcDNA3.1-LGALS4 were identified through MTT assay, plate clone formation assay, flow cytometry, and cell migration experiments.

RESULTS

LGALS4 was the hub gene of pink module and it was related to prognosis. Higher LGALS4 expression predicted higher probabilities of overall survival and disease-free survival. Overexpression of LGALS4 in urothelial carcinoma of the bladder cells suppressed cell viability and migration but induced apoptosis.

CONCLUSION

LGALS4 played a critical role in the progression of urothelial carcinoma of the bladder and held a promise to be the biomarker for diagnosis and treatment of urothelial carcinoma of the bladder. It predicted good prognosis of urothelial carcinoma of the bladder and restrained the growth and migration of urothelial carcinoma of the bladder cells.

Gene coexpression network analysis revealed biomarkers correlated with blast cells and survival in acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematological malignancy with a poorly understood pathogenesis, especially among patients with no known cytogenetic abnormalities. Furthermore, there is a lack of therapeutic gene targets and diagnostic biomarkers for the effective treatment of AML. The present study aimed to identify candidate biomarkers correlated with the clinical prognosis of patients with AML. Leukemic cells from 5 patients with AML exhibiting a normal karyotype, and hematopoietic cells from 5 healthy donors were processed for RNA sequencing (RNA-seq), and the obtained RNA expression profiles were subjected to weighted gene correlation network analysis. A novel group of genes (the red module) were identified to be significantly associated with AML, and this module contained a closely connected network with 147 nodes, which corresponded to 114 mRNAs. Analysis of the correlation between these mRNAs and blast cell percentage, overall survival (OS) and disease-free survival (DFS) using cases from The Cancer Genome Atlas (TCGA) database revealed that CSF3R, ALPL and LMTK2 were negatively associated with the percentage of blast cells, while high expression of these genes was associated with longer OS and DFS in patients with AML. The differential expression of these three genes between patients with AML and healthy control subjects was supported using the Genotype-Tissue Expression and TCGA databases and was further confirmed using reverse transcription-quantitative (RT-qPCR). These genes exhibited significantly lower expression in patients with AML compared with control subjects. The results indicated that CSF3R, ALPL and LMTK2 exhibit the potential to be prognostic biomarkers. However, the biological functions of these three candidate genes need to be assessed in further studies.

Analysis of the Expression of Cell Division Cycle-Associated Genes and Its Prognostic Significance in Human Lung Carcinoma: A Review of the Literature Databases

Background

Lung cancer (LC) has become the top cause responsible for cancer-related deaths. Cell division cycle-associated (CDCA) genes exert an important role in the life process. Dysregulation in the process of cell division may lead to malignancy.

Methods

Transcriptional data on CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases.

Results

CDCA1/2/3/4/5/7/8 expression levels were higher in lung adenocarcinoma tissues, and the CDCA1/2/3/4/5/6/7/8 expression levels were increased in squamous cell LC tissues compared with those in noncarcinoma lung tissues. The expression levels of CDCA1/2/3/4/5/8 showed correlation with tumor classification. The Kaplan-Meier Plotter database was employed to carry out survival analysis, indicating that increased CDCA1/2/3/4/5/6/7/8 expression levels were increased in squamous cell LC tissues compared with those in noncarcinoma lung tissues. The expression levels of P < 0.05). Only LC patients with increased CDCA3/4/5/8 expression were significantly related to lower post-progression survival (PPS) (P < 0.05). Only LC patients with increased CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA8, INCENP, AURKB, and BIRC5); CORUM: 127: NDC80 kinetochore complex; M129: the PID PLK1 pathway; and GO: 0007080: mitotic metaphase plate congression, all of which were remarkably modulated since the alterations affected CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases.

Conclusions

Upregulated CDCA genes' expression levels in LC tissues probably play a crucial part in LC oncogenesis. The upregulated CDCA genes' expression levels are used as the potential prognostic markers to improve patient survival and the LC prognostic accuracy. CDCA genes probably exert their functions in tumorigenesis through the PLK1 pathway.CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases. CDCA gene family and patient survival data were examined for lung cancer (LC) patients from the GEPIA, Oncomine, cBioPortal, and Kaplan-Meier Plotter databases.

Effects of hub genes on the clinicopathological and prognostic features of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is a common malignancy; however, the majority of its underlying molecular mechanisms remain unknown. In the present study, weighted gene co-expression network analysis was applied to construct gene co-expression networks for the GSE19804 dataset, in order to screen hub genes associated with the pathogenesis of LUAD. In addition, with the aid of the Database for Annotation, Visualization and Integrated Discovery, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes, pathway enrichment analyses were performed on the genes in the selected module. Using the GSE40791 dataset and The Cancer Genome Atlas database, the hub genes were identified. It was discovered that the turquoise module was the most significant module associated with the tumor stage of LUAD. After performing functional enrichment analyses, it was indicated that the turquoise module was mainly enriched in signal transduction. Additionally, at the transcriptional and translational level, nine hub genes were identified and validated: Carbonic anhydrase 4 (CA4), platelet and endothelial cell adhesion molecule 1 (PECAM1), DnaJ member B4 (DNAJB4), advanced glycosylation end-product specific receptor (AGER), GTPase, IMAP family member 6 (GIMAP6), chromosome 10 open reading frame 54 (C10orf54), dedicator of cytokinesis 4 (DOCK4), Golgi membrane protein 1 (GOLM1) and platelet activating factor acetylhydrolase 1b catalytic subunit 3 (PAFAH1B3). CA4, PECAM1, DNAJB4, AGER, GIMAP6, C10orf54 and DOCK4 were expressed at lower levels in the tumor samples, whereas GOLM1 and PAFAH1B3 were highly expressed in tumor samples. In addition, all hub genes were associated with prognosis. In conclusion, one module and nine genes were recognized to be associated with the tumor stage of LUAD. These findings may enhance the understanding of the progression and prognosis of LUAD.

Identification of gene modules and hub genes in colon adenocarcinoma associated with pathological stage based on WGCNA analysis

Colorectal cancer (CRC) is one of the leading causes of cancer-related mortality in the world, in which colon adenocarcinoma (COAD) is the most common histological subtype of CRC. In this study, our aim is to identify gene modules and representative candidate biomarkers for clinical prognosis of patients with COAD, and help to predict prognosis and reveal the mechanisms of cancer progression. Weighted gene co-expression network analysis (WGCNA) was performed to construct a co-expression network and identify gene modules correlated with TNM clinical staging of COAD patients. The Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed with the module gene. Protein-protein interaction (PPI) network and hub gene identification were explored with Cytoscape software. Finally, the hub gene mRNA level was validated in Oncomine database. Five gene modules, related with the pathological TNM stage, were constructed, and the gene module was enriched in cell proliferation, invasion and migration related GO terms and metabolic related KEGG pathways. A total of top 10 hub genes was identified, and in which six of the hub genes show a significant up-regulation in COAD as compared to normal tissue, including IVL, KRT16, KRT6C, KRT6A, KRT78 and SBSN. In conclusion, we identified five gene modules and six candidate biomarkers correlated with the TNM staging of COAD patients. These findings may help us to understand the tumor progression of COAD and provide prognostic biomarkers as well as therapeutic targets.

Construction and validation of an eight-gene signature with great prognostic value in bladder cancer

Bladder cancer (BC) is one of the most common malignancies in urinary system with a common malignancy in urinary system with a high mortality and recurrence rate, so we attempt to construct a gene signature to predict the prognosis of BCs. We initially established a co-expression network by performing WGCNA analysis and further identified magenta module as key module (P = 8e-05, R2 = 0.4). Subsequently, we screened 12 genes associated with survival from the key module, which were selected to construct an eight-gene signature by establishing a LASSO Cox model. Moreover, we reckoned the risk score (RS) of each sample, through which we could divide samples into two groups (the high-risk and low-risk groups) and verify the signature, in the training set and 3 validation sets (internal test set, GSE13507and E-MTAB-4321). This signature could distinguish between the high- and low- risk patients well (survival analysis: P = 0.015; AUC: 0.61 at 1 year, 0.61 at 3 years and 0.61 at 5 years). In the validation sets, this signature also showed good performance, which was consistent with the training test. Furthermore, we plotted a nomogram to predict the possibility of the overall survival (OS) and three calibration curves to predict the effectiveness of the nomogram, which suggested good value and clinical utility of the nomogram. In conclusion, we established an eight-gene signature, which was probably effective in the prediction of prognosis of patients with BC.

Impact of Matrix Metalloproteinases 11 Gene Variants on Urothelial Cell Carcinoma Development and Clinical Characteristics

Urothelial cell carcinoma (UCC) is one of the lethal causes of cancer mortality of the genitourinary tract. Carcinogenic epidemiological risk factors exposure and age over 65 years old are associated with UCC risk. Matrix metalloproteinase 11 (MMP11) was suggested as a tumor marker of metastasis and predictor of poor survival in urothelial carcinomas. In this study, we focused on the associations of MMP11 single-nucleotide polymorphisms (SNPs) to UCC susceptibility, clinicopathological characteristics, and prognosis. In this study, real-time polymerase chain reaction was used to analyze five SNPs of MMP11 rs738791, rs2267029, rs738792, rs28382575, and rs131451 in 431 patients with UCC and 650 cancer-free controls. The MMP11 rs28382575 polymorphic “CT” genotype were susceptible to UCC (AOR = 2.045, 95% CI = 1.088 − 3.843; p = 0.026). For MMP11 rs131451, a significant association was found in 166 UCC patients among age ≤ 65 years old who carried MMP11 rs131451 polymorphic “CC” genotype, which is associated with lower risk to develop later tumor T status (T1-T4) (OR = 0.375, 95% CI = 0.159 − 0.887; p = 0.026) compared with the (CT + TT) genotype. Furthermore, patients of UCC with rs738792 polymorphic “CC” genotype were observed to have higher free of relapse (FS) (p = 0.035), disease specific survival rate (p = 0.037), and overall survival rate (p = 0.009) compared with the rs738792 (CT + CC) genotype. In conclusion, our results demonstrated that the MMP11 SNPs are associated with UCC susceptibility, clinical status, and disease survival. The MMP11 polymorphisms may have potential to predict UCC susceptibility and prognosis.

A Weighted Gene Co-expression Network Analysis Reveals lncRNA Abnormalities in the Peripheral Blood Associated With Ultra-High-Risk for Psychosis

Objective: The primary study aim was to identify long non-coding RNA (lncRNA) abnormalities associated with ultra-high-risk (UHR) for psychosis based on a weighted gene co-expression network analysis. Methods: UHR patients were screened by the structured interview for prodromal syndromes (SIPS). We performed a WGCNA analysis on lncRNA and mRNA microarray profiles generated from the peripheral blood samples in 14 treatment-seeking patients with UHR who never received psychiatric medication and 18 demographically matched typically developing controls. Gene Ontology (GO) analysis and canonical correlation analysis were then applied to reveal functions and correlation between lncRNAs and mRNAs. Results: The lncRNAs were organized into co-expressed modules by WGCNA, two modules of which were strongly associated with UHR. The mRNA networks were constructed and two disease-associated mRNA modules were identified. A functional enrichment analysis showed that mRNAs were highly enriched for immune regulation and inflammation. Moreover, a significant correlation between lncRNAs and mRNAs were verified by a canonical correlation analysis. Conclusion: We identified novel lncRNA modules related to UHR. These results contribute to our understanding of the molecular basis of UHR from the perspective of systems biology and provide a theoretical basis for early intervention in the assumed development of schizophrenia.

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co-expression network analysis

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.

Adipocyte enhancer binding protein 1 (AEBP1) knockdown suppresses human glioma cell proliferation, invasion and induces early apoptosis

BACKGROUND

Glioma is the most common primary malignant tumor with poor prognosis due to the lack of understanding the mechanism underlying the disease and the early diagnosis indexs. It is necessary to identify molecular signatures for predicting the overall prognosis of glioma. Adipocyte enhancer binding protein1 (AEBP1) acts as a transcriptional repressor and plays a role in adipogenesis and smooth muscle cell differentiation. However, its role in glioma remains unclear.

MATERIALS AND METHODS

AEBP1 expression was analyzed by bioinformatics using the public database and by qPCR and western blotting in human glioma tissues. AEBP1 downregulation was performed by lipofectamine3000-mediated siRNA transfection. Cell proliferation and invasion were determined by cell counting kit-8 and transwell assays, while early cell apoptosis was determined by flow cytometry. The proteins of downstream NF-κB signaling pathway were determined by western blotting.

RESULTS

AEBP1 is highly expressed in human gliomas. Lipofectamine 3000-mediated siRNA transfection stably and efficiently suppressed AEBP1 mRNA and protein expression in human glioma cells. AEBP1 downregulation inhibited cell proliferation and invasion, but promoted early cell apoptosis. Also, AEBP1 knockdown in glioma cells decreased the expression of NF-κB1. Furthermore, the downstream of NF-κB signaling pathway, Bax, caspase-3 are increased, while MMP2 and Bcl-2 are decreased in glioma cells.

CONCLUSION

Elevated AEBP1 is positively associated with poor prognosis of glioma. AEBP1 downregulation suppressed cell proliferation and invasion, but promoted early cell apoptosis. AEBP1 downregulation suppressed the cell proliferation and invasion may by inhibiting the NF-κB signaling pathway.

Weighted Gene Co-Expression Network Analysis Identifies Critical Genes in the Development of Heart Failure After Acute Myocardial Infarction

Background: The development of heart failure (HF) remains a common complication following an acute myocardial infarction (AMI), and is associated with substantial adverse outcomes. However, the specific predictive biomarkers and candidate therapeutic targets for post-infarction HF have not been fully established. We sought to perform a weighted gene co-expression network analysis (WGCNA) to identify key modules, hub genes, and possible regulatory targets involved in the development of HF following AMI.

Methods: Genes exhibiting the most (top 50%) variation in expression levels across samples in a GSE59867 dataset were imported to the WGCNA. Gene Ontology and pathway enrichment analyses were performed on genes identified in the key module by Metascape. Gene regulatory networks were constructed using the microarray probe reannotation and bioinformatics database. Hub genes were screened out from the key module and validated using other datasets.

Results: A total of 10,265 most varied genes and six modules were identified between AMI patients who developed HF within 6 months of follow-up and those who did not. Specifically, the blue module was found to be the most significantly related to the development of post-infarction HF. Functional enrichment analysis revealed that the blue module was primarily associated with the inflammatory response, immune system, and apoptosis. Seven transcriptional factors, including SPI1, ZBTB7A, IRF8, PPARG, P65, KLF4, and Fos, were identified as potential regulators of the expression of genes identified in the blue module. Further, non-coding RNAs, including miR-142-3p and LINC00537, were identified as having close interactions with genes from the blue module. A total of six hub genes (BCL3, HCK, PPIF, S100A9, SERPINA1, and TBC1D9B) were identified and validated for their predictive value in identifying future HFs.

Conclusions: By using the WGCNA, we provide new insights into the underlying molecular mechanism and molecular markers correlated with HF development following an AMI, which may serve to improve risk stratification, therapeutic decisions, and prognosis prediction in AMI patients.