Identification of key candidate genes and biological pathways in bladder cancer

Identification of cuproptosis-related long non-coding RNA and construction of a novel prognostic signature for bladder cancer: An observational study

Bladder Urothelial Carcinoma (BLCA), a prevalent and lethal cancer, lacks understanding regarding the roles and prognostic value of cuproptosis-related lncRNAs (CRLs), a novel form of cell death induced by copper. We collected RNA-seq data, clinical information, and prognostic data for 414 BLCA samples and 19 matched controls from The Cancer Genome Atlas. Using multivariate and univariate Cox regression analyses, we identified CRLs to create a prognostic signature. Patients were then divided into low- and high-risk groups based on their risk scores. We analyzed overall survival using the Kaplan-Meier method, evaluated stromal and immune scores, and explored functional differences between these risk groups with gene set enrichment analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were also conducted to understand the links between CRLs and BLCA development. We developed a prognostic signature using 4 independent CRLs: RC3H1-IT1, SPAG5-AS1, FAM13A-AS1, and GNG12-AS1. This signature independently predicted the prognosis of BLCA patients. High-risk patients had worse outcomes, with gene set enrichment analysis revealing enrichment in tumor- and immune-related pathways in the high-risk group. Notably, high-risk patients exhibited enhanced responses to immunotherapy and conventional chemotherapy drugs like sunitinib, paclitaxel, and gemcitabine. The independent prognostic signature variables RC3H1-IT1, SPAG5-AS1, FAM13A-AS1, and GNG12-AS1 predicted the prognoses of BLCA patients and provided a basis for the study of the mechanism of CRLs in BLCA development and progression, and the guidance of clinical treatments for patients with BLCA.

[Identification of key genes in Wilms tumor based on high-throughput RNA sequencing and their impacts on prognosis and immune responses]

OBJECTIVE

To identify the key genes differentially expressed in Wilms tumor and analyze their potential impacts on prognosis and immune responses of the patients.

METHODS

High-throughput RNA sequencing was used to identify the differentially expressed mRNAs in clinical samples of Wilms tumor and paired normal tissues, and their biological functions were analyzed using GO, KEGG and GSEA enrichment analyses. The hub genes were identified using STRING database, based on which a prognostic model was constructed using LASSO regression. The mutations of the key hub genes were analyzed and their impacts on immunotherapy efficacy was predicted using the cBioPortal platform. RT-qPCR was used to verify the differential expressions of the key hub genes in Wilms tumor.

RESULTS

Of the 1612 differentially expressed genes identified in Wilms tumor, 1030 were up-regulated and 582 were down-regulated, involving mainly cell cycle processes and immune responses. Ten hub genes were identified, among which 4 genes (TP53, MED1, CCNB1 and EGF) were closely related to the survival of children with Wilms tumor. A 3-gene prognostic signature was constructed through LASSO regression analysis, and the patients stratified into with high- and low-risk groups based on this signature had significantly different survival outcomes (HR=1.814, log-rank P=0.002). The AUCs of the 3-, 5- and 7-year survival ROC curves of this model were all greater than 0.7. The overall mutations in the key hub genes or the individual mutations in TP53/CCNB1 were strongly correlated with a lower survival rates, and a high TP53 expression was correlated with a poor immunotherapy efficacy. RT-qPCR confirmed that the key hub genes had significant differential expressions in Wilms tumor tissues and cells.

CONCLUSION

TP53 gene plays an important role in the Wilms tumor and may potentially serve as a new immunotherapeutic biomarker as well as a therapeutic target.

Comprehensive analysis and validation reveal DEPDC1 as a potential diagnostic biomarker associated with tumor immunity in non-small-cell lung cancer

Current evidence suggests that DEP domain containing 1 (DEPDC1) has an important effect on non-small-cell lung cancer (NSCLC). However, the diagnostic value and the regulatory function within NSCLC are largely unclear. This work utilized publicly available databases and in vitro experiments for exploring, DEPDC1 expression, clinical features, diagnostic significance and latent molecular mechanism within NSCLC. According to our results, DEPDC1 was remarkably upregulated in the tissues of NSCLC patients compared with non-carcinoma tissues, linked with gender, stage, T classification and N classification based on TCGA data and associated with smoking status and stage according to GEO datasets. Meanwhile, the summary receiver operating characteristic (sROC) curve analysis result showed that DEPDC1 had a high diagnostic value in NSCLC (AUC = 0.96, 95% CI: 0.94-0.98; diagnostic odds ratio = 99.08, 95%CI: 31.91-307.65; sensitivity = 0.89, 95%CI: 0.81-0.94; specificity = 0.92, 95%CI: 0.86-0.96; positive predictive value = 0.94, 95%CI: 0.89-0.98; negative predictive value = 0.78, 95%CI: 0.67-0.90; positive likelihood ratio = 11.77, 95%CI: 6.11-22.68; and negative likelihood ratio = 0.12, 95%CI: 0.06-0.22). Subsequently, quantitative real-time PCR (qRT-PCR) and western blotting indicated that DEPDC1 was high expressed in NSCLC cells. According to the in vitro MTS and apoptotic assays, downregulated DEPDC1 expression targeting P53 signaling pathway inhibited the proliferation of NSCLC cells while promoting apoptosis of NSCLC cells. Moreover, DEPDC1 was significantly correlated with immune cell infiltrating levels in NSCLC based on TCGA data, which were primarily associated with T cells CD4 memory activated, macrophages M1, B cells memory, mast cells resting, T cells regulatory, monocytes, and T cells CD4 memory resting. Compared with the group with high expression of DEPDC1, the group with low expression level had higher scores for immune checkpoint inhibitors (ICIs) treatment. GSEA confirmed that DEPDC1 was involved in gene expression and tumor-related signaling pathways. Finally, DEPDC1 and its associated immune-related genes were shown to be enriched in 'receptor ligand activity', 'external side of plasma membrane', 'regulation of innate immune response', and 'Epstein-Barr virus infection' pathways. The present study demonstrates that DEPDC1 may contribute to NSCLC tumorigenesis and can be applied as the biomarker for diagnosis and immunology.

Bioinformatics analysis highlights CCNB1 as a potential prognostic biomarker and an anti-kidney renal papillary cell carcinoma drug target

Kidney renal papillary cell carcinoma (KIRP) is a common urinary tumor that causes lymph node invasion. Once metastatic, the prognosis is poor and there is a lack of effective early diagnostic markers for this tumor. The expression of CCNB1 in KIRP tumor tissues was significantly higher than that in normal tissues in The Cancer Genome Atlas database with or without the genotype-tissue expression database, and a consistent result was obtained in 32 paired tissues. In addition, CCNB1 expression increased remarkably with the progression of the T and M stages. Moreover, using the online HPA database, we verified that the immunohistochemical scores of CCNB1 in KIRP were higher than those in the normal kidney tissues. The higher expression group of CCNB1 showed a worse prognosis in KIRP. Moreover, the receiver operating characteristic curve, univariate and multivariate analyses, and construction of the column diagram further illustrated that CCNB1 was an independent prognostic factor for KIRP. Meanwhile, CCNB1 could better predict the 1- and 3-year survival rates of KIRP. Six genes were significantly and positively co-expressed with CCNB1. We also found that the CCNB1 high-expression group was enriched in the ECM_RECEPTOR_INTERACTION and FOCAL_ADHESION pathways. Finally, drug sensitivity analysis combined with molecular docking identified 5 targeting drugs with the strongest binding activity to CCNB1. CCNB1 is a potential and reliable biomarker for KIRP diagnosis and can be used to predict the survival of patients with KIRP. The 5 selected drugs targeting CCNB1 may provide new hopes for patients with KIRP metastasis.

The Prognosis and Immunotherapy Prediction Model of Ovarian Serous Cystadenocarcinoma Patient was Constructed Based on Cuproptosis-Related LncRNA

Cuproptosis can serve as potential prognostic predictors in patients with cancer. However, the role of this relationship in ovarian serous cystadenocarcinoma (OV) remains unclear. 376 OV tumor samples were obtained from the Cancer Genome Atlas (TCGA) database, and long non-coding RNAs (lncRNAs) related to cuproptosis were obtained through correlation analysis. The risk assessment model was further constructed by univariate Cox regression analysis and LASSO Cox regression. Bioinformatics was used to analyze the regulatory effect of relevant risk assessment models on tumor mutational burden (TMB) and immune microenvironment. We obtained 5 lncRNAs (AC025287.2, AC092718.4, AC112721.2, LINC00996, and LINC01639) and incorporated them into the Cox proportional hazards model. Kaplan-Meier (KM) curve analysis of the prognosis found that the high-risk group was associated with a poorer prognosis. The receiver operating characteristic (ROC) curve showed stronger predictive power compared to other clinicopathological features. Immune infiltration analysis showed that high-risk scores were inversely correlated with CD8+ T cells, CD4+ T cells, macrophages, NK cells, and B cells. Functional enrichment analysis found that they may act via the extracellular matrix (ECM)-interacting proteins and other pathways. We successfully constructed a reliable cuproptosis-related lncRNA model for the prognosis of OV.

Optimizing drug combination and mechanism analysis based on risk pathway crosstalk in pan cancer

Combination therapy can greatly improve the efficacy of cancer treatment, so identifying the most effective drug combination and interaction can accelerate the development of combination therapy. Here we developed a computational network biological approach to identify the effective drug which inhibition risk pathway crosstalk of cancer, and then filtrated and optimized the drug combination for cancer treatment. We integrated high-throughput data concerning pan-cancer and drugs to construct miRNA-mediated crosstalk networks among cancer pathways and further construct networks for therapeutic drug. Screening by drug combination method, we obtained 687 optimized drug combinations of 83 first-line anticancer drugs in pan-cancer. Next, we analyzed drug combination mechanism, and confirmed that the targets of cancer-specific crosstalk network in drug combination were closely related to cancer prognosis by survival analysis. Finally, we save all the results to a webpage for query ( http://bio-bigdata.hrbmu.edu.cn/oDrugCP/ ). In conclusion, our study provided an effective method for screening precise drug combinations for various cancer treatments, which may have important scientific significance and clinical application value for tumor treatment.

High Stretch Modulates cAMP/ATP Level in Association with Purine Metabolism via miRNA-mRNA Interactions in Cultured Human Airway Smooth Muscle Cells

High stretch (>10% strain) of airway smooth muscle cells (ASMCs) due to mechanical ventilation (MV) is postulated to contribute to ventilator-induced lung injury (VILI), but the underlying mechanisms remain largely unknown. We hypothesized that ASMCs may respond to high stretch via regulatory miRNA-mRNA interactions, and thus we aimed to identify high stretch-responsive cellular events and related regulating miRNA-mRNA interactions in cultured human ASMCs with/without high stretch. RNA-Seq analysis of whole genome-wide miRNAs revealed 12 miRNAs differentially expressed (DE) in response to high stretch (7 up and 5 down, fold change >2), which target 283 DE-mRNAs as identified by a parallel mRNA sequencing and bioinformatics analysis. The KEGG and GO analysis further indicated that purine metabolism was the first enriched event in the cells during high stretch, which was linked to miR-370-5p-PDE4D/AK7. Since PDE4D/AK7 have been previously linked to cAMP/ATP metabolism in lung diseases and now to miR-370-5p in ASMCs, we thus evaluated the effect of high stretch on the cAMP/ATP level inside ASMCs. The results demonstrated that high stretch modulated the cAMP/ATP levels inside ASMCs, which could be largely abolished by miR-370-5p mimics. Together, these findings indicate that miR-370-5p-PDE4D/AK7 mediated high stretch-induced modulation of cAMP and ATP synthesis inside ASMCs. Furthermore, such interactive miRNA-mRNA pairs may provide new insights for the discovery of effective biomarkers/therapeutic targets for the diagnosis and treatment of VILI and other MV-associated respiratory diseases.

IL1A regulates the inflammation in gout through the Toll-like receptors pathway

Objective: Gout is a dangerous metabolic condition related to monosodium urate (MSU). Our aim is to study the molecular mechanisms underlying gout and to identify potential clinical biomarkers by bioinformatics analysis and experimental validation. Methods: In this study, we retrieved the overlapping genes between GSE199950-Differential Expressed Genes (DEGs) dataset and key module in Weighted Gene Co-Expression Network Analysis (WGCNA) on GSE199950. These genes were then analyzed by protein-protein interaction (PPI) network, expression and Gene Set Enrichment Analysis to identify the hub gene related to gout. Then, the gene was investigated by peripheral blood mononuclear cells (PBMCs), immunoassay and cell experiments like western blotting to uncover its underlying mechanism in gout cells. Results: From the turquoise module and 83 DEGs, we identified 62 overlapping genes, only 11 genes had mutual interactions in PPI network and these genes were highly expressed in MSU-treated samples. Then, it was found that the IL1A (interleukin 1 alpha) was the only one gene related to Toll-like receptor signaling pathway that was associated with the occurrence of gout. Thus, IL1A was determined as the hub gene in this study. In immunoassay, IL1A was significantly positively correlated with B cells and negatively correlated with macrophages. Moreover, IL1A is highly expressed in gout patients,it has a good clinical diagnostic value. Finally, the results of in vitro experiments showed that after knocking down IL1A, the expressions of pro-inflammatory cytokines and Toll-like receptor signaling pathway-related proteins (TLR2, TLR4, MyD88) were all reduced. Conclusion: It is confirmed that IL1A is a promoting gene in gout with a good diagnostic value, and specifically it affects the inflammation in gout through Toll-like receptor pathway. Our research offers fresh perspectives on the pathophysiology of gout and valuable directions for future diagnosis and treatment.

Actin Alpha 2, Smooth Muscle (ACTA2) Is Involved in the Migratory Potential of Malignant Gliomas, and Its Increased Expression at Recurrence Is a Significant Adverse Prognostic Factor

Malignant glioma is a highly invasive tumor, and elucidating the glioma invasion mechanism is essential for developing novel therapies. We aimed to highlight actin alpha 2, smooth muscle (ACTA2) as potential biomarkers of brain invasion and distant recurrence in malignant gliomas. Using the human malignant glioma cell line, U251MG, we generated ACTA2 knockdown (KD) cells treated with small interfering RNA, and the cell motility and proliferation of the ACTA2 KD group were analyzed. Furthermore, tumor samples from 12 glioma patients who underwent reoperation at the time of tumor recurrence were utilized to measure ACTA2 expression in the tumors before and after recurrence. Thereafter, we examined how ACTA2 expression correlates with the time to tumor recurrence and the mode of recurrence. The results showed that the ACTA2 KD group demonstrated a decline in the mean motion distance and proliferative capacity compared to the control group. In the clinical glioma samples, ACTA2 expression was remarkably increased in recurrent samples compared to the primary samples from the same patients, and the higher the change in ACTCA2 expression from the start to relapse, the shorter the progression-free survival. In conclusion, ACTA2 may be involved in distant recurrence in clinical gliomas.

Screening of immunotherapy-related genes in bladder cancer based on GEO datasets

Background

As one of the most prevalent genitourinary cancers, bladder cancer (BLCA) is associated with high morbidity and mortality. Currently, limited indicators are available for early detection and diagnosis of bladder cancer, and there is a lack of specific biomarkers for evaluating the prognosis of BLCA patients. This study aims to identify critical genes that affect bladder cancer immunity to improve the diagnosis and prognosis of bladder cancer and to identify new biomarkers and targets for immunotherapy.

Methods

Two GEO datasets were used to screen differentially expressed genes (DEGs). The STRING database was used to construct a protein-protein interaction network of DEGs, and plug-in APP CytoHubba in Cytoscape was used to identify critical genes in the network. GO and KEGG analyses explored the functions and pathways of differential gene enrichment. We used GEPIA to validate the expression of differential genes, their impact on patient survival, and their relationship to clinicopathological parameters. Additionally, hub genes were verified using qRT-PCR and Western blotting. Immune infiltration analysis and multiple immunohistochemistry reveal the impact of Hub genes on the tumor microenvironment.

Result

We screened out 259 differential genes, and identified 10 key hub genes by the degree algorithm. Four genes (ACTA2, FLNA, TAGLN, and TPM1) were associated with overall or disease-free survival in BLCA patients and were significantly associated with clinical parameters. We experimentally confirmed that the mRNA and protein levels of these four genes were significantly decreased in bladder cancer cells. Immunoassays revealed that these four genes affect immune cell infiltration in the tumor microenvironment; they increased the polarization of M2 macrophages.

Conclusion

These four genes affect the tumor microenvironment of bladder cancer, provide a new direction for tumor immunotherapy, and have significant potential in the diagnosis and prognosis of bladder cancer.

Revealing platelet-related subtypes and prognostic signature in pancreatic adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PDAC) is a malignant tumor with high heterogeneity and poor prognosis. In this study, we sought to identify the value of platelet-related genes in prognosis and heterogeneity of PDAC through multiple transcriptomic methods.

METHODS

Based on datasets from Gene Expression Omnibus and The Cancer Genome Atlas (TCGA), platelet-related genes were screened out, and the TCGA cohort (n = 171) was identified into two subtypes by unsupervised clustering. The platelet-related risk score model (PLRScore) was constructed by univariate Cox and LASSO regression, and the predictive ability was evaluated by Kaplan-Meier test and time-dependent receiver operating characteristic (ROC) curves. The results were validated in two other external validation sets, ICGC-CA (n = 140) and GSE62452 (n = 66). Furthermore, predictive nomogram containing clinical characteristics and PLRScore was established. In addition, we determined the possible correlation between PLRScore and immune infiltration and response of immunotherapy. Finally, we analyzed the heterogeneity of our signature in various types of cells using single-cell analysis.

RESULTS

Platelet-related subtypes that have significant difference of overall survival and immune states (p < 0.05) were identified. PLRScore model based on four-gene signature (CEP55, LAMA3, CA12, SCN8A) was constructed to predict patient prognosis. The AUCs of training cohort were 0.697, 0.687 and 0.675 for 1-, 3-and 5-year, respectively. Further evaluation of the validation cohorts yielded similar results. In addition, PLRScore was associated with immune cell infiltration and immune checkpoint expression, and had promising ability to predict response to immunotherapy of PDAC.

CONCLUSIONS

In this study, the platelet-related subtypes were identified and the four-gene signature was constructed and validated. It may provide new insights into the therapeutic decision-making and molecular targets of PDAC.

Mechanical Ventilation-Related High Stretch Mainly Induces Endoplasmic Reticulum Stress and Thus Mediates Inflammation Response in Cultured Human Primary Airway Smooth Muscle Cells

Ventilator-induced lung injury (VILI) occurs in mechanically ventilated patients of respiratory disease and is typically characterized by airway inflammation. However, recent studies increasingly indicate that a major cause of VILI may be the excessive mechanical loading such as high stretch (>10% strain) on airway smooth muscle cells (ASMCs) due to mechanical ventilation (MV). Although ASMCs are the primary mechanosensitive cells in airways and contribute to various airway inflammation diseases, it is still unclear how they respond to high stretch and what mediates such a response. Therefore, we used whole genome-wide mRNA-sequencing (mRNA-Seq), bioinformatics, and functional identification to systematically analyze the mRNA expression profiles and signaling pathway enrichment of cultured human ASMCs exposed to high stretch (13% strain), aiming to screen the susceptible signaling pathway through which cells respond to high stretch. The data revealed that in response to high stretch, 111 mRNAs with count ≥100 in ASMCs were significantly differentially expressed (defined as DE-mRNAs). These DE-mRNAs are mainly enriched in endoplasmic reticulum (ER) stress-related signaling pathways. ER stress inhibitor (TUDCA) abolished high-stretch-enhanced mRNA expression of genes associated with ER stress, downstream inflammation signaling, and major inflammatory cytokines. These results demonstrate in a data-driven approach that in ASMCs, high stretch mainly induced ER stress and activated ER stress-related signaling and downstream inflammation response. Therefore, it suggests that ER stress and related signaling pathways in ASMCs may be potential targets for timely diagnosis and intervention of MV-related pulmonary airway diseases such as VILI.

Single-nucleus RNA and ATAC sequencing uncovers the molecular and cellular characteristics in the musk gland of Chinese forest musk deer (Moschus berezovskii)

The Chinese forest musk deer (FMD; Moschus berezovskii) is an endangered artiodactyl mammal. Musk secreted by the musk gland of male has extremely high economic and medicinal value. However, the molecular and cellular characteristics of the musk gland have not been studied. Here, we investigated the diversity and transcriptional composition of musk gland cell types and the effect of cell type-specific chromatin accessibility on gene expression using single-nucleus RNA sequencing (snRNA-seq) and single-nucleus ATAC sequencing (snATAC-seq) association analysis. Based on uniform manifold approximation and projection (UMAP) analysis, we identified 13 cell types from the musk gland, which included two different acinar cells (cluster 0 and cluster 10). Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis indicated that many pathways related to musk secretion were enriched in acinar cells. Our analysis also revealed acinar cell core transcription factors and core target genes, and further constructed acinar cell-specific regulatory networks. In cluster 0, 11 core target genes (Nedd4l, Adcy9, Akr1c1, Vapb, Me1, Acsl1, Acss3, Srd5a1, Scnn1a, Acadm, and Nceh1) possibly related to musk secretion were regulated by 24 core transcription factors (SP3, NFIC, NR6A1, EHF, RUNX1, TFAP2A, RREB1, GRHL2, NFIB, ELF1, MAX, KLF5, REL, HES1, POU2F3, TFDP1, NR2C1, ATF7, MEIS1, NR4A2, NFIA, PBX1, ZNF652, and NFKB1). In cluster 10, four core target genes (Akr1c1, Pcca, Atp1b1, and Sgk1) possibly related to musk secretion were regulated by 10 core transcription factors (BARX2, EHF, PBX1, RUNX1, NFIB, FOXP1, KLF3, KLF6, ETV6, and NR3C2). Moreover, the credibility of snRNA-seq and snATAC-seq data was verified by fluorescence in situ hybridization and immunohistochemistry. Finally, cell communication analysis demonstrated that the two types of acinar cells mainly have communications in musk secretion-related processes. In conclusion, we provided important insights and invaluable resources for the molecular and cellular characteristics of the musk gland, which will lay a foundation for the study of musk secretion mechanism in the future.

Identification of EMP1 as a critical gene for cisplatin resistance in ovarian cancer by using integrated bioinformatics analysis

BACKGROUND

Cisplatin resistance is among the main reasons for the poor prognosis of ovarian cancer (OC) patients. Until now, effective biomarkers for predicting cisplatin resistance in OC and specific drugs for reversing this resistance are lacking. This study identified the critical gene associated with cisplatin resistance in OC and provided a potential target for overcoming this resistance.

METHODS

Differentially expressed genes between cisplatin-resistant and -sensitive OCs were identified by screening public datasets. Survival analysis was conducted to screen prognosis-related DEGs. CIBERSORT, ESTIMATE, and immune checkpoint genes were used to assess the association between EMP1 expression and tumor microenvironment features. CTRP and GDSC databases were employed to analyze the correlation between EMP1 expression and cisplatin resistance. Furthermore, immunohistochemistry, qPCR, Western blotting, siRNA interference, and the CCK8 assay were performed to verify the role of EMP1 in cisplatin resistance in vitro. Finally, xenograft mouse models were generated to further confirm the role of EMP1 in cisplatin resistance in vivo.

RESULTS

EMP1 was identified as a critical gene associated with cisplatin resistance in OC. According to bioinformatics analyses, increased EMP1 expression was linked to higher stromal/ESTIMATE scores as well as greater ICG expression levels. The in vitro experiments showed that EMP1 was highly expressed in cisplatin-resistant OC tissues and cells, and silencing this EMP1 expression enhanced OC cell sensitivity to cisplatin. Finally, in vivo experiments confirmed that EMP1 promotes tumor growth and cisplatin resistance.

CONCLUSIONS

EMP1 can act as a predictive biomarker for cisplatin resistance in OC and as a potential therapeutic target.

Identification of potential biomarkers for papillary thyroid carcinoma by comprehensive bioinformatics analysis

To perform bioinformatics analysis on the papillary thyroid carcinoma (PTC) gene chip dataset to explore new biological markers for PTC. The gene expression profiles of GSE3467 and GSE6004 chip data were collected by GEO2R, and the differentially expressed genes (DEGs) were selected for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Protein-protein interaction (PPI) relationship analysis was achieved using STRING, and the hub genes were obtained using the Cytoscape software. GEPIA was used to validate the expressions of the hub genes in the normal and tumor tissues and to conduct survival analyses. Pertinent genetic pathology results were fetched using the HPA database. Finally, the key genes were clinically verified by reverse transcription-polymerase chain reaction. 97 genes were jointly up-regulated and 107 genes were jointly down-regulated in GSE3467 and GSE6004. GO function enrichment analysis revealed that the DEGs were involved in the regulation of calcium ion transport into cytosol, integrin binding, and cell adhesion molecule binding. KEGG pathway enrichment analysis indicated that the DEGs were chiefly associated with thyroid cancer and non-small cell lung cancer. According to the PPI network, 30 key target genes were identified. Only the expressions of ANK2, TLE1, and TCF4 matched between the normal and tumor tissues, and were associated with disease prognosis. When compared with the normal thyroid tissues, the protein and mRNA expressions of ANK2, TLE1, and TCF4 were down-regulated in PTC. Significant differences exist in overall gene expression between the thyroid tissues of patients with PTC and those of healthy people. Furthermore, the differential genes ANK2, TLE1, and TCF4 are expected to be reliable molecular markers for the mechanism study and diagnosis of PTC.

Genome-wide identification and expression pattern analysis of quinoa BBX family

BBX is a transcription factor encoding zinc finger protein that plays a key role in plant growth and development as well as in responding to abiotic stresses. However, in quinoa, which is known as a "super grain" and has extremely high nutritional value, this gene family has not yet been thoroughly studied. In this study, in order to fully understand the family function of the BBX in quinoa, a total of 31 BBX members were identified by bioinformatics methods. These BBX members were mainly acidic proteins, and most of their secondary structures were random coil s, 31 CqBBX members were unevenly distributed on 17 chromosomes, and the analysis of replication events found that quinoa BBX genes produced a total of 14 pairs of gene replication. The BBX genes were divided into five subfamilies according to phylogenetics, and its gene structure and conserved motif were basically consistent with the classification of its phylogenetic tree. In addition, a total of 43 light response elements, hormone response elements, tissue-specific expression response elements, and abiotic stress response elements were found in the promoter region, involving stress elements such as drought and low temperature. Finally, the expression patterns of CqBBX genes in different tissues and abiotic stresses were studied by combining transcriptome data and qRT-PCR , and all 13 genes responded to drought, salt, and low-temperature stress to varying degrees. This study is the first comprehensive study of the BBX family of quinoa, and its results provide important clues for further analysis of the function of the abiotic stress response.

Systems Drug Design for Muscle Invasive Bladder Cancer and Advanced Bladder Cancer by Genome-Wide Microarray Data and Deep Learning Method with Drug Design Specifications

Bladder cancer is the 10th most common cancer worldwide. Due to the lack of understanding of the oncogenic mechanisms between muscle-invasive bladder cancer (MIBC) and advanced bladder cancer (ABC) and the limitations of current treatments, novel therapeutic approaches are urgently needed. In this study, we utilized the systems biology method via genome-wide microarray data to explore the oncogenic mechanisms of MIBC and ABC to identify their respective drug targets for systems drug discovery. First, we constructed the candidate genome-wide genetic and epigenetic networks (GWGEN) through big data mining. Second, we applied the system identification and system order detection method to delete false positives in candidate GWGENs to obtain the real GWGENs of MIBC and ABC from their genome-wide microarray data. Third, we extracted the core GWGENs from the real GWGENs by selecting the significant proteins, genes and epigenetics via the principal network projection (PNP) method. Finally, we obtained the core signaling pathways from the corresponding core GWGEN through the annotations of the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to investigate the carcinogenic mechanisms of MIBC and ABC. Based on the carcinogenic mechanisms, we selected the significant drug targets NFKB1, LEF1 and MYC for MIBC, and LEF1, MYC, NOTCH1 and FOXO1 for ABC. To design molecular drug combinations for MIBC and ABC, we employed a deep neural network (DNN)-based drug-target interaction (DTI) model with drug specifications. The DNN-based DTI model was trained by drug-target interaction databases to predict the candidate drugs for MIBC and ABC, respectively. Subsequently, the drug design specifications based on regulation ability, sensitivity and toxicity were employed as filter criteria for screening the potential drug combinations of Embelin and Obatoclax for MIBC, and Obatoclax, Entinostat and Imiquimod for ABC from their candidate drugs. In conclusion, we not only investigated the oncogenic mechanisms of MIBC and ABC, but also provided promising therapeutic options for MIBC and ABC, respectively.

Identification of candidate hub genes correlated with the pathogenesis, diagnosis, and prognosis of prostate cancer by integrated bioinformatics analysis

Background

Prostate cancer (PCa) has the second highest morbidity and mortality rates in men. Concurrently, novel diagnostic and prognostic biomarkers of PCa remain crucial.

Methods

This study utilized integrated bioinformatics method to identify and validate the potential hub genes with high diagnostic and prognostic value for PCa.

Results

Four Gene Expression Omnibus (GEO) datasets including 123 PCa samples and 76 normal samples were screened and a total of 368 differentially expressed genes (DEGs), including 120 up-regulated DEGs and 248 down-regulated DEGs, were identified. Subsequent Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis showed that the DEGs were majorly enriched in focal adhesion, chemical carcinogenesis, drug metabolism, and cytochrome P450 pathways. Then, 11 hub genes were identified from the protein-protein interaction (PPI) network of the DEGs; 7 of the 11 genes showed the ability of distinguishing PCa from normal prostate based on receiver operating characteristic (ROC) curve analysis. And 5 of the 11 genes were correlated with clinical attributes. Lower CAV1, KRT5, SNAI2 and MYLK expression level were associated with higer Gleason score, advanced pathological T stage and N stage. Lower KRT5 and MYLK expression level were significantly correlated with poor disease-free survival, and lower KRT5 and PTGS2 expression level were significantly related to biochemical recurrence (BCR) status of PCa patients.

Conclusions

In conclusion, CAV1, KRT5, SNAI2, and MYLK show potential clinical diagnostic and prognostic value and could be used as novel candidate biomarkers and therapeutic targets for PCa.

CDH1 overexpression predicts bladder cancer from early stage and inversely correlates with immune infiltration

Background

Bladder cancer (BC) seriously endangers public health, but effective biomarkers for BC diagnosis, particularly in the early stage, are still lacking. Identification of reliable biomarkers associated with early-stage BC is of great importance to early treatment and an improved outcome.

Methods

Differentially expressed genes (DEGs) were identified using four publicly available early-stage BC gene-expression profiles. Protein–protein interaction (PPI) and survival analysis for hub genes was evaluated. The correlation between methylation of genes and prognosis was evaluated using the MethSurv database. Co-expressed genes were explored using Cancer Cell Line Encyclopedia database and the corresponding expression were assessed in vitro. The competing endogenous RNA network and the immune cell infiltration in BC were generated using data of The Cancer Genome Atlas.

Results

Ten hub genes of the 213 integrated DEGs were identified, including CDH1, IGFBP3, PPARG, SDC1, EPCAM, ACTA2, COL3A1, TPM1, ACTC1, and ACTN1. CDH1 appeared to increase from tumor initiation stage and negatively correlated with methylation. Six methylated sites in CDH1 indicated a good prognosis and one site indicated an aberrant prognosis. High CDH1 expression was negatively correlated with infiltrations by most immune cells, such as plasmacytoid dendritic cells (pDCs), regulatory T cells, macrophages, neutrophils, DCs, and natural killer cells. CDH1 was highly positively correlated with EPCAM and appeared to be directly regulated by miR-383.

Conclusions

The identified oncogenic alterations provide theoretical support for the development of novel biomarkers to advance early-stage BC diagnosis and personalized therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12894-022-01103-7.

A Comprehensive Transcriptomic Analysis of Arsenic-Induced Bladder Carcinogenesis

Arsenic (sodium arsenite: NaAsO2) is a potent carcinogen and a known risk factor for the onset of bladder carcinogenesis. The molecular mechanisms that govern arsenic-induced bladder carcinogenesis remain unclear. We used a physiological concentration of NaAsO2 (250 nM: 33 µg/L) for the malignant transformation of normal bladder epithelial cells (TRT-HU1), exposed for over 12 months. The increased proliferation and colony-forming abilities of arsenic-exposed cells were seen after arsenic exposure from 4 months onwards. Differential gene expression (DEG) analysis revealed that a total of 1558 and 1943 (padj < 0.05) genes were deregulated in 6-month and 12-month arsenic-exposed TRT-HU1 cells. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that cell proliferation and survival pathways, such as the MAPK, PI3K/AKT, and Hippo signaling pathways, were significantly altered. Pathway analysis revealed that the enrichment of stem cell activators such as ALDH1A1, HNF1b, MAL, NR1H4, and CDH1 (p < 0.001) was significantly induced during the transformation compared to respective vehicle controls. Further, these results were validated by qPCR analysis, which corroborated the transcriptomic analysis. Overall, the results suggested that stem cell activators may play a significant role in facilitating the arsenic-exposed cells to gain a survival advantage, enabling the healthy epithelial cells to reprogram into a cancer stem cell phenotype, leading to malignant transformation.

CCNB1 and CCNB2 involvement in the pathogenesis of psoriasis: a bioinformatics study

Objective

The cell cycle-related proteins cyclin B1 (CCNB1) and cyclin B2 (CCNB2) are potentially involved in the underlying mechanisms of psoriasis. The present study aimed to explore this possibility using bioinformatics approaches.

Methods

CCNB1 and CCNB2 protein levels were evaluated in 14 psoriasis patients and five healthy controls by enzyme-linked immunosorbent assays, and their mRNA levels were evaluated using data from four publicly available datasets (GSE53552, GSE41664, GSE14905, and GSE13355). Comparison of high- and low-expressing groups were performed to reveal CCNB1- and CCNB2-related differentially expressed genes, which were then assessed based on gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses. Correlation analyses between CCNB1 and CCNB2 levels and immune infiltration, as well as typical targets of psoriasis, were also performed.

Results

Overall, 12 CCNB1 and CCNB2 common immune-related targets potentially involved in psoriasis were identified. These could regulate the cell cycle of through multiple pathways. In addition, CCNB1 and CCNB2 were found to potentially support the release of key molecular targets of psoriasis through the regulation of mast cell activation and macrophage polarization.

Conclusions

These findings suggest that CCNB1 and CCNB2 may represent valuable molecular biomarkers of psoriasis, contributing to its onset and progression.

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.

Integrated analysis of multiple bioinformatics studies to identify microRNA-target gene-transcription factor regulatory networks in retinoblastoma

Background

In children, retinoblastoma (RB) is one of the most common primary malignant ocular tumors and has a poor prognosis and high mortality. To understand the molecular mechanisms of RB, we identified microRNAs (miRNAs), key genes and transcription factors (TFs) using bioinformatics analysis to build potential miRNA-gene-TF networks.

Methods

We collected three gene expression profiles and one miRNA expression profile from the Gene Expression Omnibus (GEO) database. We used the limma R package to identify overlapping differentially expressed genes (DEGs) and differentially expressed miRNAs in RB tissues compared to noncancer tissues. The robust rank aggregation (RRA) method was implemented to identify key genes among the DEGs. Then, miRNA-key gene-TF networks were built using the online tools TransmiR and miRTarBase. Next, we used RT-qPCR to confirm the results.

Results

We identified 180 DEGs in RB tissues compared to nontumor tissues using integrative analysis, among which 109 genes were upregulated and 71 were downregulated. Gene ontology (GO) analysis revealed that these DEGs were primarily involved with chromosome segregation, condensed chromosome and DNA replication origin binding. The most highly enriched pathways obtained in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were cell cycle, DNA replication, homologous recombination, P53 signaling pathway and pyrimidine metabolism. Furthermore, two key differentially expressed miRNAs (DEMs) were also established: let-7a and let-7b. Finally, the potential regulatory networks of miRNA-target gene-TFs were examined.

Conclusions

This study identified key genes and built miRNA-target gene-TF regulatory networks in RB, which will deepen our understanding of the molecular mechanisms involved in the development of RB. These key genes and miRNAs may be potential targets and biomarkers for RB diagnosis and therapy.

Identification and expression analysis of the CqSnRK2 gene family and a functional study of the CqSnRK2.12 gene in quinoa (Chenopodium quinoa Willd.)

Background

Sucrose non-fermenting 1 (SNF1)-associated protein kinase 2 (SnRK2) proteins belong to a relatively small family of plant-specific serine/threonine (Ser/Thr) protein kinases. SnRK2s participate in the abscisic acid (ABA) signaling pathway and play important roles in many biotic and abiotic stresses. At present, no SnRK2 gene has been reported in quinoa, and the recently published genome for this species provides an opportunity to identify and characterize the SnRK2 gene family.

Results

We identified 13 SnRK2 genes in the C. quinoa genome by bioinformatics analysis. Based on their phylogenetic relationships, these genes were divided into three subfamilies, similar to the situation in other plant species. Gene duplication analysis showed that there were seven pairs of homologous genes in the CqSnRK2 family, and that purifying selection played an important role in the evolution of SnRK2 genes. Gene structure analysis showed that the first exon in the SnRK2 family genes has the same length as the last exon, and that CqSnRK2 genes in the same subfamily have similar gene structures. Sequence analysis showed that the N-terminal region contains three highly conserved motifs. In addition, many kinds of cis-elements were identified in the promoter region of CqSnRK2, including those for hormone responses, stress responses, and tissue-specific expression. Transcription data analysis and qRT-PCR results showed that CqSnRK2 has different expression patterns in roots, stems, and leaves, and responded to biotic and abiotic stresses such as low temperature, salt, drought, and abscisic acid (ABA). In addition, we found that the protein encoded by CqSnRK2.12 was localized to the cytoplasm and nucleus, and there was no self-activation. The results of CqSnRK2.12 overexpression showed that transgenic Arabidopsis thaliana lines had increased drought tolerance compared to the controls.

Conclusion

The results of our study provide references for further studies on the evolution, function, and expression of the SnRK2 gene family in quinoa.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08626-1.

Differential Expression Profiles and Bioinformatics Analysis of tRNA-Derived Small RNAs in Muscle-Invasive Bladder Cancer in a Chinese Population

Muscle-invasive bladder cancer (MIBC) leads to a large societal burden. Recently, tRNA-derived small RNAs (tsRNAs), a novel type of noncoding RNA (ncRNAs), have been identified. However, the expression patterns and functions of tsRNAs in MIBC have not yet been identified. Here, RNA sequencing, bioinformatics, and quantitative reverse transcription- polymerase chain reaction (qRT-PCR) were used to screen the expression profiles and predict the potential roles of tsRNAs in MIBC. Of 406 tsRNAs differentially expressed in MIBC tissues, 91 tsRNAs were significantly differentially expressed. Then, four candidate tsRNAs, tiRNA-1:34-Val-CAC-2, tiRNA-1:33-Gly-GCC-1, tRF-1:32-Gly-GCC-1, and tRF-+1:T20-Ser-TGA-1, were selected. Next, a bioinformatics analysis showed the potential target genes and tsRNA–mRNA network. The most significant and meaningful terms of gene ontology were the positive regulation of the phosphate metabolic process, lamellipodium, and protein-cysteine S-acyltransferase activity in the biological process, cellular component, and molecular function, respectively. In addition, the top four pathways were predicted by the Kyoto Encyclopedia of Genes and Genomes database (KEGG). Finally, qRT-PCR demonstrated a similar expression pattern compared to sequencing data for the candidate tsRNAs. In short, we find differential expression profiles and predict that tiRNA-1:33-Gly-GCC-1, tRF-1:32-Gly-GCC-1, and tRF-+1:T20-Ser-TGA-1 are very likely to engage in the pathophysiological process of MIBC via regulating the target genes in the key pathways.

DNA Methylome Changes of Muscle- and Neuronal-Related Processes Precede Bladder Cancer Invasiveness

Bladder cancer (BC) is the ninth leading cause of cancer death with one of the highest recurrence rates among all cancers. One of the main risks for BC development is exposure to nitrosamines present in tobacco smoke or in other products. Aberrant epigenetic (DNA methylation) changes accompanied by deregulated gene expression are an important element of cancer pathogenesis. Therefore, we aimed to determine DNA methylation signatures and their impacts on gene expression in mice treated with N-butyl-N-(4-hydroxybutyl) nitrosamine (BBN), a carcinogen similar to compounds found in tobacco smoke. Following BBN administration mice developed non-invasive or invasive bladder cancers. Surprisingly, muscle- and neuronal-related pathways emerged as the most affected in those tumors. Hypo- and hypermethylation changes were present within non-invasive BC, across CpGs mapping to the genes involved in muscle- and neuronal-related pathways, however, methylation differences were not sufficient to affect the expression of the majority of associated genes. Conversely, invasive tumors displayed hypermethylation changes that were linked with alterations in gene expression profiles. Together, these findings indicate that bladder cancer progression could be revealed through methylation profiling at the pre-invasive cancer stage that could assist monitoring of cancer patients and guide novel therapeutic approaches.

Identification of Potential Prognostic Biomarkers Associated With Macrophage M2 Infiltration in Gastric Cancer

Gastric cancer is a common cancer afflicting people worldwide. Although incremental progress has been achieved in gastric cancer research, the molecular mechanisms underlying remain unclear. In this study, we conducted bioinformatics methods to identify prognostic marker genes associated with gastric cancer progression. Three hundred and twenty-seven overlapping DEGs were identified from three GEO microarray datasets. Functional enrichment analysis revealed that these DEGs are involved in extracellular matrix organization, tissue development, extracellular matrix–receptor interaction, ECM-receptor interaction, PI3K-Akt signaling pathway, focal adhesion, and protein digestion and absorption. A protein–protein interaction network (PPI) was constructed for the DEGs in which 25 hub genes were obtained. Furthermore, the turquoise module was identified to be significantly positively coexpressed with macrophage M2 infiltration by weighted gene coexpression network analysis (WGCNA). Hub genes of COL1A1, COL4A1, COL12A1, and PDGFRB were overlapped in both PPI hub gene list and the turquoise module with significant association with the prognosis in gastric cancer. Moreover, functional analysis demonstrated that these hub genes play pivotal roles in cancer cell proliferation and invasion. The investigation of the gene markers can help deepen our understanding of the molecular mechanisms of gastric cancer. In addition, these genes may serve as potential prognostic biomarkers for gastric cancer diagnosis.

Analysis of the Mechanism of RAD18 in Glioma

INTRODUCTION

This study aimed to evaluate the regulatory mechanism of RAD18 in glioma development.

METHODS

RAD18 expression was compared in glioma tumors and normal samples. Furthermore, we investigated the association between gene transcription and clinical factors in glioma samples, followed by functional enrichment analysis, screening for key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, immune infiltration analysis of high and low RAD18 expression groups, and correlation analysis of quantified KEGG signaling pathways and immune cell types.

RESULTS

The expression of RAD18 was upregulated in gliomas. Moreover, RAD18 expression was significantly correlated with age, tumor grade, and histological subtype. Notably, patients with gliomas with high RAD18 expression levels had worse overall survival. Functional enrichment analysis showed that RAD18 was significantly related to biological processes, such as cell division, chemical synaptic transmission, and mitotic nuclear division, and KEGG pathways such as cell cycle, oxidative phosphorylation, and extracellular matrix (ECM)-receptor interaction. The infiltration of five immune cells (plasma B cells, naive B cells, resting CD4+ memory T cells, monocytes, and M1 macrophages) was significantly different between the high and low RAD18 expression groups, and this difference was significantly related to key KEGG pathways, such as neuroactive ligand-receptor interaction and ECM-receptor interaction.

CONCLUSION

RAD18 may serve as a target for glioma treatment and as a key regulator of glioma development.

Identification of Gene Co-Expression Modules and Core Genes Related to Immune Disorders in Major Depression Disorder

Introduction

Various studies have confirmed the connection between the mental state and the immune system, that is, mental activities can regulate immune function, and immune system disorders can not only lead to bodily diseases but also changes related to mentality, behavior, personality, and aging. However, the specific regulatory mechanism and key genes are still unclear.

Methods

We obtained the peripheral blood gene sequencing data from patients with major depression and normal volunteers from the GEO database and evaluated the scores of different immune cells by immune scoring algorithm. Using the immune scores as clinical data, a weighted gene co-expression network analysis (WGCNA) was carried out to study the association between the clinical characteristics and modules. Therefore, providing an opportunity to lock modules and core genes which are highly related to the immune regulation of major depression.

Results

Thirteen co-expression modules were clustered from 20,011 genes, the yellow module had a positive correlation with CD4⁺ T cell, CD8⁺ T cell, B cell, and NK cell immune scores, and a negative correlation with purple module. Functional annotation and signaling pathway analysis illustrated that the yellow module is mostly enriched in thymus development, T cell co-stimulation and differentiation, and B cell activation. Genes in the purple module were primarily related to inhibition of protein phosphorylation, leukocyte migration, promotion of apoptosis and hypoxia and other signaling pathways. Additionally, hub genes in the yellow and purple modules were detected, in which SKAP1 and RALB may be important regulatory genes affecting the immune status of patients with depression.

Discussion

In general, our study reveals the key genes related to the decrease in CD4⁺ T cells, CD8⁺ T cells, and B cells, in the peripheral blood of patients with depression, which provides some new insights and understandings for the clinical treatment and diagnosis of major depression. Drug design targeting these targets may provide the possibility for the treatment of major depression.

A five-gene methylation signature predicts overall survival of patients with clear cell renal cell carcinoma

BACKGROUND

In this study, we aimed to screen methylation signatures associated with the prognosis of patients with clear cell renal cell carcinoma (ccRCC).

METHODS

Gene expression and methylation profiles of ccRCC patients were downloaded from publicly available databases, and differentially expressed genes (DEGs)-differentially methylated genes (DMGs) were obtained. Subsequently, gene set enrichment and transcription factor (TF) regulatory network analyses were performed. In addition, a prognostic model was constructed and the relationship between disease progression and immunity was analyzed.

RESULTS

A total of 23 common DEGs-DMGs were analyzed, among which 14 DEGs-DMGs were obtained with a cutoff value of PCC < 0 and p < 0.05. The enrichment analysis showed that the 14 DEGs-DMGs were enriched in three GO terms and three KEGG pathways. In addition, a total of six TFs were shown to be associated with the 14 DEGs-DMGs, including RP58, SOX9, NF-κB65, ATF6, OCT, and IK2. A prognostic model using five optimized DEGs-DMGs which efficiently predicted survival was constructed and validated using the GSE105288 dataset. Additionally, four types of immune cells (NK cells, macrophages, neutrophils, and cancer-associated fibroblasts), as well as ESTIMATE, immune, and stromal scores were found to be significantly correlated with ccRCC progression (normal, primary, and metastasis) in addition to the five optimized DEGs-DMGs.

CONCLUSION

A five-gene methylation signature with the predictive ability for ccRCC prognosis was investigated in this study, consisting of CCNB2, CDKN1C, CTSH, E2F2, and ERMP1. In addition, potential targets for methylation-mediated immunotherapy were highlighted.

High S100A7 expression is associated with early muscle invasion and poor survival in bladder carcinoma

Muscle-invasive bladder carcinoma (MIBC) accounts for 25% of newly diagnosed bladder carcinomas (BCs) and presents a high risk of progression and metastasis. This study aimed to identify reliable biomarkers associated with muscle invasion and prognosis to identify potential therapeutic targets for MIBC. Four gene datasets were downloaded from the Gene Expression Omnibus, and the integrated differentially expressed genes (DEGs) were then subjected to gene ontology (GO) terms and pathway enrichment analyses. Correlation analysis between the expression of the top-ranking DEGs and pathological T stages was performed to identify the genes associated with early muscle invasion. The corresponding prognostic values were evaluated, and co-expressed genes mined in the cBioPortal database were loaded into ClueGo in Cytoscape for pathway enrichment analysis. Using data mining from the STRING and TCGA databases, protein-protein interaction and competitive endogenous RNA networks were constructed. In total, 645 integrated DEGs were identified and these were mainly enriched in 26 pathways, including cell cycle, bladder cancer, DNA replication, and PPAR signaling pathway. S100A7 expression was significantly increased from the T2 stage and showed significantly worse overall survival and disease-specific survival in patients with BC. In total, 144 genes co-expressed with S100A7 in BC were significantly enriched in the IL-17 pathway. S100A7 was predicted to directly interact with LYZ, which potentially shows competitive binding with hsa-mir-140 to affect the expression of six lncRNAs in MIBC. In conclusion, high S100A7 expression was predicted to be associated with early muscle invasion and poor survival in patients with BC.

Next-generation Proteomics-Based Discovery, Verification, and Validation of Urine Biomarkers for Bladder Cancer Diagnosis

Purpose

We aimed to identify, verify, and validate a multiplex urinary biomarker-based prediction model for diagnosis and surveillance of urothelial carcinoma of bladder, using high-throughput proteomics methods.

Materials and Methods

Label-free quantification of data-dependent and data-independent acquisition of 12 and 24 individuals was performed in each of the discovery and verification phases using mass spectrometry, simultaneously using urinary exosome and proteins. Based on five scoring system based on proteomics data and statistical methods, we selected eight proteins. Enzyme-linked immunosorbent assay on urine from 120 patients with bladder mass lesions used for validation. Using multivariable logistic regression, we selected final candidate models for predicting bladder cancer.

Results

Comparing the discovery and verification cohorts, 38% (50/132 exosomal differentially expressed proteins [DEPs]) and 44% (109/248 urinary DEPs) are consistent at statistically significance, respectively. The 20 out of 50 exosome proteins and 27 out of 109 urinary proteins were upregulated in cancer patients. From eight selected proteins, we developed two diagnostic models for bladder cancer. The area under the receiver operating characteristic curve (AUROC) of two models were 0.845 and 0.842, which outperformed AUROC of urine cytology.

Conclusion

The results showed that the two diagnostic models developed here were more accurate than urine cytology. We successfully developed and validated a multiplex urinary protein-based prediction, which will have wide applications for the rapid diagnosis of urothelial carcinoma of the bladder. External validation for this biomarker panel in large population is required.

Focal adhesion kinase (FAK) promotes cholangiocarcinoma development and progression via YAP activation

BACKGROUND & AIMS

Focal adhesion kinase (FAK) is a non-receptor tyrosine kinase that is upregulated in many tumor types and is a promising target for cancer therapy. Herein, we elucidated the functional role of FAK in intrahepatic cholangiocarcinoma (iCCA) development and progression.

METHODS

Expression levels and activation status of FAK were determined in human iCCA samples. The functional contribution of FAK to Akt/YAP murine iCCA initiation and progression was investigated using conditional Fak knockout mice and constitutive Cre or inducible Cre mice, respectively. The oncogenic potential of FAK was further examined via overexpression of FAK in mice. In vitro cell line studies and in vivo drug treatment were applied to address the therapeutic potential of targeting FAK for iCCA treatment.

RESULTS

FAK was ubiquitously upregulated and activated in iCCA lesions. Ablation of FAK strongly delayed Akt/YAP-driven mouse iCCA initiation. FAK overexpression synergized with activated AKT to promote iCCA development and accelerated Akt/Jag1-driven cholangiocarcinogenesis. Mechanistically, FAK was required for YAP(Y357) phosphorylation, supporting the role of FAK as a central YAP regulator in iCCA. Significantly, ablation of FAK after Akt/YAP-dependent iCCA formation strongly suppressed tumor progression in mice. Furthermore, a remarkable iCCA growth reduction was achieved when a FAK inhibitor and palbociclib, a CDK4/6 inhibitor, were administered simultaneously in human iCCA cell lines and Akt/YAP mice.

CONCLUSIONS

FAK activation contributes to the initiation and progression of iCCA by inducing the YAP proto-oncogene. Targeting FAK, either alone or in combination with anti-CDK4/6 inhibitors, may be an effective strategy for iCCA treatment.

LAY SUMMARY

We found that the protein FAK (focal adhesion kinase) is upregulated and activated in human and mouse intrahepatic cholangiocarcinoma samples. FAK promotes intrahepatic cholangiocarcinoma development, whereas deletion of FAK strongly suppresses its initiation and progression. Combined FAK and CDK4/6 inhibitor treatment had a strong anti-cancer effect in in vitro and in vivo models. This combination therapy might represent a valuable and novel treatment against human intrahepatic cholangiocarcinoma.

Identification of Biomarkers Related to Prognosis of Bladder Transitional Cell Carcinoma

Bladder transitional cell carcinoma (BTCC) is highly fatal and generally has a poor prognosis. To improve the prognosis of patients with BTCC, it is particularly important to identify biomarkers related to the prognosis. In this study, differentially expressed messenger RNAs were obtained by analyzing relevant data of BTCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Next, hub genes that were suitable for correlation analysis with prognosis were determined through constructing a protein–protein interaction (PPI) network of differentially expressed genes and screening of major modules in the network. Finally, survival analysis of these hub genes found that three of them (CCNB1, ASPM, and ACTC1) were conspicuously related to the prognosis of patients with BTCC (p < 0.05). By combining the clinical features of BTCC and the expression levels of the three genes, univariate Cox and multivariate Cox regression analyses were performed and denoted that CCNB1 could be used as an independent prognostic factor for BTCC. This study provided potential biomarkers for the prognosis of BTCC as well as a theoretical basis for subsequent prognosis-related research.

Genome wide identification and expression pattern analysis of the GRAS family in quinoa

GRAS, a key transcription factor in plant growth and development, has not yet been reported in quinoa. Therefore, this study used the latest quinoa genomic data to identify and analyse GRAS genes in quinoa: 52 GRAS genes were identified in quinoa, these being unevenly distributed on 19 chromosomes. Fragment duplication and tandem duplication events were the main reasons for the expansion of the GRAS gene family in quinoa. Protein sequence analysis showed that there were some differences in amino acid numbers and isoelectric points amongst different subfamilies, and the main secondary structures were α-helix and random coil. The CqGRAS gene was divided into 14 subfamilies based on results from phylogenetic analysis. The genes located in the same subfamily had similar gene structures, conserved motifs, and three-level models. Promoter region analysis showed that the GRAS family genes contained multiple homeostasis elements that responded to hormones and adversity. GO enrichment indicated that CqGRAS genes were involved in biological processes, cell components, and molecular functions. By analysing the expression of CqGRAS genes in different tissues and different treatments, it was found that GRAS genes had obvious differential expression in different tissues and stress, which indicates that GRAS genes had tissue or organ expression specificity and thus might play an important role in response to stress. These results laid a foundation for further functional research on the GRAS gene family in quinoa.

A Novel Computational Framework to Predict Disease-Related Copy Number Variations by Integrating Multiple Data Sources

Copy number variation (CNV) may contribute to the development of complex diseases. However, due to the complex mechanism of path association and the lack of sufficient samples, understanding the relationship between CNV and cancer remains a major challenge. The unprecedented abundance of CNV, gene, and disease label data provides us with an opportunity to design a new machine learning framework to predict potential disease-related CNVs. In this paper, we developed a novel machine learning approach, namely, IHI-BMLLR (Integrating Heterogeneous Information sources with Biweight Mid-correlation and L1-regularized Logistic Regression under stability selection), to predict the CNV-disease path associations by using a data set containing CNV, disease state labels, and gene data. CNVs, genes, and diseases are connected through edges and then constitute a biological association network. To construct a biological network, we first used a self-adaptive biweight mid-correlation (BM) formula to calculate correlation coefficients between CNVs and genes. Then, we used logistic regression with L1 penalty (LLR) function to detect genes related to disease. We added stability selection strategy, which can effectively reduce false positives, when using self-adaptive BM and LLR. Finally, a weighted path search algorithm was applied to find top D path associations and important CNVs. The experimental results on both simulation and prostate cancer data show that IHI-BMLLR is significantly better than two state-of-the-art CNV detection methods (i.e., CCRET and DPtest) under false-positive control. Furthermore, we applied IHI-BMLLR to prostate cancer data and found significant path associations. Three new cancer-related genes were discovered in the paths, and these genes need to be verified by biological research in the future.

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

OBJECTIVE

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

MATERIALS AND METHODS

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

RESULTS

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

CONCLUSION

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

Co-Deregulated miRNA Signatures in Childhood Central Nervous System Tumors: In Search for Common Tumor miRNA-Related Mechanics

Simple Summary

Childhood tumors of the central nervous system (CNS) constitute a grave disease and their diagnosis is difficult to be handled. To gain better knowledge of the tumor’s biology, it is essential to understand the underlying mechanisms of the disease. MicroRNAs (miRNAs) are small noncoding RNAs that are dysregulated in many types of CNS tumors and regulate their occurrence and development through specific signal pathways. However, different types of CNS tumors’ area are characterized by different deregulated miRNAs. Here, we hypothesized that CNS tumors could have commonly deregulated miRNAs, i.e., miRNAs that are simultaneously either upregulated or downregulated in all tumor types compared to the normal brain tissue, irrespectively of the tumor sub-type and/or diagnosis. The only criterion is that they are present in brain tumors. This approach could lead us to the discovery of miRNAs that could be used as pan-CNS tumoral therapeutic targets, if successful.

Abstract

Despite extensive experimentation on pediatric tumors of the central nervous system (CNS), related to both prognosis, diagnosis and treatment, the understanding of pathogenesis and etiology of the disease remains scarce. MicroRNAs are known to be involved in CNS tumor oncogenesis. We hypothesized that CNS tumors possess commonly deregulated miRNAs across different CNS tumor types. Aim: The current study aims to reveal the co-deregulated miRNAs across different types of pediatric CNS tumors. Materials: A total of 439 CNS tumor samples were collected from both in-house microarray experiments as well as data available in public databases. Diagnoses included medulloblastoma, astrocytoma, ependydoma, cortical dysplasia, glioblastoma, ATRT, germinoma, teratoma, yoc sac tumors, ocular tumors and retinoblastoma. Results: We found miRNAs that were globally up- or down-regulated in the majority of the CNS tumor samples. MiR-376B and miR-372 were co-upregulated, whereas miR-149, miR-214, miR-574, miR-595 and miR-765 among others, were co-downregulated across all CNS tumors. Receiver-operator curve analysis showed that miR-149, miR-214, miR-574, miR-595 and miR765 could distinguish between CNS tumors and normal brain tissue. Conclusions: Our approach could prove significant in the search for global miRNA targets for tumor diagnosis and therapy. To the best of our knowledge, there are no previous reports concerning the present approach.

The remodelling of actin composition as a hallmark of cancer

Actin is a key structural protein that makes up the cytoskeleton of cells, and plays a role in functions such as division, migration, and vesicle trafficking. It comprises six different cell-type specific isoforms: ACTA1, ACTA2, ACTB, ACTC1, ACTG1, and ACTG2. Abnormal actin isoform expression has been reported in many cancers, which led us to hypothesize that it may serve as an early biomarker of cancer. We show an overview of the different actin isoforms and highlight mechanisms by which they may contribute to tumorigenicity. Furthermore, we suggest how the aberrant expression of actin subunits can confer cells with greater proliferation ability, increased migratory capability, and chemoresistance through incorporation into the normal cellular F-actin network and altered actin binding protein interaction. Studying this fundamental change that takes place within cancer cells can further our understanding of neoplastic transformation in multiple tissue types, which can ultimately aid in the early-detection, diagnosis and treatment of cancer.

Biomarker identification and pathway analysis of rheumatoid arthritis based on metabolomics in combination with ingenuity pathway analysis

Rheumatoid arthritis (RA) is a common autoimmune and inflammatory disease worldwide, but understanding its pathogenesis is still limited. In this study, plasma untargeted metabolomics of a discovery cohort and targeted analysis of a verification cohort were performed by gas chromatograph mass spectrometry (GC/MS). Univariate and multivariate statistical analysis were utilized to reveal differential metabolites, followed by the construction of biomarker panel through random forest (RF) algorithm. The pathways involved in RA were enriched by differential metabolites using Ingenuity Pathway Analysis (IPA) suite. Untargeted metabolomics revealed eighteen significantly altered metabolites in RA. Among these metabolites, a three-metabolite marker panel consisting of L-cysteine, citric acid and L-glutamine was constructed, using random forest algorithm that could predict RA with high accuracy, sensitivity and specificity based on a multivariate exploratory receiver operator characteristic (ROC) curve analysis. The panel was further validated by support vector machine (SVM) and partial least squares discriminant analysis (PLS-DA) algorithms, and also verified with targeted metabolomics using a verification cohort. Additionally, the dysregulated taurine biosynthesis pathway in RA was revealed by an integrated analysis of metabolomics and transcriptomics. Our findings in this study not only provided a mechanism underlying RA pathogenesis, but also offered alternative therapeutic targets for RA.

Combined Metabolomics with Transcriptomics Reveals Important Serum Biomarkers Correlated with Lung Cancer Proliferation through a Calcium Signaling Pathway

Lung cancer (LC) is one of the most malignant cancers in the world, but currently, it lacks effective noninvasive biomarkers to assist its early diagnosis. Our study aims to discover potential serum diagnostic biomarkers for LC. In our study, untargeted serum metabolomics of a discovery cohort and targeted analysis of a test cohort were performed based on gas chromatography-mass spectrometry. Both univariate and multivariate statistical analyses were employed to screen for differential metabolites between LC and healthy control (HC), followed by the selection of candidate biomarkers through multiple algorithms. The results showed that 15 metabolites were significantly dysregulated between LC and HC, and a panel, comprising cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid, was demonstrated to have excellent differentiating capability for LC based on multiple classification modelings. In addition, the molecular interaction analysis combined with transcriptomics revealed a close correlation between the candidate biomarkers and LC proliferation via a Ca²⁺ signaling pathway. Our study discovered that cholesterol, oleic acid, myo-inositol, 2-hydroxybutyric acid, and 4-hydroxybutyric acid in combination could be a promising diagnostic biomarker for LC, and most importantly, our results will shed some light on the pathophysiological mechanism underlying LC to understand it deeply. The data that support the findings of this study are openly available in MetaboLights at https://www.ebi.ac.uk/metabolights/, reference number MTBLS1517.

The VIM-AS1/miR-655/ZEB1 axis modulates bladder cancer cell metastasis by regulating epithelial-mesenchymal transition

BACKGROUND

Invasive bladder tumors cause a worse prognosis in patients and remain a clinical challenge. Epithelial-mesenchymal transition (EMT) is associated with bladder cancer metastasis. In the present research, we attempted to demonstrate a novel mechanism by which a long noncoding RNA (lncRNA)-miRNA-mRNA axis regulates EMT and metastasis in bladder cancer.

METHODS

Immunofluorescence (IF) staining was used to detect Vimentin expression. The protein expression of ZEB1, Vimentin, E-cadherin, and Snail was investigated by using immunoblotting assays. Transwell assays were performed to detect the invasive capacity of bladder cancer cells. A wound healing assay was used to measure the migratory capacity of bladder cancer cells.

RESULTS

Herein, we identified lncRNA VIM-AS1 as a highly- expressed lncRNA in bladder cancer, especially in metastatic bladder cancer tissues and high-metastatic bladder cancer cell lines. By acting as a ceRNA for miR-655, VIM-AS1 competed with ZEB1 for miR-655 binding, therefore eliminating the miR-655-mediated suppression of ZEB1, finally promoting EMT in both high- and low-metastatic bladder cancer cells and enhancing cancer cell metastasis.

CONCLUSIONS

In conclusion, the VIM-AS1/miR-655/ZEB1 axis might be a promising target for improving bladder cancer metastasis via an EMT-related mechanism.

Exploration of Prognostic Biomarkers for Lung Adenocarcinoma Through Bioinformatics Analysis

With the development of computer technology, screening cancer biomarkers based on public databases has become a common research method. Here, an eight-gene prognostic model, which could be used to judge the prognosis of patients with lung adenocarcinoma (LUAD), was developed through bioinformatics methods. This study firstly used several gene datasets from GEO database to mine differentially expressed genes (DEGs) in LUAD tissue and healthy tissue via joint analysis. Later, enrichment analysis for the DEGs was performed, and it was found that the DEGs were mainly activated in pathways involved in extracellular matrix, cell adhesion, and leukocyte migration. Afterward, a TCGA cohort was used to perform univariate Cox, least absolute shrinkage and selection operator method, and multivariate Cox regression analyses for the DEGs, and a prognostic model consisting of eight genes (GPX3, TCN1, ASPM, PCP4, CAV2, S100P, COL1A1, and SPOK2) was established. Receiver operation characteristic (ROC) curve was then used to substantiate the diagnostic efficacy of the prognostic model. The survival significance of signature genes was verified through the GEPIA database, and the results exhibited that the risk coefficients of the eight genes were basically congruous with the effects of these genes on the prognosis in the GEPIA database, which suggested that the results were accurate. Finally, combined with clinical characteristics of patients, the diagnostic independence of the prognostic model was further validated through univariate and multivariate regression, and the results indicated that the model had independent prognostic value. The overall finding of the study manifested that the eight-gene prognostic model is closely related to the prognosis of LUAD patients, and can be used as an independent prognostic indicator. Additionally, the prognostic model in this study can help doctors make a better diagnosis in treatment and ultimately benefit LUAD patients.

Identification of Seven-Gene Hypoxia Signature for Predicting Overall Survival of Hepatocellular Carcinoma

Background

Hepatocellular carcinoma (HCC) is ranked fifth among the most common cancer worldwide. Hypoxia can induce tumor growth, but the relationship with HCC prognosis remains unclear. Our study aims to construct a hypoxia-related multigene model to predict the prognosis of HCC.

Methods

RNA-seq expression data and related clinical information were download from TCGA database and ICGC database, respectively. Univariate/multivariate Cox regression analysis was used to construct prognostic models. KM curve analysis, and ROC curve were used to evaluate the prognostic models, which were further verified in the clinical traits and ICGC database. GSEA analyzed pathway enrichment in high-risk groups. Nomogram was constructed to predict the personalized treatment of patients. Finally, real-time fluorescence quantitative PCR (RT-qPCR) was used to detect the expressions of KDELR3 and SCARB1 in normal hepatocytes and 4 HCC cells. The expressions of SCARB1 in hepatocellular carcinoma tissue in 46 patients were detected by immunohistochemistry, and the correlation between its expressions and disease free survival of patient was calculated.

Results

Through a series of analyses, seven prognostic markers related to HCC survival were constructed. HCC patients were divided into the high and low risk group, and the results of KM curve showed that there was a significant difference between the two groups. Stratified analysis, found that there were significant differences in risk values of different ages, genders, stages and grades, which could be used as independent predictors. In addition, we assessed the risk value in the clinical traits analysis and found that it could accelerate the progression of cancer, while the results of GSEA enrichment analysis showed that the high-risk group patients were mainly distributed in the cell cycle and other pathways. Then, Nomogram was constructed to predict the overall survival of patients. Finally, RT-qPCR showed that KDELR3 and SCARB1 were highly expressed in HepG2 and L02, respectively. Results of IHC staining showed that SCARB1 was highly expressed in cancer tissues compared to adjacent normal liver tissues and its expression was related to hepatocellular carcinoma differentiation status. The Kaplan-Meier survival showed a poor percent survival in the SCARB1 high group compared to that in the SCARB1 low group.

Conclusion

This study provides a potential diagnostic indicator for HCC patients, and help clinicians to deepen the comprehension in HCC pathogenesis so as to make personalized medical decisions.

GC-MS-based metabolomics reveals new biomarkers to assist the differentiation of prostate cancer and benign prostatic hyperplasia

Lack of efficient noninvasive biomarkers for differentiating prostate cancer (PCa) and benign prostate hyperplasia (BPH) is a serious concern for men's health worldwide. In this study, we aimed to improve the diagnostic capability of the existing noninvasive biomarkers for PCa. GC-MS-based untargeted metabolomics was employed to analyze plasma samples for 41 PCa patients and 38 BPH controls. Both univariate and multivariate statistical analyses were performed to screen for differential metabolites between PCa and BPH, followed by the selection of potential biomarkers through machine learning. The chosen candidate biomarkers were then verified by targeted analysis and transcriptome data. The results showed that twelve metabolites were significantly dysregulated between PCa and BPH, three metabolites including L-serine, myo-inositol, and decanoic acid could be potential biomarkers for discriminating PCa from BPH. Most importantly, ROC curve analysis demonstrated that the involvement of the three potential biomarkers has increased the area under the curve (AUC) value of cPSA and tPSA from 0.542 and 0.592 to 0.781, respectively. Therefore, it was concluded that the involvement of L-serine, myo-inositol, and decanoic acid can largely improve the diagnostic capability of the commonly used noninvasive biomarkers in the clinic for differentiating PCa from BPH.

Genome-wide identification, characterization and expression analysis of the LIM transcription factor family in quinoa

Lim family members play an important role in the regulation of plant cell development and stress response. However, there are few studies on LIM family in quinoa. In this study, we identified nine LIMS (named cqlim01-cqlim09) from quinoa, which were divided into three subfamilies (α Lim1, γ lim2 and δ lim2) according to phylogeny. The differences in gene structure and motif composition among different subfamilies have been observed. In addition, we studied the repetitive events of the members of the family. The Ka/Ks (non synchronous substitution rate / synchronous substitution rate) ratio analysis showed that the repetitive CqLIMs probably experienced purifying selection pressure. Promoter analysis showed that the family genes contained a variety of hormones, stress and tissue-specific expression elements, and protein interactions showed that these genes had actin stabilizing effect. In addition, QRT PCR results showed that all CqLIM genes were positively regulated under three stresses (low temperature, salt and ABA). These results provide a theoretical basis of further study of LIM gene in quinoa.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s12298-021-00988-2.

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.

Defining muscle-invasive bladder cancer immunotypes by introducing tumor mutation burden, CD8+ T cells, and molecular subtypes

Immunotherapy, especially anti-PD-1, is becoming a pillar of modern muscle-invasive bladder cancer (MIBC) treatment. However, the objective response rates (ORR) are relatively low due to the lack of precise biomarkers to select patients. Herein, the molecular subtype, tumor mutation burden (TMB), and CD8+ T cells were calculated by the gene expression and mutation profiles of MIBC patients. MIBC immunotypes were constructed using clustering analysis based on tumor mutation burden, CD8+ T cells, and molecular subtypes. Mutated genes, enriched functional KEGG pathways and GO terms, and co-expressed network-specific hub genes have been identified. We demonstrated that ORR of immunotype A patients identified by molecular subtype, CD8+ T cells, and TMB is about 36% predictable. PIK3CA, RB1, FGFR3, KMT2C, MACF1, RYR2, and EP300 are differentially mutated among three immunotypes. Pathways such as ECM-receptor interaction, PI3K-Akt signaling pathway, and TGF-beta signaling pathway are top-ranked in enrichment analysis. Low expression of ACTA2 was associated with the MIBC survival benefit. The current study constructs a model that could identify suitable MIBC patients for immunotherapy, and it is an important step forward to the personalized treatment of bladder cancers.

Identification of key genes and microRNA regulatory network in development and progression of urothelial bladder carcinoma

Background

Bladder cancer as other cancers contains multiple dynamic alterations in progression. Theoretically, large number of genes participates in cancer progression. In the present study, the interconnections of genesets defined by Gene Set Enrichment Analysis (GSEA) and tumor histopathological stages were characterized. In addition, the outcomes with genesets were discussed in bladder cancer.

Methods

Transcriptome data from 411 tissues of urothelial bladder carcinoma and 19 samples from adjacent tissues were retrieved from The Cancer Genome Atlas (TCGA) database. Single-sample GSEA (ssGSEA), cluster analysis of geneset enrichment scores and genesets as indicators in prognosis were applied to elucidate the correlations between genesets and bladder cancer progression.

Results

Chemical and genetic perturbations (CGP), canonical pathways (CP), CP:BIOCARTA (BioCarta gene sets), CP:KEGG (KEGG gene sets) and CP:REACTOME (Reactome gene sets) in C2 collection, upstream cis-regulatory motifs serum response factor (SRF) in C3 collection, KRAS in C6 collection and C8+ T cells in C7 collection were observed as enriched by ssGSEA. The cluster 2 identified from cluster analysis shows a more immune active microenvironment which tended to increase in stage II and decreased in stage IV indicating the crucial role in bladder cancer progression. miR-450, miR-518s, transcription factor PAX3, KRAS and PTEN were potential markers for outcomes of urothelial bladder carcinoma. Activating tumor immune microenvironment had deteriorated prognosis of patients with bladder cancer.

Conclusions

Our findings demonstrated that activating tumor immune microenvironment is a negative factor for outcomes of urothelial bladder carcinoma. These data provided a potential combination strategy for patients with bladder cancer.