Identification of 9 key genes and small molecule drugs in clear cell renal cell carcinoma

Elevated ADH5 expression suggested better prognosis in kidney renal clear cell carcinoma (KIRC) and related to immunity through single-cell and bulk RNA-sequencing

BACKGROUND

Despite the rapid advances in modern medical technology, kidney renal clear cell carcinoma (KIRC) remains a challenging clinical problem in urology. Researchers urgently search for useful markers to break through the therapeutic conundrum due to its high lethality. Therefore, the study explores the value of ADH5 on overall survival (OS) and the immunology of KIRC.

METHODS

The gene expression matrix and clinical information on ADH5 in the TCGA database were validated using external databases and qRT-PCR. To confirm the correlation between ADH5 and KIRC prognosis, univariate/multivariate Cox regression analysis was used. We also explored the signaling pathways associated with ADH5 in KIRC and investigated its association with immunity.

RESULTS

The mRNA and protein levels showed an apparent downregulation of ADH5 in KIRC. Correlation analysis revealed that ADH5 was directly related to histological grade, clinical stage, and TMN stage (p < 0.05). Univariate and multivariate Cox regression analysis identified ADH5 as an independent factor affecting the prognosis of KIRC. Enrichment analysis looked into five ADH5-related signaling pathways. The results showed no correlation between ADH5 and TMB, TNB, and MSI. From an immunological perspective, ADH5 was found to be associated with the tumor microenvironment, immune cell infiltration, and immune checkpoints. Lower ADH5 expression was associated with greater responsiveness to immunotherapy. Single-cell sequencing revealed that ADH5 is highly expressed in immune cells.

CONCLUSION

ADH5 could be a promising prognostic biomarker and a potential therapeutic target for KIRC. Besides, it was found that KIRC patients with low ADH5 expression were more sensitive to immunotherapy.

Identification of molecular subtypes and diagnostic model in clear cell renal cell carcinoma based on collagen-related genes may predict the response of immunotherapy

Background: Collagen represents a prominent constituent of the tumor's extracellular matrix (ECM). Nonetheless, its correlation with the molecular subtype attributes of clear cell renal cell carcinoma (ccRCC) remains elusive. Our objective is to delineate collagen-associated molecular subtypes and further construct diagnostic model, offering insights conducive to the precise selection of ccRCC patients for immunotherapeutic interventions. Methods: We performed unsupervised non-negative matrix factorization (NMF) analysis on TCGA-KIRC samples, utilizing a set of 33 collagen-related differentially expressed genes (33CRDs) for clustering. Our analysis encompassed evaluations of subtype-associated differences in pathways, immune profiles, and somatic mutations. Through weighted gene co-expression network analysis (WGCNA) and four machine learning algorithms, two core genes were found and a diagnostic model was constructed. This was subsequently validated in a clinical immunotherapy cohort. Single cell sequencing analysis and experiments demonstrated the role of core genes in ccRCC. Finally, we also analyzed the roles of MMP9 and SCGN in pan-cancer. Results: We described two novel collagen related molecular subtypes in ccRCC, designated subtype 1 and subtype 2. Compared with subtype 1, subtype 2 showed more infiltration of immune components, but had a higher TIDE (tumor immunedysfunctionandexclusion) score and increased levels of immune checkpoint molecules. Furthermore, reduced prognosis for subtype 2 was a consistent finding in both high and low mutation load subgroups. MMP9 and SCGN were identified as key genes for distinguishing subtype 1 and subtype 2. The diagnostic model based on them could better distinguish the subtype of patients, and the differentiated patients had different progression free survival (PFS) in the clinical immunotherapy cohort. MMP9 was predominantly expressed in macrophages and has been extensively documented in the literature. Meanwhile, SCGN, which was overexpressed in tumor cells, underwent experimental validation, emphasizing its role in ccRCC. In various cancers, MMP9 and SCGN were associated with immune-related molecules and immune cells. Conclusion: Our study identifies two collagen-related molecular subtypes of ccRCC and constructs a diagnostic model to help select appropriate patients for immunotherapy.

A study on the role of Taxifolin in inducing apoptosis of pancreatic cancer cells: screening results using weighted gene co-expression network analysis

Pancreatic adenocarcinoma (PAAD) is a frequent malignant tumor in the pancreas. The incomplete understanding of cancer etiology and pathogenesis, as well as the limitations in early detection and diagnostic methods, have created an urgent need for the discovery of new therapeutic targets and drugs to control this disease. As a result, the current therapeutic options are limited. In this study, the weighted gene co-expression network analysis (WGCNA) method was employed to identify key genes associated with the progression and prognosis of pancreatic adenocarcinoma (PAAD) patients in the Gene Expression Profiling Interactive Analysis (GEPIA) database. To identify small molecule drugs with potential in the treatment of pancreatic adenocarcinoma (PAAD), we compared key genes to the reference dataset in the CMAP database. First, we analyzed the antitumor properties of small molecule drugs using cell counting kit-8 (CCK-8), AO/EB and Transwell assays. Subsequently, we integrated network pharmacology with molecular docking to explore the potential mechanisms of the identified molecules' anti-tumor effects. Our findings indicated that the progression and prognosis of PAAD patients in pancreatic cancer were associated with 11 genes, namely, DKK1, S100A2, CDA, KRT6A, ITGA3, GPR87, IL20RB, ZBED2, PMEPA1, CST6, and MUC16. These genes were filtered based on their therapeutic potential through comparing them with the reference dataset in the CMAP database. Taxifolin, a natural small molecule drug with the potential for treating PAAD, was screened by comparing it with the reference dataset in the CMAP database. Cell-based experiments have validated the potential of Taxifolin to facilitate apoptosis in pancreatic cancer cells while restraining their invasion and metastasis. This outcome is believed to be achieved via the HIF-1 signaling pathway. In conclusion, this study provided a theoretical basis for screening genes related to the progression of pancreatic cancer and discovered potentially active small molecule drugs. The experimental results confirm that Taxifolin has the ability to promote apoptosis in pancreatic cancer cells.

LINC00887 regulates malignant progression and T-cell chemotaxis in clear cell renal cell carcinoma by activating CD70 via recruitment of SPI1

BACKGROUND

LINC00887 has been mentioned in several articles regarding its involvement in various cancers like nasopharyngeal carcinoma, lung cancer and glioma. However, the mechanism of LINC00887 in the malignant progression of clear cell renal cell carcinoma (ccRCC) is still unclear. The topic of our study is mainly centered on exploring how LINC00887 exactly affects ccRCC malignant progression.

METHODS

The bioinformatics method predicted the downstream TF and target genes of LINC00887 by the "LncRNA-transcription factor (TF)-Gene" triplet model. RNA immunoprecipitation, chromatin immunoprecipitation analysis, and Dual-luciferase reporter assay determined the regulatory relationship between LINC00887 and its downstream genes. The LINC00887 expression and its downstream gene expression in ccRCC cells were examined by qRT-PCR and Western blot. The effect of LINC00887-SPI1-CD70 modulation axis on proliferative transfer, cell stemness and T cell chemotaxis of ccRCC cells was examined in cellular and animal experiments.

RESULTS

Our research demonstrated an upregulation of LINC00887 in ccRCC, which facilitated tumor growth and stemness in vivo. In addition, LINC00887 could upregulate the CD70 expression by recruiting transcriptional factor SPI1. The results of in vitro experiments illustrated that the LINC00887-SPI1-CD70 regulatory axis facilitated ccRCC malignant progression by promoting cell stemness and hindering T-cell chemotaxis.

CONCLUSION

LINC00887, by recruiting SPI1, activated CD70 transcription, thereby propelling malignant progression and cell stemness and suppressing T cell chemotaxis in ccRCC. Based on our findings, we believed that the LINC00887-SPI1-CD70 regulatory axis had the potential to be a critical breakthrough for treating ccRCC.

Abnormal expression of FOXM1 in carcinogenesis of renal cell carcinoma: From experimental findings to clinical applications

Renal cell carcinoma (RCC) is a prevalent malignancy within the genitourinary system. At present, patients with high-grade or advanced RCC continue to have a bleak prognosis. Mounting research have emphasized the significant involvement of Forkhead box M1 (FOXM1) in RCC development and progression. Therefore, it is imperative to consolidate the existing evidence regarding the contributions of FOXM1 to RCC tumorigenesis through a comprehensive review. This study elucidated the essential functions of FOXM1 in promoting RCC growth, invasion, and metastasis by regulating cell cycle progression, DNA repair, angiogenesis, and epithelial-mesenchymal transition (EMT). Also, FOXM1 might serve as a novel diagnostic and prognostic biomarker as well as a therapeutic target for RCC. Clinical findings demonstrated that the expression of FOXM1 was markedly upregulated in RCC samples, while a high level of FOXM1 was found to be associated with a poor overall survival rate of RCC. Furthermore, it is worth noting that FOXM1 may have a significant impact on the resistance of renal cell carcinoma (RCC) to radiotherapy. This observation suggests that inhibiting FOXM1 could be a promising strategy to impede the progression of RCC and enhance its sensitivity to radiotherapy. The present review highlighted the pivotal role of FOXM1 in RCC development. FOXM1 has the capacity to emerge as not only a valuable diagnostic and prognostic tool but also a viable therapeutic option for unresectable RCC.

Multiomics characterization and verification of clear cell renal cell carcinoma molecular subtypes to guide precise chemotherapy and immunotherapy

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor with different genetic and molecular alterations. Schemes for ccRCC classification system based on multiomics are urgent, to promote further biological insights. Two hundred and fifty-five ccRCC patients with paired data of clinical information, transcriptome expression profiles, copy number alterations, DNA methylation, and somatic mutations were collected for identification. Bioinformatic analyses were performed based on our team's recently developed R package "MOVICS." With 10 state-of-the-art algorithms, we identified the multiomics subtypes (MoSs) for ccRCC patients. MoS1 is an immune exhausted subtype, presented the poorest prognosis, and might be caused by an exhausted immune microenvironment, activated hypoxia features, but can benefit from PI3K/AKT inhibitors. MoS2 is an immune "cold" subtype, which represented more mutation of VHL and PBRM1, favorable prognosis, and is more suitable for sunitinib therapy. MoS3 is the immune "hot" subtype, and can benefit from the anti-PD-1 immunotherapy. We successfully verified the different molecular features of the three MoSs in external cohorts GSE22541, GSE40435, and GSE53573. Patients that received Nivolumab therapy helped us to confirm that MoS3 is suitable for anti-PD-1 therapy. E-MTAB-3267 cohort also supported the fact that MoS2 patients can respond more to sunitinib treatment. We also confirm that SETD2 is a tumor suppressor in ccRCC, along with the decreased SETD2 protein level in advanced tumor stage, and knock-down of SETD2 leads to the promotion of cell proliferation, migration, and invasion. In summary, we provide novel insights into ccRCC molecular subtypes based on robust clustering algorithms via multiomics data, and encourage future precise treatment of ccRCC patients.

The mitochondrial fusion-associated protein MFN2 can be used as a novel prognostic molecule for clear cell renal cell carcinoma

BACKGROUND

Mitofusin 2 (MFN2) plays an important role in many tumors, but how its role in renal clear cell carcinoma needs further research.

METHODS

In this study, we analyzed the expression of MFN2 in renal clear cell carcinoma tissues and normal kidney tissues through the Cancer Genome Atlas (TCGA) database and our clinical samples.Enrichment analysis was performed to determine MFN2-related pathways and biological functions. The correlation of MFN2 expression with immune cells was analyzed.The correlation of the expression of methylation and the methylation sites of MFN2 were analyzed by UALCAN and TCGA databases. Univariate / multivariate COX risk regression and Kaplan-Meier methods were used to determine the prognostic value of MFN2.Nomograms were drawn to predict overall survival (OS) at 1,3, and 5 years. We investigated the role of MFN2 in renal cancer cells using CCK 8, clone formation, wound healing assay, and methylase qPCR experiments.

RESULTS

MFN2 is poorly expressed in renal clear cell carcinoma compared to normal kidney tissue,and is significantly negatively associated with TNM stage, histological grade and pathological stage.MFN2 was directly associated with OS after multivariate Cox regression analysis.MFN2 shows a hypomethylation state and shows a positive correlation with multiple methylation sites.Signaling pathways through functional enrichment to B-cell receptors and oxidative stress-induced senescence.Moreover, the low expression of MFN2 was positively correlated with the degree of immune cell infiltration in a variety of immune cells.In vitro experiments showed that overexpression of MFN2 significantly inhibited the proliferation and migration of renal clear cells and promoted methylation.

CONCLUSIONS

In conclusion, MFN2 can be used as a novel prognostic marker for renal clear cell carcinoma and requires further investigation of its role in tumor development.

Identification of ATP6V0A4 as a potential biomarker in renal cell carcinoma using integrated bioinformatics analysis

Clear cell renal cell carcinoma (ccRCC) is the most common pathological type of renal cancer, and is associated with a high mortality rate, which is related to high rates of tumor recurrence and metastasis. The aim of the present study was to identify reliable molecular biomarkers with high specificity and sensitivity for ccRCC. A total of eight ccRCC-related expression profiles were downloaded from Gene Expression Omnibus for integrated bioinformatics analysis to screen for significantly differentially expressed genes (DEGs). Reverse transcription-quantitative (RT-q)PCR, western blotting and immunohistochemistry staining assays were performed to evaluate the expression levels of candidate biomarkers in ccRCC tissues and cell lines. In total, 255 ccRCC specimens and 165 adjacent normal kidney specimens were analyzed, and 344 significant DEGs, consisting of 115 upregulated DEGs and 229 downregulated DEGs, were identified. The results of Gene Ontology analysis suggested a significant enrichment of DEGs in 'organic anion transport' and 'small molecule catabolic process' in biological processes, in 'apical plasma membrane' and 'apical part of the cell' in cell components, and in 'anion transmembrane transporter activity' and 'active transmembrane transporter activity' in molecular functions. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis indicated that the DEGs were significantly enriched in the 'phagosome', the 'PPAR signaling pathway', 'complement and coagulation cascades', the 'HIF-1 signaling pathway' and 'carbon metabolism'. Next, 7 hub genes (SUCNR1, CXCR4, VCAN, CASR, ATP6V0A4, VEGFA and SERPINE1) were identified and validated using The Cancer Genome Atlas database. Survival analysis showed that low expression of ATP6V0A4 was associated with a poor prognosis in patients with ccRCC. Additionally, received operating characteristic curves indicated that ATP6V0A4 could distinguish ccRCC samples from normal kidney samples. Furthermore, RT-qPCR, western blotting and immunohistochemistry staining results showed that ATP6V0A4 was significantly downregulated in ccRCC tissues and cell lines. In conclusion, ATP6V0A4 may be involved in tumor progression and regarded as a potential therapeutic target for the recurrence and metastasis of ccRCC.

Exploring molecular markers and drug candidates for colorectal cancer through comprehensive bioinformatics analysis

Colorectal cancer (CRC) often has a poor prognosis and identifying useful and novel agents for treating CRC is urgently required. This study aimed to examine molecular markers associated with CRC prognosis and to identify potential drug candidates. The differentially expressed genes (DEGs) of CRC in TCGA were identified. The genes associated with CRC, summarized from NCBI-gene, OMIM, and the DEGs, were used to construct a co-expression network by WGCNA. Moreover, the co-expression genes from modules of interest were used to carry out functional enrichment. A total of 2742 DEGs, including 1674 upregulated and 1068 downregulated genes, were identified. Thirteen co-expression modules were constructed with WGCNA. Brown and blue co-expression modules with significant differences in disease phenotype were found. Functional enrichment analysis showed that genes in the brown module were mainly related to cell cycle, cell proliferation, DNA replication, and RNA transport. The genes in the blue module were mainly associated with fatty acid degradation, sulfur metabolism, PPAR signaling pathway and bile secretion. In addition, both the genes in brown and blue were associated with tumor staging. Some prognostic markers and candidate small molecules drugs for CRC treatment were identified. In conclusion, we revealed molecular biomarker profiles in CRC by systematic bioinformatics analysis, constructed regulatory networks of mRNA, ncRNA and transcriptional regulators (TFs), and identified potential drugs targeting hub proteins and TFs.

Identify AGAP2 as prognostic biomarker in clear cell renal cell carcinoma based on bioinformatics and IHC staining

Background

Arf GTPase-activating proteins are aberrantly expressed in a variety of tumors, but their role in clear cell renal cell carcinoma (ccRCC) was unclear. Exploring the biological role of Arf GAP with GTP binding protein like domain, Ankyrin repeat and PH domain 2 (AGAP2) in ccRCC could improve our understanding on the aggressiveness and immune relevance of ccRCC.

Methods

The expression of AGAP2 was analyzed based on the Cancer Genome Atlas (TCGA) database and verified in ccRCC samples using immunohistochemistry. The association between AGAP2 and clinical cancer stages was explored by TCGA dataset and UALCAN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were performed to analyze the biological functions of AGAP2-related genes. Moreover, the relationship between AGAP2 and immune cell infiltration was investigated with TIME and TCGA dataset.

Results

Compared to normal tissues, AGAP2 was upregulated in ccRCC tissues. Higher expression of AGAP2 was associated with clinical cancer stages, TNM stages, pathologic stages, and status. Prognostic analysis on AGAP2 showed that AGAP2 overexpression was associated with KIRC overall survival (OS) reduction (P = 0.019). However, higher expression of AGAP2 may improve the OS of CESC (P = 0.002), THYM (P = 0.006) and UCEC (P = 0.049). GO and KEGG analysis showed that AGAP2-related genes was related to T cell activation, immune activity and PD-L1 expression and PD-1 checkpoint pathway. Furthermore, our study showed that AGAP2 were significantly associated with T cells, Cytotoxic cells, Treg, Th1 cells, CD8 T cells, T helper cells. And AGAP2 expression level affected the abundance of immune cells infiltration. The infiltrating level of immune cells was different between the AGAP2 high-expression and low-expression groups.

Conclusion

The expression of AGAP2 in ccRCC was higher than that in normal kidney tissues. It was significantly associated with clinical stage, poor prognosis, and immune cell infiltration. Therefore, AGAP2 may become an important component for ccRCC patients who receive precision cancer therapy and may be a promising prognostic biomarker.

A 9-gene prognostic signature for kidney renal clear cell carcinoma overall survival based on co-expression and regression analyses

This research attempted to screen potential signatures associated with KIRC progression and overall survival by weighted gene co-expression network analysis (WGCNA) and Cox regression. The KIRC-associated mRNA expression and clinical data were accessed from The Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were screened by differential analysis. A co-expression network was constructed by "WGCNA". Based on WGCNA module, GO and KEGG analyses were performed. Protein-protein interaction (PPI) network was constructed. Prognostic signatures were screened by Lasso-Cox regression. Prognostic model was evaluated by Receiver Operating Characteristic (ROC) and Kaplan-Meier (K-M) curves. Multivariate Cox and nomogram were introduced to examine whether risk score could be an independent marker. qRT-PCR was introduced to determine expression of 9 hub genes in KIRC clinical tumor tissues and adjacent tissues, respectively. Genes in the green module were highly associated with clinical status, and green module genes were significantly enriched in mitotic nuclear division, cell cycle, and p53 signaling pathway. Twenty-six candidates were subsequently screened out from the green module. Next, a 9-gene prognostic model (DLGAP5, NUF2, TOP2A, RRM2, HJURP, PLK1, AURKB, KIF18A, CCNB2) was constructed. The predicting ability of the model was optimal. Some cancer-related signaling pathways were differently activated between two risk score groups. Additionally, under-expression of some signature genes (AURKB, CCNB2, PLK1, RRM2, TOP2A) was associated with better survival rate for KIRC patients. Meanwhile, all 9 hub genes were substantially overexpressed in KIRC patients. A KIRC prognostic signature was screened in this study, contributing valuable findings to KIRC biomarker development.

Microplastics existence intensified bloom of antibiotic resistance in livestock feces transformed by black soldier fly

Efficient degradation of residual antibiotics in livestock and poultry feces by black soldier flies (BSFs) has been widely reported. Nevertheless, the effects of widely detected microplastics in feces on the dynamic reduction of antibiotics and the transfer of gut bacterial resistome remain unclear. In this study, red fluorescence-labeled microplastics are observed to be abundantly distributed in BSFs gut, which caused epithelial cell damage along with gut peristalsis and friction, thereby releasing reactive oxygen species and activating the antioxidant enzyme system. In addition, they result in not only in inflammatory cytokine release to induce gut inflammation, but fecal hardening because of mucus released from the BSFs, thereby hindering organic mineralization and antibiotic degradation. Besides, the gut pathogenic bacteria easily obtain growth energy and crowded out ecological niches by reducing nitrate produced by inflammatory host cells to nitrite with nitrate reductase. Consequently, linear discriminant analysis effect size and detrended correspondence analysis found that microplastic intake significantly reshape the microbial community structure and cause the significant reduction of several important organic-decomposing bacteria and probiotics (e.g., Pseudomonadales, Coriobacteriales, Lachnospirales, and Ruminococcaceae). In addition, a large number of pathogenic bacteria (e.g., Enterococcaceae, Hungateiclostridiaceae, and Clostridia) are enriched in feces and BSFs gut. Weighted correlation network analysis and bubble diagram analysis indicate that microplastic intake intensified gut colonization of pathogenic bacteria carrying antibiotic-resistant genes/mobile genetic elements, driving the bloom of antibiotic resistance in transformed fecal piles. Therefore, microplastics in feces should be isolated as much as possible before insect transformation.

Identification of a Novel Myc-Regulated Gene Signature for Patients with Kidney Renal Clear Cell Carcinoma

Given that myc was known to be a cancer-causing gene in several cancers including kidney renal clear cell carcinoma (KIRC). We aimed to construct myc-regulated genes (MRGs)-based prognostic signature. We obtained the mRNA expression and clinical data of KIRC from The Cancer Genome Atlas (TCGA) database and MRGs from the Molecular Signature Database (MSigDB). Then, a prognostic signature consisting of 8 MRGs (IRF9, UBE2C, YBX3, CDKN2B, CKAP2L, CYFIP2, FBLN5, and PDLIM7) was developed by differential expression analysis, cox regression analysis, and least absolute shrinkage and selection operator (lasso) analysis. Patients with KIRC were divided into high- and low-risk groups based on risk scores of MRGs-based signatures. Patients in the high-risk group showed inferior clinical characteristics and survival. In addition, the risk score was an independent prognostic factor for KIRC, and the risk score=based nomogram displayed satisfactory performance to predict the survival of KIRC. The MRGs-based signature is also correlated with immune cell infiltration and the mRNA expression of important immune checkpoints (IDO2, PDCD1, LAG3, FOXP3, and TIGIT). The tumor mutation burden (TMB) landscape between the high- and low-risk groups showed higher levels of TMB in the high-risk group than in the low-risk group and that higher levels of TMB predicted a poorer prognosis in KIRC. Furthermore, patients with KIRC in the high-risk group are more likely to experience immune escape. At last, we found patients with KIRC in the high-risk group were more sensitive to several chemotherapy drugs such as sunitinib, gefitinib, nilotinib, and rapamycin than patients with KIRC in the low-risk group. Our study successfully constructed and validated an MRGs-based signature that can predict clinical characteristics, prognosis, level of immune infiltration, and responsiveness to immunotherapy and chemotherapy drugs in patients with KIRC.

Bioinformatics analysis of immune-related prognostic genes and immunotherapy in renal clear cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is an immunogenic tumor, and investigating the immunorelated genes is essential. To investigate the immunoprognostic genes of ccRCC, we analyzed the data assimilated from a public database (The Cancer Genome Atlas (TCGA) database and the gene expression omnibus (GEO) database) using bioinformatics. Then, an immunoprognosis model was constructed to identify four hub genes with moderate predictive values for the prognosis of ccRCC patients. These four genes were associated with the prognosis of ccRCC patients based on Oncomine and Gena Expression Profiling Interactive Analysis (GEPIA) databases. The correlation analysis between the immune infiltrate, immune checkpoints, and immunotherapy and this immunoprognosis model showed that immune infiltration could predict the immunotherapy effects. We also conducted a quantitative real-time polymerase chain reaction analysis and found that the expressions of three hub genes were associated with tumor progression (P<0.1). In conclusion, four genes that may serve as potential biomarkers in ccRCC were identified with respect to prognosis.

Identification and validation of ubiquitin-proteasome system related genes as a prognostic signature for papillary renal cell carcinoma

Dysregulation of the ubiquitin-proteasome system (UPS) pathway greatly affects uncontrolled proliferation, genomic instability, and carcinogenesis, particularly in those with renal papillary cell carcinoma (PRCC). However, there is little information at the molecular level about the full link between changes in the genes involved in ubiquitin-mediated proteolysis and PRCC.

METHODS

The Cancer Genome Atlas (TCGA) and GeneCards databases were utilized to find the clinical data and gene expression patterns of patients with PRCC. Univariate Cox regression analysis and absolute shrinkage and selection operator (LASSO) analyses identified a risk signature formed by ten optimal UPS genes. The predictive value of the risk signature in TCGA-PRCC cohorts was evaluated using Kaplan-Meier analysis and receiver operating characteristic (ROC) curves. By utilizing GO enrichment and the KEGG pathway, the interactions of differentially expressed genes connected to ubiquitin-mediated proteolysis were functionally examined. The protein expression of the hub genes was affirmed using the Human Protein Atlas (HPA) database. The effectiveness of particular CDC20 and UBE2C in vitro was confirmed by experimental research.

RESULTS

Ten of the best ubiquitin-mediated proteolysis genes (UBE2C, DDB2, CBLC, BIRC3, PRKN, UBE2O, SIAH1, SKP2, UBC, and CDC20) were detected to create a risk signature. The high-risk score group stratified was associated with advanced tumor status and poor survival of PRCC patients. 10 genes were also found to be associated with the cell cycle pathway and ubiquitin-mediated proteolysis to GO and KEGG analysis. Of these 10 genes, CDC20 and UBE2C are highly expressed in tumor tissue and correlated with cancer immunity founded on the analyses of the expression of human protein atlas and TISIDB. The downregulation of UBE2C facilitated tumor inhibition and the anti-immune effect was confirmed by in vitro experiments.

CONCLUSION

Our results indicate that the risk model created from the ubiquitin-mediated proteolysis genes can be reliably and accurately predict the prognosis of PRCC patients, highlighting its targeted value for PRCC treatment. Particularly, the expression of UBE2C may be crucial for the prognosis and immunological treatment of renal cancer.

Identification of Survival Risk and Immune-Related Characteristics of Kidney Renal Clear Cell Carcinoma

Background

Immunity exerts momentous functions in the progression and treatment of kidney renal clear cell carcinoma (KIRC). A better understanding of the relationship between KIRC and immunity may make a great contribution to evaluating the prognosis and immune-related therapeutic response of KIRC.

Methods

A series of information such as RNA sequence, clinical data, and tumor mutation burden (TMB) of KIRC patients were downloaded through The Cancer Genome Atlas (TCGA). Next, combining the survival information and gene expression data of TCGA and Gene Expression Omnibus (GEO), we established an immune gene-related prognosis model (IGRPM) and analyzed it. Then we constructed a nomogram which was convenient for clinicians to judge the prognosis of KIRC. Last but not the least, the expressions of some genes used to construct IGRPM in early KIRC, and adjacent normal tissues were verified through real-time fluorescence quantitative polymerase chain reaction (RT-qPCR). Perl (strawberry-perl-5.30.0.1-64bit), R software (4.0.3), and GraphPad Prism 7 were used to process the relevant data.

Results

The single-sample gene set enrichment analysis (ssGSEA) showed that there were significant differences in StromalScore, ImmuneScore, ESTIMATEScore, TumorPurity, 22 kinds of human immune cells infiltration, and HLA genes expression between high immunity group (Immunity_H) and low immunity group (Immunity_L). The Immunity_H expressed more immune-related genes and enriched more immune-related functions than the Immunity_L. In addition, compared with the low-risk group, the high-risk group had worse survival outcome and higher TMB. Combining IGRPM-based risk characteristic and TMB, we found that low-TMB + low-risk was the most beneficial to the survival outcome of KIRC patients. The risk characteristic based on IGRPM could be used as an independent prognostic factor for KIRC, and the nomogram constructed for evaluating the prognosis of KIRC showed excellent predictive potential. The RT-qPCR results suggested that not all the genes used to construct IGRPM showed differential expression in early KIRC compared with adjacent normal tissues, but all these genes had significant influence on the prognosis of KIRC.

Conclusion

These comprehensive immune assessments and survival predictions, integrating multiple aspects of data and clinical information, can provide additional value to the current Tumor Node Metastasis staging system for risk stratification of KIRC and may facilitate the development of KIRC immunotherapy.

METTL14-mediated N6-methyladenosine modification of Pten mRNA inhibits tumour progression in clear-cell renal cell carcinoma

BACKGROUND

Clear-cell renal-cell carcinoma (ccRCC) is one of the leading causes of tumour-related death worldwide. Methyltransferase-like 14 (METTL14) is reported to regulate m6A modification in cancers. The aim of this study is to investigate the biological function and molecular mechanism of METTL14 in the pathogenesis of ccRCC.

METHODS

Quantitative real-time PCR (qRT-PCR), western blot and immunohistochemical (IHC) assays were used to detect the expression of METTL14 and Pten. METTL14 overexpression or knockdown was used in the in vitro and in vivo studies to investigate the biological functions of METTL14. m6A-RNA immunoprecipitation and RNA immunoprecipitation were used to investigate the m6A modification mediated by METTL14.

RESULTS

METTL14 expression was significantly down-regulated in ccRCC tissues. Functionally, upregulation of METTL14 inhibited ccRCC cells proliferation and migration in vitro. METTL14 overexpression significantly inhibited the activation of the phosphoinositide 3 kinase (PI3K)/AKT signalling pathway. Furthermore, phosphate and tension homology deleted on chromosome ten (Pten) is a target of METTL14. Overexpression of METTL14 increased the m6A enrichment of Pten, and promoted Pten expression. METTL14-enhanced Pten mRNA stability was dependent on YTHDF1.

CONCLUSIONS

METTL14-mediated m6A modification of Pten mRNA inhibited tumour progression, suggesting that METTL14 might be a potential prognostic biomarker and effective therapeutic target for ccRCC.

A Potential Nine-lncRNAs Signature Identification and Nomogram Diagnostic Model Establishment for Papillary Thyroid Cancer

The purpose of our current study was to establish a long non-coding RNA(lncRNA) signature and assess its prognostic and diagnostic power in papillary thyroid cancer (PTC). LncRNA expression profiles were obtained from the Cancer Genome Atlas (TCGA). The key module and hub lncRNAs related to PTC were determined by weighted gene co-expression network analysis (WGCNA) and LASSO Cox regression analyses, respectively. Functional enrichment analyses, including Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) and gene set enrichment analysis were implemented to analyze the possible biological processes and signaling pathways of hub lncRNAs. Associations between key lncRNA expressions and tumor-infiltrating immune cells were identified using the TIMER website, and proportions of immune cells in high/low risk score groups were compared. Kaplan-Meier Plotter was used to evaluate the prognostic significance of hub genes in PTC. A diagnostic model was conducted with logistic regression analysis, and its diagnostic performance was assessed by calibration/receiver operating characteristic curves and principal component analysis. A nine-lncRNAs signature (SLC12A5-AS1, LINC02028, KIZ-AS1, LINC02019, LINC01877, LINC01444, LINC01176, LINC01290, and LINC00581) was established in PTC, which has significant diagnostic and prognostic power. Functional enrichment analyses elucidated the regulatory mechanism of the nine-lncRNAs signature in the development of PTC. This signature and expressions of nine hub lncRNAs were correlated with the distributions of tumor infiltrating immune cells. A diagnostic nomogram was also established for PTC. By comparing with the published models with less than or equal to nine lncRNAs, our signature showed a preferable performace for prognosis prediction. In conclusion, our present research established an innovative nine-lncRNAs signature and a six-lncRNAs nomogram that might act as a potential indicator for PTC prognosis and diagnosis, which could be conducive to the PTC treatment.

A Comprehensive Pan-Cancer Analysis for Pituitary Tumor-Transforming Gene 1

Pituitary tumor-transforming gene 1 (PTTG1) encodes a multifunctional protein that is involved in many cellular processes. However, the potential role of PTTG1 in tumor formation and its prognostic function in human pan-cancer is still unknown. The analysis of gene alteration, PTTG1 expression, prognostic function, and PTTG1-related immune analysis in 33 types of tumors was performed based on various databases such as The Cancer Genome Atlas database, the Genotype-Tissue Expression database, and the Human Protein Atlas database. Additionally, PTTG1-related gene enrichment analysis was performed to investigate the potential relationship and possible molecular mechanisms between PTTG1 and tumors. Overexpression of PTTG1 may lead to tumor formation and poor prognosis in various tumors. Consequently, PTTG1 acts as a potential oncogene in most tumors. Additionally, PTTG1 is related to immune infiltration, immune checkpoints, tumor mutational burden, and microsatellite instability. Thus, PTTG1 could be potential biomarker for both prognosis and outcomes of tumor treatment and it could also be a promising target in tumor therapy.

TIMP1 Indicates Poor Prognosis of Renal Cell Carcinoma and Accelerates Tumorigenesis via EMT Signaling Pathway

Renal cell carcinoma (RCC) is one of the most common malignancies in the urinary system. The mortality of advanced RCC remains high despite advances in systemic therapy of RCC. Considering the misdiagnosis of early-stage RCC, the identification of effective biomarkers is of great importance. Tissue inhibitor matrix metalloproteinase 1 (TIMP1), which belongs to TIMP gene family, is a natural inhibitor of the matrix metalloproteinases (MMPs). In this study, we found TIMP1 was significantly up-regulated in cell lines and RCC tissues. Kaplan-Meier analysis revealed that high expression of TIMP1 indicated a poor prognosis. Multivariate analysis further indicated that TIMP1 overexpression was an independent prognostic factor of RCC patients. Furthermore, knockdown of TIMP1 in vitro suppressed the proliferation, migration, and invasion of RCC cells, while upregulating TIMP1 accelerated the proliferation, migration, and invasion of RCC cells. In addition, we also found that TIMP1 prompted the progression of RCC via epithelial-to-mesenchymal transition (EMT) signaling pathway. In conclusion, the present results suggested that TIMP1 indicated poor prognosis of renal cell carcinoma and could serve as a potential diagnostic and prognostic biomarker for RCC.

Identification and Quantification of Necroptosis Landscape on Therapy and Prognosis in Kidney Renal Clear Cell Carcinoma

Kidney renal clear cell carcinoma (KIRC) has high morbidity and gradually increased in recent years, and the rate of progression once relapsed is high. At present, owing to lack of effective prognosis predicted markers and post-recurrence drug selection guidelines, the prognosis of KIRC patients is greatly affected. Necroptosis is a regulated form of cell necrosis in a way that is independent of caspase. Induced necroptosis is considered an effective strategy in chemotherapy and targeted drugs, and it can also be used to improve the efficacy of immunotherapy. Herein, we quantified the necroptosis landscape of KIRC patients from The Cancer Genome Atlas (TCGA) database and divided them into two distinct necroptosis-related patterns (C1 and C2) through the non-negative matrix factorization (NMF) algorithm. Multi-analysis revealed the differences in clinicopathological characteristics and tumor immune microenvironment (TIME). Then, we constructed the NRG prognosis signature (NRGscore), which contained 10 NRGs (PLK1, APP, TNFRSF21, CXCL8, MYCN, TNFRSF1A, TRAF2, HSP90AA1, STUB1, and FLT3). We confirmed that NRGscore could be used as an independent prognostic marker for KIRC patients and performed excellent stability and accuracy. A nomogram model was also established to provide a more beneficial prognostic indicator for the clinic. We found that NRGscore was significantly correlated with clinicopathological characteristics, TIME, and tumor mutation burden (TMB) of KIRC patients. Moreover, NRGscore had effective guiding significance for immunotherapy, chemotherapy, and targeted drugs.

Identification of UBE2I as a Novel Biomarker in ccRCC Based on a Large-Scale CRISPR-Cas9 Screening Database and Immunohistochemistry

Background: The genome-wide CRISPR-cas9 dropout screening has emerged as an outstanding approach for characterization of driver genes of tumor growth. The present study aims to investigate core genes related to clear cell renal cell carcinoma (ccRCC) cell viability by analyzing the CRISPR-cas9 screening database DepMap, which may provide a novel target in ccRCC therapy.

Methods: Candidate genes related to ccRCC cell viability by CRISPR-cas9 screening from DepMap and genes differentially expressed between ccRCC tissues and normal tissues from TCGA were overlapped. Weighted gene coexpression network analysis, pathway enrichment analysis, and protein–protein interaction network analysis were applied for the overlapped genes. The least absolute shrinkage and selection operator (LASSO) regression was used to construct a signature to predict the overall survival (OS) of ccRCC patients and validated in the International Cancer Genome Consortium (ICGC) and E-MTAB-1980 database. Core protein expression was determined using immunohistochemistry in 40 cases of ccRCC patients.

Results: A total of 485 essential genes in the DepMap database were identified and overlapped with differentially expressed genes in the TCGA database, which were enriched in the cell cycle pathway. A total of four genes, including UBE2I, NCAPG, NUP93, and TOP2A, were included in the gene signature based on LASSO regression. The high-risk score of ccRCC patients showed worse OS compared with these low-risk patients in the ICGC and E-MTAB-1980 validation cohort. UBE2I was screened out as a key gene. The immunohistochemistry indicated UBE2I protein was highly expressed in ccRCC tissues, and a high-level nuclear translocation of UBE2I occurs in ccRCC. Based on the area under the curve (AUC) values, nuclear UBE2I had the best diagnostic power (AUC = 1). Meanwhile, the knockdown of UBE2I can inhibit the proliferation of ccRCC cells.

Conclusion: UBE2I, identified by CRISPR-cas9 screening, was a core gene-regulating ccRCC cell viability, which accumulated in the nucleus and acted as a potential novel promising diagnostic biomarker for ccRCC patients. Blocking the nuclear translocation of UBE2I may have potential therapeutic value with ccRCC patients.

DTL Is a Prognostic Biomarker and Promotes Bladder Cancer Progression through Regulating the AKT/mTOR axis

Background

Denticleless E3 ubiquitin protein ligase homolog (DTL) has been reported to be an important regulator for tumorigenesis and progression. Nonetheless, the biological functions and molecular mechanisms of DTL in BCa remain elusive.

Methods

We implemented integrative bioinformatics analysis to explore the diagnostic and prognostic values of DTL based on The Cancer Genome Atlas (TCGA), ArrayExpress, and Gene Expression Omnibus (GEO) databases. Then, we utilized qRT-PCR and immunohistochemistry to verify the clinical significance of DTL expression according to clinical specimens and tissue microarray (TMA). Moreover, the biological functions and underlying mechanisms of DTL in BCa were investigated through in vitro and in vivo experiments.

Results

Integrative bioinformatics analysis revealed that DTL was a key gene associated with BCa progression, and increased DTL expression was correlated with malignant biological behavior and poor prognosis. Experiments on clinical specimens and tissue microarray (TMA) further confirmed our findings. Bioinformatics analysis demonstrated that DTL could be associated with cell cycle- and DNA replication-associated pathways in BCa. The suppression of DTL inhibited BCa cell proliferation, migration, and invasion in vivo and in vitro. Mechanistically, DTL may promote BCa progression through the AKT/mTOR pathway.

Conclusions

Increased DTL expression was correlated with malignant biological behavior and poor prognosis of BCa patients, and it may promote BCa progression through the AKT/mTOR pathway. Our research provided a potential predictor and therapeutic target for BCa.

Identification of Differentially Expressed and Prognostic lncRNAs for the Construction of ceRNA Networks in Lung Adenocarcinoma

Background

Long noncoding RNAs (lncRNAs) could function as competitive endogenous RNAs (ceRNAs) to competitively adsorb microRNAs (miRNAs), thereby regulating the expression of their target protein-coding mRNAs. In this study, we aim to identify more effective diagnostic and prognostic markers for lung adenocarcinoma (LUAD).

Methods

We obtained differentially expressed lncRNAs (DElncRNAs), miRNAs (DEmiRNAs), and mRNAs (DEmRNAs) for LUAD by using The Cancer Genomes Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) was performed to unveil core gene modules associated with LUAD. The Cox proportional hazards model was performed to determine the prognostic significance of DElncRNAs. The diagnostic and prognostic significance of DElncRNAs was further verified based on the receiver operating characteristic curve (ROC). Cytoscape was used to construct the ceRNA networks comprising the lncRNAs-miRNAs-mRNAs axis based on the correlation obtained from the miRcode, miRDB, and TargetScan.

Results

Compared with normal lung tissues, 2355 DElncRNAs, 820 DEmiRNAs, and 17289 DEmRNAs were identified in LUAD tissues. We generated 8 WGCNA core modules in the lncRNAs coexpression network, 5 modules in the miRNAs, and 12 modules in the mRNAs coexpression network, respectively. One lncRNA module (blue) consisting of 441 lncRNAs, two miRNA modules (blue and turquoise) containing 563 miRNAs, and one mRNA module (turquoise), which consisted of 15162 mRNAs, were mostly significantly related to LUAD status. Furthermore, 67 DEmRNAs were found to be tumor-associated as well as the target genes of the DElncRNAs-DEmiRNAs axis. Survival analyses showed that 6 lncRNAs (LINC01447, WWC2-AS2, OGFRP1, LINC00942, LINC01168, and AC005863.1) were significantly correlated with the prognosis of LUAD patients. Ultimately, the potential ceRNA networks including 6 DElncRNAs, 4 DEmiRNAs, and 22 DEmRNAs were constructed.

Conclusion

Our study indicated that 6 DElncRNAs had the possibilities as diagnostic and prognostic biomarkers for LUAD. The lncRNA-mediated ceRNA networks might provide novel insights into the molecular mechanisms of LUAD progression.

Comprehensive Pan-Cancer Analysis of the Prognostic and Immunological Roles of the METTL3/lncRNA-SNHG1/miRNA-140-3p/UBE2C Axis

Growing evidence has demonstrated that UBE2C plays a critical role in cancer progression, but there is no study focusing on the prognosis, upstream regulation mechanism, and immunological roles of UBE2C across diverse tumor types. In this study, we found that UBE2C was elevated in this human pan-cancer analysis, and high expression of UBE2C was correlated with poor prognosis. In addition, UBE2C expression was markedly associated with tumor mutation burden (TMB), microsatellite instability (MSI), immune cell infiltration, and diverse drug sensitivities. Finally, we showed that the METTL3/SNHG1/miRNA-140-3p axis could potentially regulate UBE2C expression. N(6)-Methyladenosine (m6A) modifications improved the stability of methylated SNHG1 transcripts by decreasing the rate of RNA degradation, which lead to upregulation of SNHG1 in non-small cell lung cancer (NSCLC). In vitro functional experiments showed that SNHG1, as a competing endogenous RNA, sponges miR-140-3p to increase UBE2C expression in NSCLC cell lines. Our study elucidates the clinical importance and regulatory mechanism of the METTL3/SNHG1/miRNA-140-3p/UBE2C axis in NSCLC and provides a prognostic indicator, as well as a promising therapeutic target for patients with NSCLC.

Analysis of Ferroptosis-Related Gene Expression and Prognostic Factors of Renal Clear Cell Carcinoma Based on TCGA Database

Introduction

Renal clear cell carcinoma (ccRCC) is a common tumor of the urinary system, most of which are primary malignant tumors with high metastatic rate and remaining incurable. Ferroptosis is a newly discovered form of iron-dependent programmed cell necrosis in recent years, which is inextricably linked to the occurrence and development of tumors progression. Due to the complexity of the interaction between genes in ccRCC, the research on the pathogenesis of ccRCC is still not remarkably accurate. Therefore, whether ferroptosis-related genes (FRGs) can play a role in predicting prognosis in ccRCC needs to be discussed.

Methods

We entered the Cancer Genome Mapping Project (TCGA) database and downloaded the relevant genes and clinical research data of ccRCC patients. Lasso Cox regression was used to construct a multi-gene prognostic model in the TCGA cohort. R language software was used for drawing pictures related to our study.

Results

Most of the genes involved in ferroptosis (86.2%) existing differences between the tumor and normal tissues in the TCGA public gene database. In terms of univariate Cox regression analysis, 20 differentially expressed genes (DEGs) were associated with prognosis and survival (P<0.05). A prognostic model of 12 FRGs was constructed, and patients were segmented into two different groups depending on how risky they are. Considering overall survival, the high-risk group is dramatically lower than the low-risk group (P<0.001). In multivariate Cox regression analysis, risk scores and stage turned out be an independent prognostic factor (P<0.001). GO and KEGG analysis and ssGSEA analysis of DEGs revealed that these genes were related to immune-related pathways (P<0.05).

Conclusion

This study established and identified the changes in FRGs expression and prognostic factors of ccRCC, which can be helpful for prognosis evaluation and clinical treatment of this disease.

Construction of circRNA-based ceRNA network and its prognosis-associated subnet of clear cell renal cell carcinoma

Circular RNAs (circRNAs) are novel biomarkers of various cancers. CircRNAs can sponge miRNAs and regulate target mRNAs, which was called competing endogenous RNAs (ceRNA). This study was designed to identify circRNAs related to patients with clear cell renal cell carcinoma (ccRCC) and the first to select three independent Gene Expression Omnibus microarrays covering circRNAs, miRNAs, and mRNAs for multiple analyses. The data of clinical cases applied in our study were obtained from The Cancer Genome Atlas. We successfully conducted a circRNA/miRNA/mRNA ceRNA network related to ccRCC patients via R software and Cytoscape including 8 circRNAs, 6 miRNAs, and 49 mRNAs. The prognosis‐associated subnet covered 8 circRNAs, 6 miRNAs, and 22 mRNAs. Quantitative real‐time PCR was applied to measure our prediction in three renal cell lines and 23 pairs of tissues. Small interfering RNA targeting the back‐splice region of hsa_circ_0001167 was further implied to confirm the regulation. Ultimately, hsa_circ_0001167/hsa‐miR‐595/CCDC8 regulatory axis was identified in this study, which may serve as prognostic indicators. Lower levels of hsa_circ_0001167 and CCDC8 were potentially correlated with worse patient survival.

NS398 as a potential drug for autosomal-dominant polycystic kidney disease: Analysis using bioinformatics, and zebrafish and mouse models

Autosomal‐dominant polycystic kidney disease (ADPKD) is characterized by uncontrolled renal cyst formation, and few treatment options are available. There are many parallels between ADPKD and clear‐cell renal cell carcinoma (ccRCC); however, few studies have addressed the mechanisms linking them. In this study, we aimed to investigate their convergences and divergences based on bioinformatics and explore the potential of compounds commonly used in cancer research to be repurposed for ADPKD. We analysed gene expression datasets of ADPKD and ccRCC to identify the common and disease‐specific differentially expressed genes (DEGs). We then mapped them to the Connectivity Map database to identify small molecular compounds with therapeutic potential. A total of 117 significant DEGs were identified, and enrichment analyses results revealed that they are mainly enriched in arachidonic acid metabolism, p53 signalling pathway and metabolic pathways. In addition, 127 ccRCC‐specific up‐regulated genes were identified as related to the survival of patients with cancer. We focused on the compound NS398 as it targeted DEGs and found that it inhibited the proliferation of Pkd1^−/− and 786‐0 cells. Furthermore, its administration curbed cystogenesis in Pkd2 zebrafish and early‐onset Pkd1‐deficient mouse models. In conclusion, NS398 is a potential therapeutic agent for ADPKD.

High-Throughput In Vitro Gene Expression Profile to Screen of Natural Herbals for Breast Cancer Treatment

Breast cancer has surpassed lung cancer as the most commonly diagnosed cancer in women worldwide. Some therapeutic drugs and approaches could cause side effects and weaken the immune system. The combination of conventional therapies and traditional Chinese medicine (TCM) significantly improves treatment efficacy in breast cancer. However, the chemical composition and underlying anti-tumor mechanisms of TCM still need to be investigated. The primary aim of this study is to provide unique insights to screen the natural components for breast cancer therapy using high-throughput transcriptome analysis. Differentially expressed genes were identified based on two conditions: single samples and groups were classified according to their pharmaceutical effect. Subsequently, the sample treated with E. cochinchinensis Lour. generated the most significant DEGs set, including 1,459 DEGs, 805 upregulated and 654 downregulated. Similarly, group 3 treatment contained the most DEGs (414 DEGs, 311 upregulated and 103 downregulated). KEGG pathway analyses showed five significant pathways associated with the inflammatory and metastasis processes in cancer, which include the TNF, IL−17, NF-kappa B, MAPK signaling pathways, and transcriptional misregulation in cancer. Samples were classified into 13 groups based on their pharmaceutical effects. The results of the KEGG pathway analyses remained consistent with signal samples; group 3 presents a high significance. A total of 21 genes were significantly regulated in these five pathways, interestingly, IL6, TNFAIP3, and BRIC3 were enriched on at least two pathways, seven genes (FOSL1, S100A9, CXCL12, ID2, PRS6KA3, AREG, and DUSP6) have been reported as the target biomarkers and even the diagnostic tools in cancer therapy. In addition, weighted correlation network analysis (WGCNA) was used to identify 18 modules. Among them, blue and thistle2 were the most relevant modules. A total of 26 hub genes in blue and thistle2 modules were identified as the hub genes. In conclusion, we screened out three new TCM (R. communis L., E. cochinchinensis Lour., and B. fruticosa) that have the potential to develop natural drugs for breast cancer therapy, and obtained the therapeutic targets.

High-Throughput Screen of Natural Compounds and Biomarkers for NSCLC Treatment by Differential Expression and Weighted Gene Coexpression Network Analysis (WGCNA)

Lung cancer is known as the leading cause which presents the highest fatality rate worldwide; non-small-cell lung cancer (NSCLC) is the most prevalent type of lung carcinoma with high severity and affects 80% of patients with lung malignancies. Up to now, the general treatment for NSCLC includes surgery, chemotherapy, and radiotherapy; however, some therapeutic drugs and approaches could cause side effects and weaken the immune system. The combination of conventional therapies and traditional Chinese medicine (TCM) significantly improves treatment efficacy in lung cancer. Therefore, it is necessary to investigate the chemical composition and underlying antitumor mechanisms of TCM, so as to get a better understanding of the potential natural ingredient for lung cancer treatment. In this study, we selected 78 TCM to treat NSCLC cell line (A549) and obtained 92 transcriptome data; differential expression and WGCNA were applied to screen the potential natural ingredient and target genes. The sample which was treated with A. pierreana generated the most significant DEG set, including 6130 DEGs, 2479 upregulated, and 3651 downregulated. KEGG pathway analyses found that four pathways (MAPK, NF-kappa B, p53, and TGF-beta signaling pathway) were significantly enriched; 16 genes were significantly regulated in these four pathways. Interestingly, some of them such as EGFR, DUSP4, IL1R1, IL1B, MDM2, CDKNIA, and IDs have been used as the target biomarkers for cancer diagnosis and therapy. In addition, classified samples into 14 groups based on their pharmaceutical effects, WGCNA was used to identify 27 modules. Among them, green and darkgrey were the most relevant modules. Eight genes in the green module and four in darkgrey were identified as hub genes. In conclusion, we screened out three new TCM (B. fruticose, A. pierreana, and S. scandens) that have the potential to develop natural anticancer drugs and obtained the therapeutic targets for NSCLC therapy. Our study provides unique insights to screen the natural components for NSCLC therapy using high-throughput transcriptome analysis.

Identification of CDC20 as a Novel Biomarker in Diagnosis and Treatment of Wilms Tumor

Objective: Wilms tumor (WT) is a common malignant solid tumor in children. Many tumor biomarkers have been reported; however, there are poorly targetable molecular mechanisms which have been defined in WT. This study aimed to identify the oncogene in WT and explore the potential mechanisms. Methods: Differentially expressed genes (DEGs) in three independent RNA-seq datasets were downloaded from The Cancer Genome Atlas data portal and the Gene Expression Omnibus database (GSE66405 and GSE73209). The common DEGs were then subjected to Gene Ontology enrichment analysis, protein-protein interaction (PPI) network analysis, and gene set enrichment analysis. The protein expression levels of the hub gene were analyzed by immunohistochemical analysis and Western blotting in a 60 WT sample. The univariate Kaplan-Meier analysis for overall survival was performed, and the log-rank test was utilized. A small interfering RNA targeting cell division cycle 20 (CDC20) was transfected into G401 and SK-NEP-1 cell lines. The Cell Counting Kit-8 assay and wound healing assay were used to observe the changes in cell proliferation and migration after transfection. Flow cytometry was used to detect the effect on the cell cycle. Western blot was conducted to study the changes of related functional proteins. Results: We commonly identified 44 upregulation and 272 downregulation differentially expressed genes in three independent RNA-seq datasets. Gene and pathway enrichment analyses of the regulatory networks involving hub genes suggested that cell cycle changes are crucial in WT. The top 15 highly connected genes were found by PPI network analysis. Furthermore, we demonstrated that one candidate biomarker, CDC20, for the diagnosis of WT was detected, and its high expression predicted poor prognosis of WT patients. Moreover, the area under the curve value obtained by receiver operating characteristic curve analysis from paired WT samples was 0.9181. Finally, we found that the suppression of CDC20 inhibited proliferation and migration and resulted in G2/M phase arrest in WT cells. The mechanism may be involved in increasing the protein level of securin, cyclin B1, and cyclin A Conclusion: Our results suggest that CDC20 could serve as a candidate diagnostic and prognostic biomarker for WT, and suppression of CDC20 may be a potential approach for the prevention and treatment of WT.

A ferroptosis-related gene signature for graft loss prediction following renal allograft

Allogeneic kidney transplantation (renal allograft) is the most effective treatment for advanced kidney disease. Previous studies have indicated that ferroptosis participates in the progression of acute kidney injury and renal transplant failure. However, few studies have evaluated the prognostic value of ferroptosis on renal transplantation outcomes. In this study, a total of 22 differentially expressed ferroptosis-related genes (DFGs) were identified, which were mainly enriched in infection-related pathways. Next, a ferroptosis-related gene signature, including GA-binding protein transcription factor subunit beta 1 (GABPB1), cyclin-dependent kinase inhibitor 1A (CDKN1A), Toll-like receptor 4 (TLR4), C-X-C motif chemokine ligand 2 (CXCL2), caveolin 1 (CAV1), and ribonucleotide reductase subunit M2 (RRM2), was constructed to predict graft loss following renal allograft. Moreover, receiver operating characteristic (ROC) curves (area under the ROC curve [AUC] > 0.8) demonstrated the accuracy of the gene signature and univariate Cox analysis suggested that the gene signature could play an independent role in graft loss (p < 0.05). Furthermore, the nomogram and calibration plots also indicated the good prognostic capability of the gene signature. Finally, immune-related and cytokine signaling pathways were mostly enriched in renal allograft patients with poor outcomes. Considered together, a ferroptosis-related gene signature and nomogram based on DFGs were created to predict the 1-, 2- and 3- year graft loss probability of renal allograft patients.The gene signature could serve as a valuable biomarker for predicting graft loss, contributing to improving the outcome of allogeneic kidney transplantation.

Nucleolar protein NOP2 could serve as a potential prognostic predictor for clear cell renal cell carcinoma

As an indispensable part for cancer precision medicine, biomarkers and signatures for predicting cancer prognosis and therapeutic benefits were urgently required. The purpose of this study was to investigate the prognostic roles of NOP2 in renal clear cell carcinoma (ccRCC) for overall survival (OS) and its relationships with immunity. NOP2-related gene expression matrix associated with clinical information was obtained from the Cancer Genome Atlas (TCGA) ccRCC dataset and NOP2-related pathways were identified by gene set enrichment analysis (GSEA). Associations among the NOP2 expression and MSI, TMB, TNB, and immunity were also explored. Both the NOP2 mRNA and protein/phosphoprotein had a higher expression in ccRCC tumor tissues than in normal kidney tissues (both P < 0.001) and elevated NOP2 expression was associated with poor OS (P < 0.001). Logistic regression analysis revealed the NOP2 expression was significantly linked to stage, age, grade, N stage, T stage, and M stage (all P < 0.05). Univariate/multivariate Cox hazard regression analysis results indicated that NOP2 was an independent prognostic factor for OS in ccRCC and GSEA revealed five NOP2-related signaling pathways. Nomogram based on NOP2 and eight clinical characteristic parameters (grade, age, stage, gender, T stage, race, M stage, N stage) was constructed and carefully evaluated. Furthermore, NOP2 gene expression was also found to be significantly related to MSI, TMB, and immunity. Our findings revealed that NOP2 might be a potential prognostic factor for OS in ccRCC and it was significantly associated with immunity, MSI, and TMB.

Development of a novel gene signature to predict prognosis and response to PD-1 blockade in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common kidney malignancy characterized by a poor prognosis. The treatment efficacy of immune checkpoint inhibitors (ICIs) also varies widely in advanced ccRCC. We aim to construct a robust gene signature to improve the prognostic discrimination and prediction of ICIs for ccRCC patients. In this study, adopting differentially expressed genes from seven ccRCC datasets in GEO (Gene Expression Omnibus), a novel signature (FOXM1&TOP2A) was constructed in TCGA (The Cancer Genome Atlas) database by LASSO and Cox regression. Survival and time-dependent ROC analysis revealed the strong predictive ability of our signature in discovery set, two online validation sets and one tissue microarray (TMA) from our institution. High-risk group based on the signature comprises more high-grade (G3&G4) and advanced pathologic stage (stageIII/IV) tumors and presents hyperactivation of cell cycle process according to the functional analysis. Meanwhile, high-risk tumors demonstrate an immunosuppressive phenotype with more infiltrations of regulatory T cells (Tregs), macrophages and high expressions of genes negatively regulating anti-tumor immunity. Low-risk tumors have an improved response to anti-PD-1 therapy and the predictive ability of our signature is better than other recognized biomarkers in ccRCC. A nomogram containing this signature showed a high predictive accuracy with AUCs of 0.90 and 0.84 at 3 and 5 years. Overall, this robust signature could predict prognosis, evaluate immune microenvironment and response to anti-PD-1 therapy in ccRCC, which is very promising in clinical promotion.

Characterization of Hypoxia-Related Molecular Subtypes in Clear Cell Renal Cell Carcinoma to Aid Immunotherapy and Targeted Therapy via Multi-Omics Analysis

Objective: Tumor hypoxia is a key factor in resistance to anti-cancer treatment. Herein, this study aimed to characterize hypoxia-related molecular subtypes and assess their correlations with immunotherapy and targeted therapy in clear cell renal cell carcinoma (ccRCC). Materials: We comprehensively analyzed copy number variation (CNV), somatic mutation, transcriptome expression profile and clinical information for ccRCC from TCGA and ICGC databases. Based on 98 prognosis-related hypoxia genes, samples were clustered using unsupervized non-negative matrix factorization (NMF) analysis. We characterized the differences between subtypes concerning prognosis, CNV, somatic mutations, pathways, immune cell infiltrations, stromal/immune scores, tumor purity, immune checkpoint inhibitors (ICI), response to immunotherapy and targeted therapy and CXC chemokines. Based on differentially expressed genes (DEGs) between subtypes, a prognostic signature was built by LASSO Cox regression analysis, followed by construction of a nomogram incorporating the signature and clinical features. Results: Two hypoxia-related molecular subtypes (C1 and C2) were constructed for ccRCC. Differential CNV, somatic mutations and pathways were found between subtypes. C2 exhibited poorer prognosis, higher immune/stromal scores, and lower tumor purity than C1. Furthermore, C2 had more sensitivity to immunotherapy and targeted therapy than C1. The levels of CXCL1/2/3/5/6/8 chemokines in C2 were distinctly higher than in C1. Consistently, DEGs between subtypes were significantly enriched in cytokine-cytokine receptor interaction and immune responses. This subtype-specific signature can independently predict patients' prognosis. Following verification, the nomogram could be utilized for personalized prediction of the survival probability. Conclusion: Our findings characterized two hypoxia-related molecular subtypes for ccRCC, which can assist in identifying high-risk patients with poor clinical outcomes and patients who can benefit from immunotherapy or targeted therapy.

A Novel Immune-Related Prognostic Signature in Head and Neck Squamous Cell Carcinoma

The immune response within the tumor microenvironment plays a key role in tumorigenesis and determines the clinical outcomes of head and neck squamous cell carcinoma (HNSCC). However, to date, very limited robust and reliable immunological biomarkers have been developed that are capable of estimating prognosis in HNSCC patients. In this study, we aimed to identify the effects of novel immune-related gene signatures (IRGs) that can predict HNSCC prognosis. Based on gene expression profiles and clinical data of HNSCC patient cohorts from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, a total of 439 highly variable expressed immune-related genes (including 239 upregulated and 200 downregulated genes) were identified by using differential gene expression analysis. Pathway enrichment analysis indicated that these immune-related differentially expressed genes were enriched in inflammatory functions. After process screening in the training TCGA cohort, six immune-related genes (PLAU, STC2, TNFRSF4, PDGFA, DKK1, and CHGB) were significantly associated with overall survival (OS) based on the LASSO Cox regression model. Integrating these genes with clinicopathological features, a multivariable model was built and suggested better performance in determining patients’ OS in the testing cohort, and the independent validation cohort. In conclusion, a well-established model encompassing both immune-related gene signatures and clinicopathological factors would serve as a promising tool for the prognostic prediction of HNSCC.

Ferroptosis-Related Gene-Based Prognostic Model and Immune Infiltration in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Ferroptosis plays a vital role in ccRCC development and progression. We did an update of ferroptosis-related multigene expression signature for individualized prognosis prediction in patients with ccRCC. Differentially expressed ferroptosis-related genes in ccRCC and normal samples were screened using The Cancer Genome Atlas. Univariate and multivariate Cox regression analyses and machine learning methods were employed to identify optimal prognosis-related genes. CARS1, CD44, FANCD2, HMGCR, NCOA4, SLC7A11, and ACACA were selected to establish a prognostic risk score model. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that these genes were mainly enriched in immune-related pathways; single-sample Gene Set Enrichment Analysis revealed several immune cells potentially related to ferroptosis. Kaplan-Meier survival analysis demonstrated that patients with high-risk scores had significantly poor overall survival (log-rank P = 7.815 × 10^-11). The ferroptosis signature was identified as an independent prognostic factor. Finally, a prognostic nomogram, including the ferroptosis signature, age, histological grade, and stage status, was constructed. Analysis of The Cancer Genome Atlas-based calibration plots, C-index, and decision curve indicated the excellent predictive performance of the nomogram. The ferroptosis-related seven-gene risk score model is useful as a prognostic biomarker and suggests therapeutic targets for ccRCC. The prognostic nomogram may assist in individualized survival prediction and improve treatment strategies.

The Choice of Candidates in Survival Markers Based on Coordinated Gene Expression in Renal Cancer

We aimed to identify and investigate genes that are essential for the development of clear cell renal cell carcinoma (ccRCC) and sought to shed light on the mechanisms of its progression and create prognostic markers for the disease. We used real-time PCR to study the expression of 20 genes that were preliminarily selected based on their differential expression in ccRCC, in 68 paired tumor/normal samples. Upon ccRCC progression, seven genes that showed an initial increase in expression showed decreased expression. The genes whose expression levels did not significantly change during progression were associated mainly with metabolic and inflammatory processes. The first group included CA9, NDUFA4L2, EGLN3, BHLHE41, VWF, IGFBP3, and ANGPTL4, whose expression levels were coordinately decreased during tumor progression. This expression coordination and gene function is related to the needs of tumor development at different stages. Specifically, the high correlation coefficient of EGLN3 and NDUFA4L2 expression may indicate the importance of the coordinated regulation of glycolysis and mitochondrial metabolism. A panel of CA9, EGLN3, BHLHE41, and VWF enabled the prediction of survival for more than 3.5 years in patients with ccRCC, with a probability close to 90%. Therefore, a coordinated change in the expression of a gene group during ccRCC progression was detected, and a new panel of markers for individual survival prognosis was identified.

Identification of prognostic significance of BIRC5 in breast cancer using integrative bioinformatics analysis

Aims: Baculoviral inhibitor of apoptosis repeat containing 5 (BIRC5) plays vital roles in carcinogenesis by influencing cell division and proliferation and by inhibiting apoptosis. However, the prognostic significance of BIRC5 remains unclear in breast cancer.

Methods:BIRC5 expression and methylation status were evaluated using the Oncomine and The Cancer Genome Atlas (TCGA) databases. The relevance between BIRC5 and different clinicopathological features as well as survival information was analyzed using the bc-GenExMiner database and Kaplan–Meier Plotter. BIRC5–drug interaction network was obtained using the Comparative Toxicogenomics Database.

Results: Based on the results from databases and own hospital data, BIRC5 was higher expressed in different breast cancer subtypes compared with the matched normal individuals. Hormone receptors were negatively correlated with BIRC5 expression, whereas the Scarff–Bloom–Richardson (SBR) grade, Nottingham Prognostic Index (NPI), human epidermal growth factor receptor-2 (HER-2) status, basal-like status, and triple-negative status were positively related to BIRC5 level in breast cancer samples with respect to normal tissues. High BIRC5 expression was responsible for shorter relapse-free survival, worse overall survival, reduced distant metastasis free survival, and increased risk of metastatic relapse event. BIRC5–drug interaction network indicated that several common drugs could modulate BIRC5 expression. Furthermore, a positive correlation between BIRC5 andcell-division cycle protein 20 (CDC20) gene was confirmed.

Conclusion:BIRC5 may be adopted as a promising predictive marker and potential therapeutic target in breast cancer. Further large-scale studies are needed to more precisely confirm the value of BIRC5 in treatment of breast cancer.

Identification of potential oncogenes in triple-negative breast cancer based on bioinformatics analyses

Triple-negative breast cancer (TNBC) is a subtype with high rates of metastasis, poor prognosis and limited therapeutic options. The present study aimed to identify the potential pivotal genes for prognosis and treatment in TNBC. A total of two microarray expression datasets, GSE38959 and GSE65212, were downloaded from the Gene Expression Omnibus database, and RNA-sequencing data of breast cancer from The Cancer Genome Atlas database were analyzed to screen out differentially expressed genes (DEGs) between TNBC tissues and normal tissues. The intersection of DEGs was submitted to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. A protein-protein interaction (PPI) network was constructed and visualized using Cytoscape software. Furthermore, module, centrality and survival analyses were performed to identify the potential hub genes. Reverse transcription-quantitative (RT-q)PCR analysis was performed to detect the expression levels of key genes in TNBC samples, and 377 DEGs were identified. Functional analysis revealed that the DEGs were significantly involved in cell cycle process, nuclear division and the p53 signaling pathway. A PPI network was constructed with these DEGs, and 66 core genes with high centrality features in module 1 were selected. Relapse-free survival analysis confirmed that high expression levels of five genes [cyclin B1 (CCNB1), GINS complex subunit 2, non-SMC condensin I complex subunit G (NCAPG), minichromosome maintenance 4 (MCM4) and ribonucleotide reductase regulatory subunit M2 (RRM2)] were significantly associated with poor prognosis in TNBC. RT-qPCR analysis demonstrated that CCNB1, NCAPG, MCM4 and RRM2 were significantly upregulated in 25 TNBC tissues compared with adjacent normal breast tissues. Furthermore, gene set enrichment analysis revealed that CCNB1, NCAPG, MCM4 and RRM2 were closely associated with tumor proliferation. Taken together, these results suggest that CCNB1, NCAPG, MCM4 and RRM2 are associated with tumorigenesis and TNBC progression, and thus may act as promising prognostic biomarkers and therapeutic targets for TNBC.

Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system

We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this study, artificial intelligence (AI) algorithms including random forest and neural network were trained to build a molecular prognostic score (mPS) system. Afterwards, we investigated the potential mechanisms underlying mPS by assessing gene set enrichment analysis, mutations, copy number variations (CNVs) and immune cell infiltration. A total of 275 prognosis-related genes were identified, which were also differentially expressed between ccRCC patients and healthy controls. We then constructed a universal mPS system that depends on the expression status of only 21 of these genes by applying AI-based algorithms. Then, the mPS were validated by another independent cohort and demonstrated to be applicable to ccRCC subsets. Furthermore, a nomogram comprising the mPS score and several independent variables was established and proved to effectively predict ccRCC patient prognosis. Finally, significant differences were identified regarding the pathways, mutated genes, CNVs and tumor-infiltrating immune cells among the subgroups of ccRCC stratified by the mPS system. The AI-based mPS system can provide critical prognostic prediction for ccRCC patients and may be useful to inform treatment and surveillance decisions before initial intervention.

[Identification of Hub Genes for Ovarian Cancer Stem Cell Properties with Weighted Gene Co-expression Network Analysis]

OBJECTIVE

To investigate the significance of stemness-related genes in the diagnosis and treatment of ovarian cancer.

METHODS

Key modules and genes were identified with weighted gene co-expression network analysis (WGCNA). The signal pathways of high expression of key genes were analyzed by gene set enrichment analysis (GSEA) and single cell sequencing data. The chemosensitivity of ovarian cancer to chemotherapy drugs was estimated with pRRophetic. Flow cytometry was used to examine the expression of CD44 +CD117 +in SKOV3 cells and cancer stem cells. The expression of key genes in ovarian cancer stem cells was confirmed by qRT-PCR. The core genes were identified by GeneMANIA analysis.

RESULTS

According to the WGCNA results, 15 key genes were identified at the transcription level, all being highly expressed in many kinds of tumors. They were involved in the cell cycle, DNA repair, E2 target and G2M checkpoint pathway, and had significant correlation with chemosensitivity. The proportion of CD44 + CD117 + cells in SKOV3 cells and ovarian cancer stem cells were (1.20±0.34)% and (37.17±1.80)% respectively, with statistically significant difference ( P<0.05). qRT-PCR confirmed that seven key genes ( BUB1, CDC20, CCNB2, DLGAP5, KIF4 A, NEK2, NUSAP1) in the WGCNA results were highly expressed in ovarian cancer stem cells, and BUB1 might have played a core role.

CONCLUSION

Seven hub genes, especially BUB1, were identified by constructing gene co-expression network, which may become potential biomarkers of ovarian cancer gene.

Identification of novel candidate biomarkers for pancreatic adenocarcinoma based on TCGA cohort

Pancreatic adenocarcinoma (PAAD) is the most serious solid tumor type throughout the world. The present study aimed to identify novel biomarkers and potential efficacious small drugs in PAAD using integrated bioinformatics analyses. A total of 4777 differentially expressed genes (DEGs) were filtered, 2536 upregulated DEGs and 2241 downregulated DEGs. Weighted gene co-expression network analysis was then used and identified 12 modules, of which, blue module with the most significant enrichment result was selected. KEGG and GO enrichment analyses showed that all DEGs of blue module were enriched in EMT and PI3K/Akt pathway. Three hub genes (ITGB1, ITGB5, and OSMR) were determined as key genes with higher expression levels, significant prognostic value and excellent diagnostic efficiency for PAAD. Additionally, some small molecule drugs that possess the potential to treat PAAD were screened out, including thapsigargin (TG). Functional in vitro experiments revealed that TG repressed cell viability via inactivating the PI3K/Akt pathway in PAAD cells. Totally, our findings identified three key genes implicated in PAAD and screened out several potential small drugs to treat PAAD.

Assessing immune infiltration and the tumor microenvironment for the diagnosis and prognosis of sarcoma

BACKGROUND

Sarcomas, cancers originating from mesenchymal cells, are comprehensive tumors with poor prognoses, yet their tumorigenic mechanisms are largely unknown. In this study, we characterize infiltrating immune cells and analyze immune scores to identify the molecular mechanism of immunologic response to sarcomas.

METHOD

The "CIBERSORT" algorithm was used to calculate the amount of L22 immune cell infiltration in sarcomas. Then, the "ESTIMATE" algorithm was used to assess the "Estimate," "Immune," and "Stromal" scores. Weighted gene co-expression network analysis (WGCNA) was utilized to identify the significant module related to the immune therapeutic target. Gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed using the "clusterProfiler" package in R for annotation and visualization.

RESULTS

Macrophages were the most common immune cells infiltrating sarcomas. The number of CD8 T cells was negatively associated with that of M0 and M2 macrophages, and positively associated with M macrophages in sarcomas samples. The clinical parameters (disease type, gender) significantly increased with higher Estimate, Immune, and Stromal scores, and with a better prognosis. The blue module was significantly associated with CD8 T cells. Functional enrichment analysis showed that the blue module was mainly involved in chemokine signaling and the PI3K-Akt signaling pathway. CD48, P2RY10 and RASAL3 were identified and validated at the protein level.

CONCLUSION

Based on the immune cell infiltration and immune microenvironment, three key genes were identified, thus presenting novel molecular mechanisms of sarcoma metastasis.

A Three-Metabolic-Genes Risk Score Model Predicts Overall Survival in Clear Cell Renal Cell Carcinoma Patients

Metabolic alterations play crucial roles in carcinogenesis, tumor progression, and prognosis in clear cell renal cell carcinoma (ccRCC). A risk score (RS) model for ccRCC consisting of disease-associated metabolic genes remains unidentified. Here, we utilized gene set enrichment analysis to analyze expression data from normal and tumor groups from the cancer genome atlas. Out of 70 KEGG metabolic pathways, we found seven and two pathways to be significantly enriched in our normal and tumor groups, respectively. We identified 113 genes enriched in these nine pathways. We further filtered 47 prognostic-related metabolic genes and used Least absolute shrinkage and selection operator (LASSO) analysis to construct a three-metabolic-genes RS model composed of ALDH3A2, B3GAT3, and CPT2. We further tested the RS by mapping Kaplan-Meier plots and receiver operating characteristic curves, the results were promising. Additionally, multivariate Cox analysis revealed the RS to be an independent prognostic factor. Thereafter, we considered all the independent factors and constructed a nomogram model, which manifested in better prediction capability. We validated our results using a dataset from ArrayExpress and through qRT-PCR. In summary, our study provided a metabolic gene-based RS model that can be used as a prognostic predictor for patients with ccRCC.

Profiles of overall survival-related gene expression-based risk signature and their prognostic implications in clear cell renal cell carcinoma

The present work aimed to evaluate the prognostic value of overall survival (OS)-related genes in clear cell renal cell carcinoma (ccRCC) and to develop a nomogram for clinical use. Transcriptome data from The Cancer Genome Atlas (TCGA) were collected to screen differentially expressed genes (DEGs) between ccRCC patients with OS > 5 years (149 patients) and those with <1 year (52 patients). In TCGA training set (265 patients), seven DEGs (cytochrome P450 family 3 subfamily A member 7 (CYP3A7), contactin-associated protein family member 5 (CNTNAP5), adenylate cyclase 2 (ADCY2), TOX high mobility group box family member 3 (TOX3), plasminogen (PLG), enamelin (ENAM), and collagen type VII α 1 chain (COL7A1)) were further selected to build a prognostic risk signature by the least absolute shrinkage and selection operator (LASSO) Cox regression model. Survival analysis confirmed that the OS in the high-risk group was dramatically shorter than their low-risk counterparts. Next, univariate and multivariate Cox regression revealed the seven genes-based risk score, age, and Tumor, lymph Node, and Metastasis staging system (TNM) stage were independent prognostic factors to OS, based on which a novel nomogram was constructed and validated in both TCGA validation set (265 patients) and the International Cancer Genome Consortium cohort (ICGC, 84 patients). A decent predictive performance of the nomogram was observed, the C-indices and corresponding 95% confidence intervals of TCGA training set, validation set, and ICGC cohort were 0.78 (0.74–0.82), 0.75 (0.70–0.80), and 0.70 (0.60–0.80), respectively. Moreover, the calibration plots of 3- and 5 years survival probability indicated favorable curve-fitting performance in the above three groups. In conclusion, the proposed seven genes signature-based nomogram is a promising and robust tool for predicting the OS of ccRCC, which may help tailor individualized therapeutic strategies.

Development and Validation of a Tumor Mutation Burden-Related Immune Prognostic Model for Lower-Grade Glioma

Tumor mutation burden (TMB) is a useful biomarker to predict prognosis and the efficacy of immune checkpoint inhibitors (ICIs). In this study, we aimed to explore the prognostic value of TMB and the potential association between TMB and immune infiltration in lower-grade gliomas (LGGs). Somatic mutation and RNA-sequencing (RNA-seq) data were downloaded from the Cancer Genome Atlas (TCGA) database. TMB was calculated and patients were divided into high- and low-TMB groups. After performing differential analysis between high- and low-risk groups, we identified six hub TMB and immune-related genes that were correlated with overall survival in LGGs. Then, Gene Set Enrichment Analysis was performed to screen significantly enriched GO terms between the two groups. Moreover, an immune-related risk score system was developed by LASSO Cox analysis based on the six hub genes and was validated with the Chinese Glioma Genome Atlas dataset. Using the TIMER database, we further systematically analyzed the relationships between mutants of the six hub genes and immune infiltration levels, as well as the relationships between the immune-related risk score system and the immune microenvironment in LGGs. The results showed that TMB was negatively correlated with OS and high TMB might inhibit immune infiltration in LGGs. Furthermore, the risk score system could effectively stratify patients into low- and high-risk groups in both the training and validation datasets. Multivariate Cox analysis demonstrated that TMB was not an independent prognostic factor, but the risk score was. Higher infiltration of immune cells (B cells, CD4⁺ T cells, CD8⁺ T cells, neutrophils, macrophages, and dendritic cells) and higher levels of immune checkpoints (PD-1, CTLA-4, LAG-3, and TIM-3) were found in patients in the high-risk group. Finally, a novel nomogram model was constructed and evaluated to estimate the overall survival of LGG patients. In summary, our study provided new insights into immune infiltration in the tumor microenvironment and immunotherapies for LGGs.

Weighted correlation gene network analysis reveals a new stemness index-related survival model for prognostic prediction in hepatocellular carcinoma

In this study, we constructed a new survival model using mRNA expression-based stemness index (mRNAsi) for prognostic prediction in hepatocellular carcinoma (HCC). Weighted correlation network analysis (WGCNA) of HCC transcriptome data (374 HCC and 50 normal liver tissue samples) from the TCGA database revealed 7498 differentially expressed genes (DEGs) that clustered into seven gene modules. LASSO regression analysis of the top two gene modules identified ANGPT2, EMCN, GLDN, USHBP1 and ZNF532 as the top five mRNAsi-related genes. We constructed our survival model with these five genes and tested its performance using 243 HCC and 202 normal liver samples from the ICGC database. Kaplan-Meier survival curve and receive operating characteristic curve analyses showed that the survival model accurately predicted the prognosis and survival of high- and low-risk HCC patients with high sensitivity and specificity. The expression of these five genes was significantly higher in the HCC tissues from the TCGA, ICGC, and GEO datasets (GSE25097 and GSE14520) than in normal liver tissues. These findings demonstrate that a new survival model derived from five strongly correlating mRNAsi-related genes provides highly accurate prognoses for HCC patients.

Bioinformatical Analysis of Gene Expression Omnibus Database Associates TAF7/CCNB1, TAF7/CCNA2, and GTF2E2/CDC20 Pathways with Glioblastoma Development and Prognosis

OBJECTIVE

This study bioinformatically analyzed aberrant genes and pathways for associations with glioblastoma development and prognosis.

METHODS

The Gene Expression Omnibus (GEO) database was searched and 4 GEO datasets (GSE4290, GSE50161, GSE116520, and GSE90598) were retrieved for limma and RobustRankAggreg package analyses of differentially expressed genes (DEGs) between glioblastoma and normal brain tissues. Functional enrichment analysis was conducted for the main biological functions of these DEGs, whereas the hub genes were identified using the protein-protein interaction network and confirmed for transcriptional and translational levels using the Cancer Genome Atlas, the Genotype-Tissue Expression, and the Human Protein Atlas data. The prognostic values of these hub genes were analyzed using the Chinese Glioma Genome Atlas. Their transcriptional factor regulation network was constructed to assess the roles in glioblastoma development and progression.

RESULTS

A total of 473 DEGs (182 upregulated and 291 downregulated) were identified and the hub genes (including CCNB1, CDC20, CCNB2, BUB1, and CCNA2) were shown in module 1 and enriched in the cell cycle or p53 signaling pathway. The highly expressed CCNB1, CDC20, BUB1, and CCNA2 in patients with glioblastoma were associated with poor overall survival, whereas TAF7 could upregulate expression of CCNB1 and CCNA2 and GTF2E2 could upregulate CDC20 expression in glioblastoma.

CONCLUSIONS

This study showed several DEGs in glioblastoma, and aberrant expression of their hub genes was associated with glioblastoma pathogenesis and poor prognosis, especially the signaling axes of TAF7/CCNB1, TAF7/CCNA2, and GTF2E2/CDC20.

Characterization of long non-coding RNA and messenger RNA profiles in laryngeal cancer by weighted gene co-expression network analysis

Laryngeal cancer (LC) is a malignant tumor in the head and neck region. It was recently elucidated that long non-coding RNAs (lncRNAs) participate in the pathogenesis of LC. However, the detailed mechanism of lncRNA in LC and whether long non-coding RNAs serve as effective biomarkers remains unclear. Ribonucleic acid (RNA) sequence data of LC and 11 patient clinical traits were extracted from The Cancer Genome Atlas (TCGA) database and analyzed by weighted gene co-expression network analysis (WGCNA). A total of 9 co-expression modules were identified. The co-expression Pink module significantly correlated with four clinical traits, including history of smoking, lymph node count, tumor status, and the success of follow-up treatment. Based on the co-expression Pink module, lncRNA-microRNA (miRNA)-messenger RNA (mRNA) and lncRNA-RNA binding protein-mRNA networks were constructed. We found that 8 lncRNAs significantly impacted overall survival (OS) in LC patients. These identified lncRNA and hub gene biomarkers were also validated in multiple LC cells in vitro via qPCR. Taken together, this study provided the framework of co-expression gene modules of LC and identified some important biomarkers in LC development and disease progression.