A glycolysis-based 4-mRNA signature correlates with the prognosis and cell cycle process in patients with bladder cancer

EGFLAM exhibits oncogenic activity and shows promise as a prognostic biomarker and therapeutic target in glioblastoma

Glioblastoma (GBM) remains the most lethal primary brain tumor, characterized by dismal survival rates. Novel molecular targets are urgently required to enhance therapeutic outcomes. A combination of bioinformatics analysis and experimental validation was employed to investigate the role of EGFLAM in GBM. The Chinese Glioma Genome Atlas provided a platform for gene expression profiling, while siRNA-mediated knockdown and overexpression assays in GBM cell lines, alongside in vivo tumorigenesis models, facilitated functional validation. EGFLAM was found to be significantly overexpressed in GBM tissues, correlating with adverse prognostic factors and higher tumor grades, particularly in patients over the age of 41. Functional assays indicated that EGFLAM is vital for maintaining GBM cell proliferation, viability, and invasiveness. Knockdown of EGFLAM expression led to a marked decrease in tumorigenic capabilities. Proteomic interactions involving EGFLAM, such as with NUP205, were implicated in cell cycle regulation, providing insight into its oncogenic mechanism. In vivo studies further demonstrated that silencing EGFLAM expression could inhibit tumor growth, underscoring its therapeutic potential. The study identifies EGFLAM as a pivotal oncogenic factor in GBM, serving as both a prognostic biomarker and a viable therapeutic target. These findings lay the groundwork for future research into EGFLAM-targeted therapies, aiming to improve clinical outcomes for GBM patients.

A metabolic reprogramming-related gene signature correlates with prognosis and proliferation of BLCA

Bladder cancer (BLCA) is one of the most frequent urothelium carcinoma, but with poor prognosis due to lack of reliable predictive biomarkers. Metabolic reprogramming involving in various nutrients, and is reported to be closely associated with malignant progression in BLCA. With the use of transcriptome sequencing data profiles of 349 patients from The Cancer Genome Atlas, we established a three-gene glycolysis-related signature to predict the prognosis of BLCA patients. Our signature constructed on the basis of AK3, GALK1 and NUP205 expression, detail features and interactions between these three genes were further explored. We established a nomogram by integrating clinical variables and the risk score. Glycolytic level and proliferation ability were detected to study the role and mechanisms of NUP205 on BLCA. The connections between three genes in our signature were independent. We found our signature gains more value for patients with highly malignant stage. The established nomogram also confirmed that the signature had a eligible clinically predict capacity. After inhibited NUP205 expression, we found the glycolysis level of BLCA cells decreased and proliferation ability suppressed, mainly through AMPK signaling pathway inactivation. Collectively, our study explored a three-gene glycolysis-related signature that predict the prognosis of patients with BLCA, and highlights NUP205 as a potential therapeutic target for inhibiting glycolytic processes and proliferation in BLCA cells.

A promising natural killer cell-based model and a nomogram for the prognostic prediction of clear-cell renal cell carcinoma

BACKGROUND

Clear-cell renal cell carcinoma (ccRCC) is one of prevalent kidney malignancies with an unfavorable prognosis. There is a need for a robust model to predict ccRCC patient survival and guide treatment decisions.

METHODS

RNA-seq data and clinical information of ccRCC were obtained from the TCGA and ICGC databases. Expression profiles of genes related to natural killer (NK) cells were collected from the Immunology Database and Analysis Portal database. Key NK cell-related genes were identified using consensus clustering algorithms to classify patients into distinct clusters. A NK cell-related risk model was then developed using Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression to predict ccRCC patient prognosis. The relationship between the NK cell-related risk score and overall survival, clinical features, tumor immune characteristics, as well as response to commonly used immunotherapies and chemotherapy, was explored. Finally, the NK cell-related risk score was validated using decision tree and nomogram analyses.

RESULTS

ccRCC patients were stratified into 3 molecular clusters based on expression of NK cell-related genes. Significant differences were observed among the clusters in terms of prognosis, clinical characteristics, immune infiltration, and therapeutic response. Furthermore, six NK cell-related genes (DPYSL3, SLPI, SLC44A4, ZNF521, LIMCH1, and AHR) were identified to construct a prognostic model for ccRCC prediction. The high-risk group exhibited poor survival outcomes, lower immune cell infiltration, and decreased sensitivity to conventional chemotherapies and immunotherapies. Importantly, the quantitative real-time polymerase chain reaction (qRT-PCR) confirmed significantly high DPYSL3 expression and low SLC44A4 expression in ACHN cells. Finally, the decision tree and nomogram consistently show the dramatic prediction performance of the risk score on the survival outcome of the ccRCC patients.

CONCLUSIONS

The six-gene model based on NK cell-related gene expression was validated and found to accurately mirror immune microenvironment and predict clinical outcomes, contributing to enhanced risk stratification and therapy response for ccRCC patients.

A novel prognostic N7-methylguanosine-related long non-coding RNA signature in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is regulated by methylation modifications and long noncoding RNAs (lncRNAs). However, knowledge of N7-methylguanosine (m7G)-related lncRNAs that predict ccRCC prognosis remains insufficient. A prognostic multi-lncRNA signature was created using LASSO regression to examine the differential expression of m7G-related lncRNAs in ccRCC. Furthermore, we performed Kaplan-Meier analysis and area under the curve (AUC) analysis for diagnosis. In all, a model based on five lncRNAs was developed. Principal component analysis (PCA) indicated that the risk model precisely separated the patients into different groups. The IC50 value for drug sensitivity divided patients into two risk groups. High-risk group of patients was more susceptible to A.443654, A.770041, ABT.888, AMG.706, and AZ628. Moreover, a lower tumor mutation burden combined with low-risk scores was associated with a better prognosis of ccRCC. Quantitative real-time polymerase chain reaction (qRT-PCR) exhibited that the expression levels of LINC01507, AC093278.2 were very high in all five ccRCC cell lines, AC084876.1 was upregulated in all ccRCC cell lines except 786-O, and the levels of AL118508.1 and DUXAP8 were upregulated in the Caki-1 cell line. This risk model may be promising for the clinical prediction of prognosis and immunotherapeutic responses in patients with ccRCC.

Exploring the relationship between abnormally high expression of NUP205 and the clinicopathological characteristics, immune microenvironment, and prognostic value of lower-grade glioma

Nuclear pore complex (NPC) is a major transport pivot for nucleocytoplasmic molecule exchange. Nucleoporin 205 (NUP205)-a main component of NPC-plays a key regulatory role in tumor cell proliferation; however, few reports document its effect on the pathological progression of lower-grade glioma (LGG). Therefore, we conducted an integrated analysis using 906 samples from multiple public databases to explore the effects of NUP205 on the prognosis, clinicopathological characteristics, regulatory mechanism, and tumor immune microenvironment (TIME) formation in LGG. First, multiple methods consistently showed that the mRNA and protein expression levels of NUP205 were higher in LGG tumor tissue than in normal brain tissue. This increased expression was mainly noted in the higher WHO Grade, IDH-wild type, and 1p19q non-codeleted type. Second, various survival analysis methods showed that the highly expressed NUP205 was an independent risk indicator that led to reduced survival time of patients with LGG. Third, GSEA analysis showed that NUP205 regulated the pathological progress of LGG via the cell cycle, notch signaling pathway, and aminoacyl-tRNA biosynthesis. Ultimately, immune correlation analysis suggested that high NUP205 expression was positively correlated with the infiltration of multiple immune cells, particularly M2 macrophages, and was positively correlated with eight immune checkpoints, particularly PD-L1. Collectively, this study documented the pathogenicity of NUP205 in LGG for the first time, expanding our understanding of its molecular function. Furthermore, this study highlighted the potential value of NUP205 as a target of anti-LGG immunotherapy.

Establishment of a risk score model for bladder urothelial carcinoma based on energy metabolism-related genes and their relationships with immune infiltration

Bladder urothelial carcinoma (BLCA) is a common malignant tumor of the human urinary system, and a large proportion of BLCA patients have a poor prognosis. Therefore, there is an urgent need to find more efficient and sensitive biomarkers for the prognosis of BLCA patients in clinical practice. RNA sequencing (RNA-seq) data and clinical information were obtained from The Cancer Genome Atlas, and 584 energy metabolism-related genes (EMRGs) were obtained from the Reactome pathway database. Cox regression analysis and least absolute shrinkage and selection operator analysis were applied to assess prognostic genes and build a risk score model. The estimate and cibersort algorithms were used to explore the immune microenvironment, immune infiltration, and checkpoints in BLCA patients. Furthermore, we used the Human Protein Atlas database and our single-cell RNA-seq datasets of BLCA patients to verify the expression of 13 EMRGs at the protein and single-cell levels. We constructed a risk score model; the area under the curve of the model at 5 years was 0.792. The risk score was significantly correlated with the immune markers M0 macrophages, M2 macrophages, CD8 T cells, follicular helper T cells, regulatory T cells, and dendritic activating cells. Furthermore, eight immune checkpoint genes were significantly upregulated in the high-risk group. The risk score model can accurately predict the prognosis of BLCA patients and has clinical application value. In addition, according to the differences in immune infiltration and checkpoints, BLCA patients with the most significant benefit can be selected for immune checkpoint inhibitor therapy.

LASSO-based screening for potential prognostic biomarkers associated with glioblastoma

Background

Glioblastoma is the most common malignancy of the neuroepithelium, yet existing research on this tumor is limited. LASSO is an algorithm of selected feature coefficients by which genes associated with glioblastoma prognosis can be obtained.

Methods

Glioblastoma-related data were selected from the Cancer Genome Atlas (TCGA) database, and information was obtained for 158 samples, including 153 cancer samples and five samples of paracancerous tissue. In addition, 2,642 normal samples were selected from the Genotype-Tissue Expression (GTEx) database. Whole-gene bulk survival analysis and differential expression analysis were performed on glioblastoma genes, and their intersections were taken. Finally, we determined which genes are associated with glioma prognosis. The STRING database was used to analyze the interaction network between genes, and the MCODE plugin under Cytoscape was used to identify the highest-scoring clusters. LASSO prognostic analysis was performed to identify the key genes. Gene expression validation allowed us to obtain genes with significant expression differences in glioblastoma cancer samples and paracancer samples, and glioblastoma independent prognostic factors could be derived by univariate and multivariate Cox analyses. GO functional enrichment analysis was performed, and the expression of the screened genes was detected using qRT-PCR.

Results

Whole-gene bulk survival analysis of glioblastoma genes yielded 607 genes associated with glioblastoma prognosis, differential expression analysis yielded 8,801 genes, and the intersection of prognostic genes with differentially expressed genes (DEG) yielded 323 intersecting genes. PPI analysis of the intersecting genes revealed that the genes were significantly enriched in functions such as the formation of a pool of free 40S subunits and placenta development, and the highest-scoring clusters were obtained using the MCODE plug-in. Eight genes associated with glioblastoma prognosis were identified based on LASSO analysis: RPS10, RPS11, RPS19, RSL24D1, RPL39L, EIF3E, NUDT5, and RPF1. All eight genes were found to be highly expressed in the tumor by gene expression verification, and univariate and multivariate Cox analyses were performed on these eight genes to identify RPL39L and NUDT5 as two independent prognostic factors associated with glioblastoma. Both RPL39L and NUDT5 were highly expressed in glioblastoma cells.

Conclusion

Two independent prognostic factors in glioblastoma, RPL39L and NUDT5, were identified.

A Novel Glycolysis-Related Gene Signature Predicts Prognosis For Cutaneous Melanoma

BACKGROUND

There exists a lack of effective tools predicting prognosis for cutaneous melanoma patients. Glycolysis plays an essential role in the carcinogenesis process.

OBJECTIVE

We intended to construct a new prognosis model for cutaneous melanoma.

METHODS

Based on the data from the TCGA database, we conducted a univariate Cox regression analysis and identified prognostic glycolysis-related genes (GRGs). Meanwhile, the GSE15605 dataset was used to identify differentially expressed genes (DEGs). The intersection of prognostic GRGs and DEGs was extracted for the subsequent multivariate Cox regression analysis.

RESULTS

A prognostic signature containing ten GRGs was built, and the TCGA cohort was classified into high and low risk subgroups based on the risk score of each patient. K-M analysis manifested that the overall survival of the high-risk group was statistically worse than that of the lowrisk group. Further study indicated that the risk-score could be used as an independent prognostic factor that effectively predicted the clinical prognosis in patients of different ages, genders, and stages. GO and KEGG enrichment analysis showed DEGs between high and low risk groups were enriched in immune-related functions and pathways. In addition, a significant difference existed between high and low risk groups in infiltration pattern of immune cells and expression levels of inhibitory immune checkpoint genes.

CONCLUSION

A new glycolysis-related gene signature was established for identifying cutaneous melanoma patients with poor prognoses and formulating individualized treatment.

Identification of tumor microenvironment-related signature for predicting prognosis and immunotherapy response in patients with bladder cancer

The tumor microenvironment (TME) not only provides fertile soil for tumor growth and development but also widely involves immune evasion as well as the resistance towards therapeutic response. Accumulating interest has been attracted from the biological function of TME to its effects on patient outcomes and treatment efficacy. However, the relationship between the TME-related gene expression profiles and the prognosis of bladder cancer (BLCA) remains unclear. The TME-related genes expression data of BLCA were collected from The Cancer Genome Atlas (TCGA) database. NFM algorithm was used to identify the distinct molecular pattern based on the significantly different TME-related genes. LASSO regression and Cox regression analyses were conducted to identify TME-related gene markers related to the prognosis of BLCA and to establish a prognostic model. The predictive efficacy of the risk model was verified through integrated bioinformatics analyses. Herein, 10 TME-related genes (PFKFB4, P4HB, OR2B6, OCIAD2, OAS1, KCNJ15, AHNAK, RAC3, EMP1, and PRKY) were identified to construct the prognostic model. The established risk scores were able to predict outcomes at 1, 3, and 5 years with greater accuracy than previously known models. Moreover, the risk score was closely associated with immune cell infiltration and the immunoregulatory genes including T cell exhaustion markers. Notably, the predictive power of the model in immunotherapy sensitivity was verified when it was applied to patients with metastatic urothelial carcinoma (mUC) undergoing immunotherapy. In conclusion, TME risk score can function as an independent prognostic biomarker and a predictor for evaluating immunotherapy response in BLCA patients, which provides recommendations for improving patients' response to immunotherapy and promoting personalized tumor immunotherapy in the future.

The roles of glycolysis in osteosarcoma

Metabolic reprogramming is of great significance in the progression of various cancers and is critical for cancer progression, diagnosis, and treatment. Cellular metabolic pathways mainly include glycolysis, fat metabolism, glutamine decomposition, and oxidative phosphorylation. In cancer cells, reprogramming metabolic pathways is used to meet the massive energy requirement for tumorigenesis and development. Metabolisms are also altered in malignant osteosarcoma (OS) cells. Among reprogrammed metabolisms, alterations in aerobic glycolysis are key to the massive biosynthesis and energy demands of OS cells to sustain their growth and metastasis. Numerous studies have demonstrated that compared to normal cells, glycolysis in OS cells under aerobic conditions is substantially enhanced to promote malignant behaviors such as proliferation, invasion, metastasis, and drug resistance of OS. Glycolysis in OS is closely related to various oncogenes and tumor suppressor genes, and numerous signaling pathways have been reported to be involved in the regulation of glycolysis. In recent years, a vast number of inhibitors and natural products have been discovered to inhibit OS progression by targeting glycolysis-related proteins. These potential inhibitors and natural products may be ideal candidates for the treatment of osteosarcoma following hundreds of preclinical and clinical trials. In this article, we explore key pathways, glycolysis enzymes, non-coding RNAs, inhibitors, and natural products regulating aerobic glycolysis in OS cells to gain a deeper understanding of the relationship between glycolysis and the progression of OS and discover novel therapeutic approaches targeting glycolytic metabolism in OS.

In Silico Establishment and Validation of Novel Lipid Metabolism-Related Gene Signature in Bladder Cancer

Background

Aberrant lipid metabolism is an alteration common to many types of cancer. Dysregulation of lipid metabolism is considered a major risk factor for bladder cancer. Accordingly, we focused on genes related to lipid metabolism and screened novel markers for predicting the prognosis of bladder cancer.

Methods

RNA-seq data for bladder cancer were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The nonnegative matrix factorization (NMF) algorithm was used to classify the molecular subtypes. Weighted correlation network analysis (WGCNA) was applied to identify coexpressed genes, and least absolute shrinkage and selection operator (LASSO) multivariate Cox analysis was used to construct a prognostic risk model. External validation data and in vitro experiments were used to verify the results from in silico analysis.

Results

Bladder cancer samples were grouped into two clusters based on the NMF algorithm. A total of 1467 genes involved in coexpression modules were identified in WGCNA. We finally established a 5-gene signature (TM4SF1, KCNK5, FASN, IMPDH1, and KCNJ15) that exhibited good stability across different datasets and was also an independent risk factor for prognosis. Furthermore, the predictive efficacy of our model was generally higher than the predictive efficacy of other published models. Distinct risk groups of patients also showed significantly different immune infiltration cell patterns and associations with clinical variables. Moreover, the 5 signature genes were verified in clinical samples by quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry, which were in agreement with the in silico analysis. For in vitro experiments, knockdown of IMPDH1 markedly inhibited cell proliferation in bladder cancer.

Conclusion

We established a 5-gene prognosis signature based on lipid metabolism in bladder cancer, which could be an effective prognostic indicator.

Identification and validation of an immune-related gene pairs signature for three urologic cancers

Reliable biomarkers are needed to recognize urologic cancer patients at high risk for recurrence. In this study, we built a novel immune-related gene pairs signature to simultaneously predict recurrence for three urologic cancers. We gathered 14 publicly available gene expression profiles including bladder, prostate and kidney cancer. A total of 2,700 samples were classified into the training set (n = 1,622) and validation set (n = 1,078). The 25 immune-related gene pairs signature consisting of 41 unique genes was developed by the least absolute shrinkage and selection operator regression analysis and Cox regression model. The signature stratified patients into high- and low-risk groups with significantly different relapse-free survival in the meta-training set and its subpopulations, and was an independent prognostic factor of urologic cancers. This signature showed a robust ability in the meta-validation and multiple independent validation cohorts. Immune and inflammatory response, chemotaxis and cytokine activity were enriched with genes relevant to the signature. A significantly higher infiltration level of M1 macrophages was found in the high-risk group versus the low-risk group. In conclusion, our signature is a promising prognostic biomarker for predicting relapse-free survival in patients with urologic cancer.

A glycolysis-related two-gene risk model that can effectively predict the prognosis of patients with rectal cancer

Background

Aerobic glycolysis is an emerging hallmark of cancer. Although some studies have constructed glycolysis-related prognostic models of colon adenocarcinoma (COAD) based on The Cancer Genome Atlas (TCGA) database, whether the COAD glycolysis-related prognostic model is appropriate for distinguishing the prognosis of rectal adenocarcinoma (READ) patients remains unknown. Exploring critical and specific glycolytic genes related to READ prognosis may help us discover new potential therapeutic targets for READ patients.

Results

Three gene sets, HALLMARK_GLYCOLYSIS, REACTOME_GLYCOLYSIS and REACTOME_REGULATION_OF_GLYCOLYSIS_BY_FRUCTOSE_2_6_BISPHOSPHATE_METABOLISM, were both significantly enriched in both COAD and READ through glycolysis-related gene set enrichment analysis (GSEA). We found that six genes (ANKZF1, STC2, SUCLG2P2, P4HA1, GPC1 and PCK1) were independent prognostic genes in COAD, while TSTA3 and PKP2 were independent prognostic genes in READ. Glycolysis-related prognostic model of COAD and READ was, respectively, constructed and assessed in COAD and READ. We found that the glycolysis-related prognostic model of COAD was not appropriate for READ, while glycolysis-related prognostic model of READ was more appropriate for READ than for COAD. PCA and t-SNE analysis confirmed that READ patients in two groups (high and low risk score groups) were distributed in discrete directions based on the glycolysis-related prognostic model of READ. We found that this model was an independent prognostic indicator through multivariate Cox analysis, and it still showed robust effectiveness in different age, gender, M stage, and TNM stage. A nomogram combining the risk model of READ with clinicopathological characteristics was established to provide oncologists with a practical tool to evaluate the rectal cancer outcomes. GO enrichment and KEGG analyses confirmed that differentially expressed genes (DEGs) were enriched in several glycolysis-related molecular functions or pathways based on glycolysis-related prognostic model of READ.

Conclusions

We found that a glycolysis-related prognostic model of COAD was not appropriate for READ, and we established a novel glycolysis-related two-gene risk model to effectively predict the prognosis of rectal cancer patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40246-022-00377-0.

A Novel Glycolysis and Hypoxia Combined Gene Signature Predicts the Prognosis and Affects Immune Infiltration of Patients with Colon Cancer

Purpose

We aimed to characterize the expression patterns of glycolysis and hypoxia genes in colon cancers as well as their value in prognosis and immune microenvironment.

Methods

The expression profiles were acquired from the Cancer Genome Atlas database. Enrichment of hypoxia and glycolysis gene sets in colon cancer was identified by gene set enrichment analysis. Then, a prognostic signature was built up after Cox regression analyses, and overall survival analysis validated the predictive ability. Immune status and infiltration in cancer tissues were explored using the single sample gene set enrichment analysis and CIBERSORT algorithm. A nomogram model integrating clinical variables and the gene signature was established and assessed.

Results

Altogether, 378 cancer and 39 control cases were enrolled. Three glycolysis gene sets and two hypoxia gene sets were enriched in colon cancer (P < 0.05). Five independent genes (ENO3, GPC1, P4HA1, SPAG4, and STC2) were significantly correlated with prognosis of colon cancer patients. Patients with higher risks had significantly better prognosis than those with lower risks (P = 0.002 and AUC = 0.750), which was also observed in the elderly, female and stage I–II subgroups (P < 0.05). In high-risk cases, proportion of NK cells resting increased (P < 0.05) while that of dendritic cells activated (P < 0.05), dendritic cells resting (P < 0.01) and monocytes (P < 0.01) decreased. Besides, expressions of 22 checkpoint genes were found abnormal in groups with different risks (P < 0.05). The predictive nomogram presented satisfactory performance with C-index of 0.771 (0.712–0.830). The area under ROC curve was 0.796 and 0.803 for 3- and 5-year survival prediction, respectively.

Conclusion

A glycolysis and hypoxia combined gene signature was a promising method to evaluate the prognosis and immune infiltration of colon cancer patients, which may provide a new tool for cancer management.

Development and Validation of Prognostic Model in Transitional Bladder Cancer Based on Inflammatory Response-Associated Genes

Background

Bladder cancer is a common malignant type in the world, and over 90% are transitional cell carcinoma. While the impact of inflammatory response on cancer progression has been reported, the role of inflammatory response-associated genes (IRAGs) in transitional bladder cancer still needs to be understood.

Methods

In this study, IRAGs were download from Molecular Signature Database (MSigDB). The transcriptional expression and matched clinicopathological data were separately obtained from public databases. The TCGA-BLCA cohort was used to identify the differentially expressed IRAGs, and prognostic IRAGs were filtrated by univariate survival analysis. The intersection between them was displayed by Venn diagram. Based on least absolute shrinkage and selection operator (LASSO) regression analysis method, the TCGA-BLCA cohort was used to construct a risk signature. Survival analysis was conducted to calculate the overall survival (OS) in TCGA and GSE13507 cohort between two groups. We then conducted univariate and multivariate survival analyses to identify independently significant indicators for prognosis. Relationships between the risk scores and age, grade, stage, immune cell infiltration, immune function, and drug sensitivity were demonstrated by correlation analysis. The expression level of prognostic genes in vivo and in vitro were determined by qRT-PCR assay.

Results

Comparing with normal tissues, there were 49 differentially expressed IRAGs in cancer tissues, and 12 of them were markedly related to the prognosis in TCGA cohort for transitional bladder cancer patients. Based on LASSO regression analysis, a risk model consists of 10 IRAGs was established. Comparing with high-risk groups, survival analysis showed that patients in low-risk groups were more likely to have a better survival time in TCGA and GSE13507 cohorts. Besides, the accuracy of the model in predicting prognosis is acceptable, which is demonstrated by receiver operating characteristic curve (ROC) analysis. Age, stage, and risk scores variables were identified as the independently significant indicators for survival in transitional bladder cancer. Correlation analysis represented that the risk score was identified to be significantly related to the above variables except gender variable. Moreover, the expression level of prognostic genes in vivo and in vitro was markedly upregulated for transitional bladder cancer.

Conclusions

A novel model based on the 10 IRAGs that can be used to predict survival time for transitional bladder cancer. In addition, this study may provide treatment strategies according to the drug sensitivity in the future.

Hypoxia-related prognostic model in bladder urothelial reflects immune cell infiltration

Hypoxia is a common feature of tumor microenvironment (TME). This study aims to establish the genetic features related to hypoxia in Bladder urothelial carcinoma (BLCA) and investigate the potential correlation with hypoxia in the TME and immune cells. We established a BLCA outcome model using the hypoxia-related genes from The Cancer Genome Atlas using regression analysis and verified the model using the Gene Expression Omnibus GSE32894 cohort. We measured the effect of each gene in the hypoxia-related risk model using the Human Protein Atlas website. The predictive abilities were compared using the area under the receiver operating characteristic curves. Gene Set Enrichment Analysis was utilized for indicating enrichment pathways. We analyzed immune cell infiltration between risk groups using the CIBERSORT method. The indicators related to immune status between the two groups were also analyzed. The findings indicated that the high-risk group had better outcomes than the low-risk group in the training and validation sets. Each gene in the model affected the survival of BLCA patients. Our hypoxia-related risk model had better performance compared to other hypoxia-related markers (HIF-1α and GLUT-1). The high-risk group was enriched in immune-related pathways. The expression of chemokines and immune cell markers differed significantly between risk groups. Immune checkpoints were more highly expressed in the high-risk group. These findings suggest that the hypoxia-related risk model predicts patients' outcomes and immune status in BLCA risk groups. Our findings may contribute to the treatment of BLCA.

A Novel Risk Factor Model Based on Glycolysis-Associated Genes for Predicting the Prognosis of Patients With Prostate Cancer

BACKGROUND

Prostate cancer (PCa) is one of the most prevalent cancers among males, and its mortality rate is increasing due to biochemical recurrence (BCR). Glycolysis has been proven to play an important regulatory role in tumorigenesis. Although several key regulators or predictors involved in PCa progression have been found, the relationship between glycolysis and PCa is unclear; we aimed to develop a novel glycolysis-associated multifactor prediction model for better predicting the prognosis of PCa.

METHODS

Differential mRNA expression profiles derived from the Cancer Genome Atlas (TCGA) PCa cohort were generated through the "edgeR" package. Glycolysis-related genes were obtained from the GSEA database. Univariate Cox and LASSO regression analyses were used to identify genes significantly associated with disease-free survival. ROC curves were applied to evaluate the predictive value of the model. An external dataset derived from Gene Expression Omnibus (GEO) was used to verify the predictive ability. Glucose consumption and lactic production assays were used to assess changes in metabolic capacity, and Transwell assays were used to assess the invasion and migration of PC3 cells.

RESULTS

Five glycolysis-related genes were applied to construct a risk score prediction model. Patients with PCa derived from TCGA and GEO (GSE70770) were divided into high-risk and low-risk groups according to the median. In the TCGA cohort, the high-risk group had a poorer prognosis than the low-risk group, and the results were further verified in the GSE70770 cohort. In vitro experiments demonstrated that knocking down HMMR, KIF20A, PGM2L1, and ANKZF1 separately led to less glucose consumption, less lactic production, and inhibition of cell migration and invasion, and the results were the opposite with GPR87 knockdown.

CONCLUSION

The risk score based on five glycolysis-related genes may serve as an accurate prognostic marker for PCa patients with BCR.

Development and Validation of a Prognostic Model for Predicting Overall Survival in Patients With Bladder Cancer: A SEER-Based Study

OBJECTIVE

To establish a prognostic model for Bladder cancer (BLCA) based on demographic information, the American Joint Commission on Cancer (AJCC) 7th staging system, and additional treatment using the surveillance, epidemiology, and end results (SEER) database.

METHODS

Cases with BLCA diagnosed from 2010-2015 were collected from the SEER database, while patient records with incomplete information on pre-specified variables were excluded. All eligible cases were included in the full analysis set, which was then split into training set and test set with a 1:1 ratio. Univariate and multivariate Cox regression analyses were conducted to identify prognostic factors for overall survival (OS) in BLCA patients. With selected independent prognosticators, a nomogram was mapped to predict OS for BLCA. The nomogram was evaluated using receiver operating characteristic (ROC) analysis and calibration plot in both the training and test sets. The area under curve [AUC] of the nomogram was calculated and compared with clinicopathological indicators using the full analysis set. Statistical analyses were conducted using the R software, where P-value <0.05 was considered significant.

RESULTS

The results indicated that age, race, sex, marital status, histology, tumor-node-metastasis (TNM) stages based on the AJCC 7th edition, and additional chemotherapy were independent prognostic factors for OS in patients with BLCA. Patients receiving chemotherapy tend to have better survival outcomes than those without. The proposed nomogram showed decent classification (AUCs >0.8) and prediction accuracy in both the training and test sets. Additionally, the AUC of the nomogram was observed to be better than that of conventional clinical indicators.

CONCLUSIONS

The proposed nomogram incorporated independent prognostic factors including age, race, sex, marital status, histology, tumor-node-metastasis (TNM) stages, and additional chemotherapy. Patients with BLCA benefit from chemotherapy on overall survival. The nomogram-based prognostic model could predict overall survival for patients with BLCA with accurate stratification, which is superior to clinicopathological factors.

A Glycolysis-Based Long Non-coding RNA Signature Accurately Predicts Prognosis in Renal Carcinoma Patients

Background

The prognosis of renal cell carcinoma (RCC) varies greatly among different risk groups, and the traditional indicators have limited effect in the identification of risk grade in patients with RCC. The purpose of our study is to explore a glycolysis-based long non-coding RNAs (lncRNAs) signature and verify its potential clinical significance in prognostic prediction of RCC patients.

Methods

In this study, RNA data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate cox regression displayed six significantly related lncRNAs (AC124854.1, AC078778.1, EMX2OS, DLGAP1-AS2, AC084876.1, and AC026401.3) which were utilized in construction of risk score by a formula. The accuracy of risk score was verified by a series of statistical methods such as receiver operating characteristic (ROC) curves, nomogram and Kaplan-Meier curves. Its potential clinical significance was excavated by gene enrichment analysis.

Results

Kaplan-Meier curves and ROC curves showed reliability of the risk score to predict the prognosis of RCC patients. Stratification analysis indicated that the risk score was independent predictor compare to other traditional clinical parameters. The clinical nomogram showed highly rigorous with index of 0.73 and precisely predicted 1-, 3-, and 5-year survival time of RCC patients. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene set enrichment analysis (GSEA) depicted the top ten correlated pathways in both high-risk group and low-risk group. There are 6 lncRNAs and 25 related mRNAs including 36 lncRNA-mRNA links in lncRNA-mRNA co-expression network.

Conclusion

This research demonstrated that glycolysis-based lncRNAs possessed an important value in survival prediction of RCC patients, which would be a potential target for future treatment.

The novel prognostic risk factor STC2 can regulate the occurrence and progression of osteosarcoma via the glycolytic pathway

Osteosarcoma, a highly aggressive malignant tumor of the bone, usually occurs in children and young adults. However, although the considerable achievement in the clinical treatment of osteosarcoma recent years, the overall survival of osteosarcoma patients has not been obviously improved. Cancer cells preferentially use glycolysis instead of oxidative phosphorylation to meet their increased energetic and biosynthetic demands, a phenomenon known as the Warburg effect. Glycolysis is a driving factor in multiple cancers and is emerging as a new cancer target treatment. In the present study, we established a model to screen for glycolysis-associated genes in osteosarcoma. This risk score of the model were correlated with clinical characteristics osteosarcoma patients. Besides, a functional assay identified that STC2 enhanced the glycolysis of osteosarcoma cells. Modulation of STC2 changes glucose consumption and lactate production as well as GLUT1 expression in osteosarcoma. Furthermore, we identified that change in the expression levels of STC2 affected the proliferation, invasion, and migration of osteosarcoma cells. Our findings showed STC2 as a new tumor-promoting factor of osteosarcoma cells through enhancing glycolysis.

Identification of a Novel Glycolysis-Related Gene Signature Correlates With the Prognosis and Therapeutic Responses in Patients With Clear Cell Renal Cell Carcinoma

Background

Accumulating evidences indicate significant alterations in the aerobic glycolysis in clear cell renal cell carcinoma (ccRCC). We aim to develop and validate a glycolysis-related genes signature for predicting the clinical outcomes of patients with ccRCC.

Methods

mRNA expression profiling of ccRCC was obtained from The Cancer Genome Atlas database. Univariate Cox regression analysis and lasso Cox regression model were performed to identify and construct the prognostic gene signature. The protein expression levels of the core genes were obtained from the Human Protein Atlas database. We used four external independent data sets to verify the predictive power of the model for prognosis, tyrosine kinase inhibitor (TKI) therapy, and immunotherapy responses, respectively. Finally, we explored the potential mechanism of this signature through gene set enrichment analysis (GSEA).

Results

Through the GSEA, glycolysis-related gene sets were significantly different between ccRCC tissues and normal tissues. Next, we identified and constructed a seven-mRNA signature (GALM, TGFA, RBCK1, CD44, HK3, KIF20A, and IDUA), which was significantly correlated with worse survival outcome and was an independent prognostic indicator for ccRCC patients. Furthermore, the expression levels of hub genes were validated based on the Human Protein Atlas databases. More importantly, the model can predict patients’ response to TKI therapy and immunotherapy. These findings were successfully validated in the external independent ccRCC cohorts. The mechanism exploration showed that the model may influence the prognosis by influencing tumor proliferation, base mismatch repair system and immune status of patients.

Conclusions

Our study has built up a robust glycolysis-based molecular signature that predicts the prognosis and TKI therapy and immunotherapy responses of patients with ccRCC with high accuracy, which might provide important guidance for clinical assessment. Also, clinical investigations in large ccRCC cohorts are greatly needed to validate our findings.

Correlations between glycolysis with clinical traits and immune function in bladder urothelial carcinoma

BACKGROUND

Glycolysis was a representative hallmark in the tumor microenvironment (TME), and we aimed to explore the correlations between glycolysis with immune activity and clinical traits in bladder urothelial carcinoma (BLCA).

METHODS

Our study obtained glycolysis scores for each BLCA samples from TCGA by a single-sample gene set enrichment analysis (ssGSEA) algorithm, based on a glycolytic gene set. The relationship between glycolysis with prognosis, clinical characteristics, and immune function were investigated subsequently.

RESULTS

We found that enhanced glycolysis was associated with poor prognosis and metastasis in BLCA. Moreover, glycolysis had a close correlation with immune function, and enhanced glycolysis increased immune activities. In other words, glycolysis had a positive correlation with immune activities. Immune checkpoints such as IDO1, CD274, were up-regulated in high-glycolysis group as well.

CONCLUSION

We speculated that in BLCA, elevated glycolysis enhanced immune function, which caused tumor cells to overexpress immune checkpoints to evade immune surveillance. Inhibition of glycolysis might be a promising assistant for immunotherapy in bladder cancer.

Six Glycolysis-Related Genes as Prognostic Risk Markers Can Predict the Prognosis of Patients with Head and Neck Squamous Cell Carcinoma

Objective

Head and neck squamous cell carcinoma (HNSCC) is one of the worst-prognosis malignant tumors. This study used bioinformatic analysis of the transcriptome sequencing data of HNSCC and the patients' survival and clinical data to construct a prediction signature of glycolysis-related genes as the prognostic risk markers.

Methods

Gene expression profile data about HNSCC tissues (n = 498) and normal tissues in the head and neck (n = 44) were got from The Cancer Genome Atlas (TCGA), as well as patients' survival and clinical data. Then, we obtained core genes; their expression in head and neck squamous cell carcinoma tissues is significantly different from that in normal head and neck tissues. The predicted glycolysis-related genes are screened through univariate Cox regression analysis, and then, the prognostic risk markers were constructed through further correction of multivariate Cox regression analysis. The Kaplan-Meier curve and receiver operating characteristic curve are used to analyze the potential value of these risk markers in diagnosis and prognosis. We also evaluated that the glycolysis-related prognostic risk markers composed of 6 oncogenes are correlated with clinical features, such as age, gender, grade, and clinical stage of the tumor, by univariate and multivariate Cox regression analyses.

Results

Differentially expressed glycolytic genes in HNSCC tissues and normal head and neck tissues were screened from TCGA databases using the bioinformatic method. We confirmed a set of six glycolytic genes that were significantly associated with OS in the test series. According to our analysis, the prognostic risk markers composed of HPRT1, STC2, PLCB3, GPR87, PYGL, and SLC5A12 may be an independent risk factor for the prognosis of HNSCC.

Conclusions

Through this analysis, we constructed new prognostic risk markers related to glycolysis as a prognostic risk marker for patients with HNSCC and provided new ideas and molecular targets for the research and individualized treatment of HNSCC.

An epithelial-mesenchymal transition-related long noncoding RNA signature correlates with the prognosis and progression in patients with bladder cancer

Bladder cancer is a common malignant tumour worldwide. Epithelial-mesenchymal transition (EMT)-related biomarkers can be used for early diagnosis and prognosis of cancer patients. To explore, accurate prediction models are essential to the diagnosis and treatment for bladder cancer. In the present study, an EMT-related long noncoding RNA (lncRNA) model was developed to predict the prognosis of patients with bladder cancer. Firstly, the EMT-related lncRNAs were identified by Pearson correlation analysis, and a prognostic EMT-related lncRNA signature was constructed through univariate and multivariate Cox regression analyses. Then, the diagnostic efficacy and the clinically predictive capacity of the signature were assessed. Finally, Gene set enrichment analysis (GSEA) and functional enrichment analysis were carried out with bioinformatics. An EMT-related lncRNA signature consisting of TTC28-AS1, LINC02446, AL662844.4, AC105942.1, AL049840.3, SNHG26, USP30-AS1, PSMB8-AS1, AL031775.1, AC073534.1, U62317.2, C5orf56, AJ271736.1, and AL139385.1 was constructed. The diagnostic efficacy of the signature was evaluated by the time-dependent receiver-operating characteristic (ROC) curves, in which all the values of the area under the ROC (AUC) were more than 0.73. A nomogram established by integrating clinical variables and the risk score confirmed that the signature had a good clinically predict capacity. GSEA analysis revealed that some cancer-related and EMT-related pathways were enriched in high-risk groups, while immune-related pathways were enriched in low-risk groups. Functional enrichment analysis showed that EMT was associated with abundant GO terms or signaling pathways. In short, our research showed that the 14 EMT-related lncRNA signature may predict the prognosis and progression of patients with bladder cancer.

The effect of a novel glycolysis-related gene signature on progression, prognosis and immune microenvironment of renal cell carcinoma

Background

Glycolysis is a central metabolic pathway for tumor cells. However, the potential roles of glycolysis-related genes in renal cell carcinoma (RCC) have not been investigated.

Methods

Seven glycolysis-related gene sets were selected from MSigDB and were analyzed through GSEA. Using TCGA database, the glycolysis-related gene signature was constructed. Prognostic analyses were based on the Kaplan–Meier method. The cBioPortal database was employed to perform the mutation analyses. The CIBERSORT algorithm and TIMER database were used to determine the immunological effect of glycolytic gene signature. The expressions in protein level of eight glycolytic risk genes were determined by HPA database. Finally, qPCR, MTT and Transwell invasion assays were conducted to validate the roles of core glycolytic risk genes (CD44, PLOD1 and PLOD2) in RCC.

Results

Four glycolysis-related gene sets were significantly enriched in RCC samples. The glycolytic risk signature was constructed (including CD44, PLOD2, KIF20A, IDUA, PLOD1, HMMR, DEPDC1 and ANKZF1) and identified as an independent RCC prognostic factor (HR = 1.204). Moreover, genetic alterations of glycolytic risk genes were uncommon in RCC (10.5%) and glycolytic risk signature can partially affect immune microenvironment of RCC. Six glycolytic risk genes (except for IDUA and HMMR) were over-expression in A498 and 786-O renal cancer cells through qPCR test. MTT and Transwell assays revealed that silencing of CD44, PLOD1 and PLOD2 suppressed the proliferation and invasion of renal cancer cells.

Conclusions

The glycolysis-related risk signature is closely associated with RCC prognosis, progression and immune microenvironment. CD44, PLOD1 and PLOD2 may serve as RCC oncogenes.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-020-07702-7.

Development and Validation of a Scoring System Based on 9 Glycolysis-Related Genes for Prognosis Prediction in Gastric Cancer

Gastric cancer is a malignant tumor with high morbidity and mortality worldwide. However, increasing evidences have revealed the correlation between the glycolysis process and tumorigenesis. This study is aim to develop a list of glycolysis-related genes for risk stratification in gastric cancer patients. We included 500 patients’ sample data from GSE62254 and GSE26942 datasets, and classified patients into training (n = 350) and testing sets (n = 150) at a ratio of 7: 3. Univariate and multivariate Cox regression analysis were performed to screen genes having prognostic value. Based on HALLMARK gene sets, we identified 9 glycolysis-related genes (BPNT1, DCN, FUT8, GMPPA, GPC3, LDHC, ME2, PLOD2, and UGP2). On the basis of risk score developed by the 9 genes, patients were classified into high- and low-risk groups. The survival analysis showed that the high-risk patients had a worse prognosis (p < 0.001). Similar finding was observed in the testing cohort and 2 independent cohorts (GSE13861 and TCGA-STAD, all p < 0.001). The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for overall survival (p < 0.001). Furthermore, we constructed a nomogram that integrated the risk score and tumor stage, age, and adjuvant chemotherapy. Through comparing the results of the receiver operating characteristic curves and decision curve analysis, we found that the nomogram had a superior predictive accuracy than conventional TNM staging system, suggesting that the risk score combined with other clinical factors (age, tumor stage, and adjuvant chemotherapy) can develop a robust prediction for survival and improve the individualized clinical decision making of the patient.

In conclusion, we identified 9 glycolysis-related genes from hallmark glycolysis pathway. Based on the 9 genes, gastric cancer patients were separated into different risk groups related to survival.