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

Knockdown of ADAM8 inhibits the proliferation, migration, invasion, and tumorigenesis of renal clear cell carcinoma cells to enhance the immunotherapy efficacy

The current study performed bioinformatics and in vitro and in vivo experiments to explore the effects of ADAM8 on the malignant behaviors and immunotherapeutic efficacy of renal clear cell carcinoma (ccRCC) Cells. The modular genes most associated with immune cells were screened. Then, prognostic risk models were constructed by univariate COX analysis, LASSO regression analysis and multivariate COX analysis, and their diagnostic value was determined. The correlation between tumor mutation load (TMB) scores and the prognosis of ccRCC patients was clarified. Finally, six key genes (ABI3, ADAM8, APOL3, MX2, CCDC69, and STAC3) were analyzed for immunotherapy efficacy. Human and mouse ccRCC cell lines and human proximal tubular epithelial cell lines were used for in vitro cell experiments. The effect of ADAM8 overexpression or knockdown on tumor formation and survival in ccRCC cells was examined by constructing subcutaneous transplanted tumor model. Totally, 636 Black module genes were screened as being most associated with immune cell infiltration. Six genes were subsequently confirmed for the construction of prognostic risk models, of which ABI3, APOL3 and CCDC69 were low-risk factors, while ADAM8, MX2 and STAC3 were high-risk factors. The constructed risk model based on the identified six genes could accurately predict the prognosis of ccRCC patients. Besides, TMB was significantly associated with the prognosis of ccRCC patients. Furthermore, ABI3, ADAM8, APOL3, MX2, CCDC69 and STAC3 might play important roles in treatment concerning CTLA4 inhibitors or PD-1 inhibitors or combined inhibitors. Finally, we confirmed that ADAM8 could promote the proliferation, migration and invasion of ccRCC cells through in vitro experiments, and further found that in in vivo experiments, ADAM8 knockdown could inhibit tumor formation in ccRCC cells, improve the therapeutic effect of anti-PD1, and prolong the survival of mice. Our study highlighted the alleviative role of silencing ADAM8 in ccRCC patients.

FGFR3 mutation characterization identifies prognostic and immune-related gene signatures in bladder cancer

BACKGROUND

Immunotherapy and FGFR3-targeted therapy play an important role in the management of locally advanced and metastatic bladder cancer (BLCA). Previous studies indicated that FGFR3 mutation (mFGFR3) may be involved in the alterations of immune infiltration, which may affect the priority or combination of these two treatment regimes. However, the specific impact of mFGFR3 on the immunity and how FGFR3 regulates the immune response in BLCA to affect prognosis remain unclear. In this study, we aimed to elucidate the immune landscape associated with mFGFR3 status in BLCA, screen immune-related gene signatures with prognostic value, and construct and validate a prognostic model.

METHODS

ESTIMATE and TIMER were used to assess the immune infiltration within tumors in the TCGA BLCA cohort based on transcriptome data. Further, the mFGFR3 status and mRNA expression profiles were analyzed to identify immune-related genes that were differentially expressed between patients with BLCA with wild-type FGFR3 or mFGFR3 in the TCGA training cohort. An FGFR3-related immune prognostic score (FIPS) model was established in the TCGA training cohort. Furthermore, we validated the prognostic value of FIPS with microarray data in the GEO database and tissue microarray from our center. Multiple fluorescence immunohistochemical analysis was performed to confirm the relationship between FIPS and immune infiltration.

RESULTS

mFGFR3 resulted in differential immunity in BLCA. In total, 359 immune-related biological processes were enriched in the wild-type FGFR3 group, whereas none were enriched in the mFGFR3 group. FIPS could effectively distinguish high-risk patients with poor prognosis from low-risk patients. The high-risk group was characterized by a higher abundance of neutrophils; macrophages; and follicular helper, CD4, and CD8 T-cells than the low-risk group. In addition, the high-risk group exhibited higher expression of PD-L1, PD-1, CTLA-4, LAG-3, and TIM-3 than the low-risk group, indicating an immune-infiltrated but functionally suppressed immune microenvironment. Furthermore, patients in the high-risk group exhibited a lower mutation rate of FGFR3 than those in the low-risk group.

CONCLUSIONS

FIPS effectively predicted survival in BLCA. Patients with different FIPS exhibited diverse immune infiltration and mFGFR3 status. FIPS might be a promising tool for selecting targeted therapy and immunotherapy for patients with BLCA.

Screening of renal clear cell carcinoma prognostic marker genes based on TCGA and GTEx chip data and construction of transcription factor-related regulatory networks

This study aimed to identify prognostic marker genes for renal clear cell carcinoma (RCCC) and construct a regulatory network of transcription factors and prognostic marker genes. Three hundred eighty-six genes were significantly differentially expressed in RCCC, with functional enrichment analysis suggesting a relationship between these genes and kidney function and development. Cox and Lasso regression analyses revealed 10 prognostic marker genes (RNASET2, MSC, DPEP1, FGF1, ATP1A1, CLDN10, PLG, SLC44A1, PCSK1N, and LGI4) that accurately predicted RCCC patient prognosis. Upstream transcription factors of these genes were also identified, and in vitro experiments suggested that ATP1A1 may play a key role in RCCC patient prognosis. The findings of this study provide important insights into the molecular mechanisms of RCCC and may have implications for personalized treatment strategies.

A Ferroptosis-Related LncRNA Signature Associated with Prognosis, Tumor Immune Environment, and Genome Instability in Hepatocellular Carcinoma

Background

Ferroptosis is an iron-dependent form of cell death. In this study, we identified ferroptosis-related long noncoding RNAs (FRlncRNAs) to investigate their association with hepatocellular carcinoma (HCC) in prognosis, tumor immune environment, and genome instability.

Methods

Transcriptome profile data of HCC were retrieved from a public database. FRlncRNAs were identified by co-expression analysis. Patients were randomly divided into training and test cohorts. Univariate Cox analysis and Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression were performed to construct a risk model. Patients were divided into high- and low-risk groups based on the risk model. AUC and C index were used to assess the risk model. Survival analysis, immune status, and genome instability were compared between the two groups.

Results

Sixteen FRlncRNAs were identified and used to construct an FRlncRNA signature for the risk model. The Kaplan-Meier analysis revealed that patients in the high-risk group had poorer overall survival than patients in the low-risk group. The area under curve of the risk model was 0.879, 0.809, and 0.757 in the training cohort and 0.635, 0.688, and 0.739 in the test cohort at 1, 2, and 3 years, respectively. The risk model was an independent prognostic predictor and showed excellent prediction of prognosis compared with clinicopathological features. For the differentially expressed ferroptosis-related genes, many enriched metabolic pathways were identified in the functional enrichment analysis. Immune cells such as CD8+ T cells, macrophages M1, natural killer cells, and B cells, which may be associated with antitumor immune responses, differed between the high- and low-risk groups. Genome instability based on the risk model was also explored. A total of 61 genes were differently mutated between the two risk groups, and among them, TP53, HECW2, TRIM66, MCTP2, and KIAA1551 had the most significant mutation frequency differences.

Conclusion

The FRlncRNA signature is closely related with overall survival, tumor immune environment, and genome instability in HCC.

Survival-related genes are diversified across cancers but generally enriched in cancer hallmark pathways

Background

Pan-cancer studies have disclosed many commonalities and differences in mutations, copy number variations, and gene expression alterations among cancers. Some of these features are significantly associated with clinical outcomes, and many prognosis-predictive biomarkers or biosignatures have been proposed for specific cancer types. Here, we systematically explored the biological functions and the distribution of survival-related genes (SRGs) across cancers.

Results

We carried out two different statistical survival models on the mRNA expression profiles in 33 cancer types from TCGA. We identified SRGs in each cancer type based on the Cox proportional hazards model and the log-rank test. We found a large difference in the number of SRGs among different cancer types, and most of the identified SRGs were specific to a particular cancer type. While these SRGs were unique to each cancer type, they were found mostly enriched in cancer hallmark pathways, e.g., cell proliferation, cell differentiation, DNA metabolism, and RNA metabolism. We also analyzed the association between cancer driver genes and SRGs and did not find significant over-representation amongst most cancers.

Conclusions

In summary, our work identified all the SRGs for 33 cancer types from TCGA. In addition, the pan-cancer analysis revealed the similarities and the differences in the biological functions of SRGs across cancers. Given the potential of SRGs in clinical utility, our results can serve as a resource for basic research and biotech applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08581-x.

Detecting prognostic biomarkers of breast cancer by regularized Cox proportional hazards models

BACKGROUND

The successful identification of breast cancer (BRCA) prognostic biomarkers is essential for the strategic interference of BRCA patients. Recently, various methods have been proposed for exploring a small prognostic gene set that can distinguish the high-risk group from the low-risk group.

METHODS

Regularized Cox proportional hazards (RCPH) models were proposed to discover prognostic biomarkers of BRCA from gene expression data. Firstly, the maximum connected network with 1142 genes by mapping 956 differentially expressed genes (DEGs) and 677 previously BRCA-related genes into the gene regulatory network (GRN) was constructed. Then, the 72 union genes of the four feature gene sets identified by Lasso-RCPH, Enet-RCPH, [Formula: see text]-RCPH and SCAD-RCPH models were recognized as the robust prognostic biomarkers. These biomarkers were validated by literature checks, BRCA-specific GRN and functional enrichment analysis. Finally, an index of prognostic risk score (PRS) for BRCA was established based on univariate and multivariate Cox regression analysis. Survival analysis was performed to investigate the PRS on 1080 BRCA patients from the internal validation. Particularly, the nomogram was constructed to express the relationship between PRS and other clinical information on the discovery dataset. The PRS was also verified on 1848 BRCA patients of ten external validation datasets or collected cohorts.

RESULTS

The nomogram highlighted that the importance of PRS in guiding significance for the prognosis of BRCA patients. In addition, the PRS of 301 normal samples and 306 tumor samples from five independent datasets showed that it is significantly higher in tumors than in normal tissues ([Formula: see text]). The protein expression profiles of the three genes, i.e., ADRB1, SAV1 and TSPAN14, involved in the PRS model demonstrated that the latter two genes are more strongly stained in tumor specimens. More importantly, external validation illustrated that the high-risk group has worse survival than the low-risk group ([Formula: see text]) in both internal and external validations.

CONCLUSIONS

The proposed pipelines of detecting and validating prognostic biomarker genes for BRCA are effective and efficient. Moreover, the proposed PRS is very promising as an important indicator for judging the prognosis of BRCA patients.

Effect of unfolded protein response on the immune infiltration and prognosis of transitional cell bladder cancer

Background: Bladder cancer (BC) is one of the most common human malignancies worldwide. Previous researches have shown that the unfolded protein response (UPR) pathway could contribute to the tumorigenesis of BC. However, the role of UPR in the immune infiltration, progression, and prognosis of BC is unclear.Methods: The GSVA and ssGSEA methods were used for assessing the UPR score and immune cells infiltration score in three BC public datasets, respectively. The relationship between the UPR pathway and clinicopathological characteristics was analyzed by the Kruskal-Wallis, Wilcox test, and log-rank test. The association of the UPR pathway with various tumor-infiltrating immune cells was evaluated with the correlation analysis. Univariate Cox regression analysis was performed to identify risk factors significantly associated with prognosis. The predictive models were built based on risk factors and visualized with nomograms. The performance of our models was evaluated with the calibration curve, Harrell's concordance index (c-index), and receiver operating characteristic (ROC) analysis.Results: We found that the UPR pathway and many UPR-related genes were significantly associated with the pathologic grade, tumor type, and invasive progression of transitional cell bladder cancer (TCBC), and a high UPR score predicted a poor prognosis in patients. The UPR score was positively correlated with the infiltration abundance of many tumor immune cells in TCBC. Besides, we constructed predictive models based on the UPR score, and good performance was observed, with c-indexes ranging from 0.74 to 0.87.Conclusions: Our study proved that the UPR pathway may have an important impact on the progression, prognosis, and tumor immune infiltration in TCBC, and the models we built may provide effective and reliable guides for prognosis assessment and treatment decision-making for TCBC patients.

Identification of Signature Genes Associated With Invasiveness and the Construction of a Prognostic Model That Predicts the Overall Survival of Bladder Cancer

Background: Bladder cancer has become the tenth most diagnosed cancer worldwide. The prognosis has been shown to differ between non-muscle invasive bladder cancer (NMIBC) and muscle invasive bladder cancer (MIBC). We aimed to identify signature genes that are associated with the invasiveness and survival of bladder cancer and to identify potential treatments. Methods: We downloaded gene expression profiles of bladder cancer from the Gene Expression Omnibus database to identify differentially expressed genes and perform weighted gene co-expression network analysis. Functional enrichment was analyzed by GO and KEGG analyses. Hub genes were identified from the significant module. Another dataset was also acquired to verify the expression of hub genes. Univariate and multivariate Cox regression analyses were applied to the dataset downloaded from The Cancer Genome Atlas database. Risk scores were calculated and the effect was evaluated by Kaplan-Meier survival analysis. A nomogram was constructed and validated using training and testing samples, respectively. Analysis of the tumor immune microenvironment was conducted with the CIBERSORT algorithm. Results: In total, 1,245 differentially expressed genes (DEGs) were identified. A distinct module was identified that was significantly correlated to invasiveness. The genes within this module were found to be significantly associated with extracellular exosomes, GTPase activity, metabolic pathways, etc. Three hub genes (VSIG2, PPFIBP2, and DENND2D) were identified as biomarkers of invasiveness; two of these (PPFIBP2 and DENND2D) were closely associated with prognosis. The risk score was regarded as an independent prognostic factor. The nomogram was associated with acceptable accuracy for predicting 1- and 5-year overall survival. The infiltrating levels of resting NK cells, activated natural killer (NK) cells, CD8⁺ T cells, activated memory CD4⁺ T cells, and T follicular helper cells, were significantly higher in the group with lower risk scores. The group with higher risk scores showed predominant infiltration by regulatory T cells (Tregs). Conclusion: We successfully identified three signature genes related to invasiveness and constructed a nomogram of bladder cancer with acceptable performance. Differences suggested by risk scores between groups of patients showing diverse patterns of immune cell infiltration may be beneficial for selecting therapeutic approaches and predicting prognosis.

SmulTCan: A Shiny application for multivariable survival analysis of TCGA data with gene sets

BACKGROUND

Survival analysis is widely used in cancer research, and although several methods exist in R, there is the need for a more interactive, flexible, yet comprehensive online tool to analyze gene sets using Cox proportional hazards (CPH) models. The web-based Shiny application (app) SmulTCan extends existing tools to multivariable CPH models of gene sets-as exemplified using the netrins and their receptors (netrins-receptors). It can be used to identify survival gene signatures (GSs) and select the best subsets of input gene, microRNA, methylation level, and copy number variation sets from the Cancer Genome Atlas (TCGA).

OBJECTIVES

To create a tool for CPH model building and best subset selection, using survival data from TCGA with input gene expression files from UCSC Xena. Furthermore, we aim to analyze the input TSV file of netrins-receptors in SmulTCan and discuss our findings.

METHODS

SmulTCan uses Shiny's reactivity with built-in R functions from packages for CPH model analysis and best subset selection including "survminer", "riskRegression", "rms", "glmnet", and "BeSS".

RESULTS

Results from the SmulTCan app with the netrins-receptors gene set indicated unique hazard ratio GSs in certain renal and neural cancers, while the best subsets for this gene set, obtained via the app, could differentiate between prognostic outcomes in these cancers.

AVAILABILITY

SmulTCan is available at http://konulabapps.bilkent.edu.tr:3838/SmulTCan/. The input file for netrins-receptors is available in the online version of this paper. TCGA dataset folders containing survival files are available through https://github.com/aozh7/SmulTCan/.

SUPPLEMENTARY INFORMATION

The supplementary information (SI) accompanies the online version of this article.

The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.

Identification and Complete Validation of Prognostic Gene Signatures for Human Papillomavirus-Associated Cancers: Integrated Approach Covering Different Anatomical Locations

Human papillomavirus (HPV) infects squamous epithelium and is a major cause of cervical cancer (CC) and a subset of head and neck cancers (HNC). Virus-induced tumorigenesis, molecular alterations, and related prognostic markers are expected to be similar between the two cancers, but they remain poorly understood. We present integrated molecular analysis of HPV-associated tumors from TCGA and GEO databases and identify prognostic biomarkers. Analysis of gene expression profiles identified common upregulated genes and pathways of DNA replication and repair in the HPV-associated tumors. We established 34 prognostic gene signatures with a universal cutoff value in TCGA-CC using Elastic Net Cox regression analysis. We were able to externally validate our results in the TCGA-HNC and several GEO data sets, and demonstrated prognostic power in HPV-associated HNC, but not in HPV-negative cancers. The HPV-related prognostic and predictive indicator did not discriminate other cancers, except bladder urothelial carcinoma. These results identify and completely validate a highly selective prognostic system and its cross-usefulness in HPV-associated cancers, regardless of the tumor's anatomical subsite.IMPORTANCE Persistent infection with high-risk HPV interferes with cell function regulation and causes cell mutations, which accumulate over the long term and eventually develop into cancer. Results of pathway enrichment analysis presumably showed this accumulation of intracellular damage during the chronic HPV-infected state. We used highly advanced statistical methods to identify the most appropriate genes and coefficients and developed the HPV-related prognostic and predictive indicator (HPPI) risk scoring system. We applied the same cutoff value to training and validation sets and demonstrated good prognostic performance in both data sets, and confirmed a consistent trend in external validation. Moreover, HPPI presented significant validation results for bladder cancer suspected to be related to HPV. This suggested that our risk scoring system based on the prognostic gene signature could play an important role in the development of treatment strategies for patients with HPV-related cancer.

A novel prognostic nomogram for patients with surgically resected perihilar cholangiocarcinoma: a SEER-based study

Background

This study aimed to compare the predictive efficacy of four different lymph node (LN) staging systems on the overall survival (OS) of patients with surgically resected perihilar cholangiocarcinoma (pCCA), and construct a novel prognostic nomogram to predict OS in pCCA patients.

Methods

Patients with pCCA that underwent surgical resection between 2004 to 2016 were selected from the Surveillance, Epidemiology, and End Results (SEER) database (n=1,173). Patients were randomly divided into a modeling cohort and an internal verification cohort. To compare the prognostic efficacy of four different N staging systems [American Joint Committee on Cancer (AJCC) 7^th and 8^th edition N stages, lymph node ratio (LNR), and log odds of positive lymph nodes (LODDS)], we used three different evaluation methods: Harrell's index of concordance (C-index), Akaike information criterion (AIC), and area under the receiver operating characteristic (ROC) curve (AUC). Multivariate analysis was used to identify independent prognostic factors and validate LODDS in the modeling cohort. A nomogram was then constructed to predict 1-, 3-, and 5-year survival. The nomogram was validated using Harrell's C-indexes and calibration curves.

Results

Of the four different N staging methods, LODDS was considered to be the most effective LN staging system for evaluating the prognosis of patients with surgically resected pCCA, according to the values calculated for C-index, AUC, and AIC. After validation by C-indexes and calibration curves, the constructed nomogram accurately predicted the OS of pCCA patients.

Conclusions

For patients with surgically resected pCCA, LODDS was found to be the most accurate N staging system. The novel LODDS-based nomogram constructed in this study provides an accurate method for predicting patient survival in pCCA.

A robust 11-genes prognostic model can predict overall survival in bladder cancer patients based on five cohorts

Background

Bladder cancer is the tenth most common cancer globally, but existing biomarkers and prognostic models are limited.

Method

In this study, we used four bladder cancer cohorts from The Cancer Genome Atlas and Gene Expression Omnibus databases to perform univariate Cox regression analysis to identify common prognostic genes. We used the least absolute shrinkage and selection operator regression to construct a prognostic Cox model. Kaplan-Meier analysis, receiver operating characteristic curve, and univariate/multivariate Cox analysis were used to evaluate the prognostic model. Finally, a co-expression network, CIBERSORT, and ESTIMATE algorithm were used to explore the mechanism related to the model.

Results

A total of 11 genes were identified from the four cohorts to construct the prognostic model, including eight risk genes (SERPINE2, PRR11, DSEL, DNM1, COMP, ELOVL4, RTKN, and MAPK12) and three protective genes (FABP6, C16orf74, and TNK1). The 11-genes model could stratify the risk of patients in all five cohorts, and the prognosis was worse in the group with a high-risk score. The area under the curve values of the five cohorts in the first year are all greater than 0.65. Furthermore, this model's predictive ability is stronger than that of age, gender, grade, and T stage. Through the weighted co-expression network analysis, the gene module related to the model was found, and the key genes in this module were mainly enriched in the tumor microenvironment. B cell memory showed low infiltration in high-risk patients. Furthermore, in the case of low B cell memory infiltration and high-risk score, the prognosis of the patients was the worst.

Conclusion

The proposed 11-genes model is a promising biomarker for estimating overall survival in bladder cancer. This model can be used to stratify the risk of bladder cancer patients, which is beneficial to the realization of individualized treatment.

Identification of GSN and LAMC2 as Key Prognostic Genes of Bladder Cancer by Integrated Bioinformatics Analysis

Bladder cancer is a common malignancy with mechanisms of pathogenesis and progression. This study aimed to identify the prognostic hub genes, which are the central modulators to regulate the progression and proliferation in the specific subtype of bladder cancer. The identification of the candidate hub gene was performed by weighted gene co-expression network analysis to construct a free-scale gene co-expression network. The gene expression profile of GSE97768 from the Gene Expression Omnibus database was used. The association between prognosis and hub gene was evaluated by The Cancer Genome Atlas database. Four gene-expression modules were significantly related to bladder cancer disease: the red module (human adenocarcinoma lymph node metastasis), the darkturquioise module (grade 2 carcinoma), the lightgreen module (grade 3 carcinoma), and the royalblue module (transitional cell carcinoma lymphatic metastasis). Based on betweenness centrality and survival analysis, we identified laminin subunit gamma-2 (LAMC2) in the grade 2 carcinoma, gelsolin (GSN) in the grade 3 carcinoma, and homeodomain-interacting protein kinase 2 (HIPK2) in the transitional cell carcinoma lymphatic metastasis. Subsequently, the protein levels of LAMC2 and GSN were respectively down-regulated and up-regulated in tumor tissue with the Human Protein Atlas (HPA) database. Our results suggested that LAMC2 and GSN are the central modulators to transfer information in the specific subtype of the disease.