A seven-gene signature model predicts overall survival in kidney renal clear cell carcinoma

A deep audit of the PeptideAtlas database uncovers evidence for unannotated coding genes and aberrant translation

The human genome has been the subject of intense scrutiny by experimental and manual curation projects for more than two decades. Novel coding genes have been proposed from large-scale RNASeq, ribosome profiling and proteomics experiments. Here we carry out an in-depth analysis of an entire proteomics database. We analysed the proteins, peptides and spectra housed in the human build of the PeptideAtlas proteomics database to identify coding regions that are not yet annotated in the GENCODE reference gene set. We find support for hundreds of missing alternative protein isoforms and unannotated upstream translations, and evidence of cross-contamination from other species. There was reliable peptide evidence for 34 novel unannotated open reading frames (ORFs) in PeptideAtlas. We find that almost half belong to coding genes that are missing from GENCODE and other reference sets. Most of the remaining ORFs were not conserved beyond human, however, and their peptide confirmation was restricted to cancer cell lines. We show that this is strong evidence for aberrant translation, raising important questions about the extent of aberrant translation and how these ORFs should be annotated in reference genomes.

A novel brown adipocytes-related gene signature predicts and validates prognosis and immune infiltration of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer. The crosstalk between tumor tissue and adjacent adipose tissue has been appreciated recently. This study examines the predictive usefulness of brown adipocyte-related genes (BARGs) in ccRCC.

METHODS

The transcriptome and clinical data of ccRCC patients were obtained from TCGA-KIRC and USA-ccRCC cohorts (848 tumor samples; 72 normal samples). Lasso-Cox methods were used to construct the risk prognostic signature model. We used Kaplan-Meier survival analysis to evaluate the prognostic significance of the risk model with ROC curves ascertaining prediction accuracy. The differences in immune cell infiltrates and signature risk scores between different risk categories were analyzed. Finally, biological experiments were performed to explore the functions of candidate genes.

RESULTS

TCGA-KIRC patients were classified into two clusters that differed significantly regarding overall survival (OS) and tumor microenvironment. After screening BARGs candidates, a signature consisting of PPP1R1A, DPYSL3, and PTPRM was created to calculate risk score. Patients were assigned to the high or low-risk group, and the high-risk group had a significantly worse prognosis. Consistent trend was validated in external USA-ccRCC cohort. Meanwhile, the signature risk score affected immune cell infiltrates within the ccRCC microenvironment, positively correlated with the infiltration of CD4+ T cells, CD8+ T cells, CD56dim, CD56bright NK cells, MDSCs, and macrophage cells, while negatively correlated with neutrophil, iDCs, mast cells, and eosinophil. Finally, knockdown of PPP1R1A and DPYSL3 in renal cancer cells showed impairment in tumor proliferation ability of ccRCC in vitro and in vivo. Conversely, knockdown of PTPRM exhibited a promotive effect.

CONCLUSION

We developed a predictive BARGs-related risk signature for early diagnosis and classifying ccRCC patients, which offers potential targets for individualized treatment of ccRCC.

Artificial Intelligence in Pathomics and Genomics of Renal Cell Carcinoma

The integration of artificial intelligence (AI) with histopathology images and gene expression patterns has led to the emergence of the dynamic fields of pathomics and genomics. These fields have revolutionized renal cell carcinoma (RCC) diagnosis and subtyping and improved survival prediction models. Machine learning has identified unique gene patterns across RCC subtypes and grades, providing insights into RCC origins and potential treatments, as targeted therapies. The combination of pathomics and genomics using AI opens new avenues in RCC research, promising future breakthroughs and innovations that patients and physicians can anticipate.

Genes associated with inflammation for prognosis prediction for clear cell renal cell carcinoma: a multi-database analysis

Background

Clear cell renal cell carcinoma (ccRCC) is the largest subtype of kidney tumour, with inflammatory responses characterising all stages of the tumour. Establishing the relationship between the genes related to inflammatory responses and ccRCC may help the diagnosis and treatment of patients with ccRCC.

Methods

First, we obtained the data for this study from a public database. After differential analysis and Cox regression analysis, we obtained the genes for the establishment of a prognostic model for ccRCC. As we used data from multiple databases, we standardized all the data using the surrogate variable analysis (SVA) package to make the data from different sources comparable. Next, we used a least absolute shrinkage and selection operator (LASSO) regression to construct a prognostic model of genes related to inflammation. The data used for modelling and internal validation came from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) series (GSE29609) databases. ccRCC data from the International Cancer Genome Consortium (ICGC) database were used for external validation. Tumour data from the E-MTAB-1980 cohort were used for external validation. The GSE40453 and GSE53757 datasets were used to verify the differential expression of inflammation-related gene model signatures (IRGMS). The immunohistochemistry of IRGMS was queried through the Human Protein Atlas (HPA) database. After the adequate validation of the IRGM, we further explored its application by constructing nomograms, pathway enrichment analysis, immunocorrelation analysis, drug susceptibility analysis, and subtype identification.

Results

The IRGM can robustly predict the prognosis of samples from patients with ccRCC from different databases. The verification results show that nomogram can accurately predict the survival rate of patients. Pathway enrichment analysis showed that patients in the high-risk (HR) group were associated with a variety of tumorigenesis biological processes. Immune-related analysis and drug susceptibility analysis suggested that patients with higher IRGM scores had more treatment options.

Conclusions

The IRGMS can effectively predict the prognosis of ccRCC. Patients with higher IRGM scores may be better candidates for treatment with immune checkpoint inhibitors and have more chemotherapy options.

Current Landscape of Genomic Biomarkers in Clear Cell Renal Cell Carcinoma

CONTEXT

Dramatic gains in our understanding of the molecular biology of clear cell renal cell carcinoma (ccRCC) have created a foundation for clinical translation to improve patient care.

OBJECTIVE

To review and contextualize clinically impactful data surrounding genomic biomarkers in ccRCC.

EVIDENCE ACQUISITION

A systematic literature search was conducted focusing on genomic-based biomarkers with an emphasis on studies assessing clinical outcomes.

EVIDENCE SYNTHESIS

The advancement of tumor sequencing techniques has led to a rapid increase in the knowledge of the molecular underpinnings of ccRCC and with that the discovery of multiple candidate genomic biomarkers. These include somatic gene mutations such as VHL, PBRM1, SETD2, and BAP1; copy number variations; transcriptomic multigene signatures; and specific immune cell populations. Many of these biomarkers have been assessed for their association with survival and a smaller number as potential predictors of a response to systemic therapy. In this scoping review, we discuss many of these biomarkers in detail. Further studies are needed to continue to refine and validate these molecular tools for risk stratification, with the ultimate goal of improving clinical decision-making and patient outcomes.

CONCLUSIONS

While no tissue or blood-based biomarkers for ccRCC have been incorporated into routine clinical practice to date, the field continues to expand rapidly. There remains a critical need to develop and validate these tools in order to improve the care for patients with kidney cancer.

PATIENT SUMMARY

Genomic biomarkers have the potential to better predict outcome and select the most appropriate treatment for patients with kidney cancer; however, further research is needed before any of these currently developed biomarkers are adopted into clinical practice.

Telomere-related gene risk model for prognosis and drug treatment efficiency prediction in kidney cancer

Kidney cancer is one of the most common urological cancers worldwide, and kidney renal clear cell cancer (KIRC) is the major histologic subtype. Our previous study found that von-Hippel Lindau (VHL) gene mutation, the dominant reason for sporadic KIRC and hereditary kidney cancer-VHL syndrome, could affect VHL disease-related cancers development by inducing telomere shortening. However, the prognosis role of telomere-related genes in kidney cancer has not been well discussed. In this study, we obtained the telomere-related genes (TRGs) from TelNet. We obtained the clinical information and TRGs expression status of kidney cancer patients in The Cancer Genome Atlas (TCGA) database, The International Cancer Genome Consortium (ICGC) database, and the Clinical Proteomic Tumor Analysis Consortium (CPTAC) database. Totally 353 TRGs were differential between tumor and normal tissues in the TCGA-KIRC dataset. The total TCGA cohort was divided into discovery and validation TCGA cohorts and then using univariate cox regression, lasso regression, and multivariate cox regression method to conduct data analysis sequentially, ten TRGs (ISG15, RFC2, TRIM15, NEK6, PRKCQ, ATP1A1, ELOVL3, TUBB2B, PLCL1, NR1H3) risk model had been constructed finally. The kidney patients in the high TRGs risk group represented a worse outcome in the discovery TCGA cohort (p<0.001), and the result was validated by these four cohorts (validation TCGA cohort, total TCGA cohort, ICGC cohort, and CPTAC cohort). In addition, the TRGs risk score is an independent risk factor for kidney cancer in all these five cohorts. And the high TRGs risk group correlated with worse immune subtypes and higher tumor mutation burden in cancer tissues. In addition, the high TRGs risk group might benefit from receiving immune checkpoint inhibitors and targeted therapy agents. Moreover, the proteins NEK6, RF2, and ISG15 were upregulated in tumors both at the RNA and protein levels, while PLCL1 and PRKCQ were downregulated. The other five genes may display the contrary expression status at the RNA and protein levels. In conclusion, we have constructed a telomere-related genes risk model for predicting the outcomes of kidney cancer patients, and the model may be helpful in selecting treatment agents for kidney cancer patients.

Exploring prognostic value and regulation network of PPP1R1A in hepatocellular carcinoma

Novel and accurate biomarkers are needed for early detection and progression evaluation of hepatocellular carcinoma (HCC). Protein phosphatase 1 regulatory subunit 1A (PPP1R1A) has been studied in cancer biology; however, the expression pattern and biological function of PPP1R1A in HCC are unclear. The differentially expressed genes (DEGs) in HCC were screened by The Cancer Genome Atlas (TCGA) database. Real-time PCR and immunohistochemistry (IHC) assay were used to detect the expression of PPP1R1A in BALB/c mice, human normal tissues and corresponding tumor tissues, especially HCC. Then, Kaplan-Meier analysis of patients with HCC was performed to evaluate the relationship between PPP1R1A expression and prognosis. The transcriptional regulatory network of PPP1R1A was constructed based on the differentially expressed mRNAs, microRNAs and transcription factors (TFs). To explore the downstream regulation of PPP1R1A, the Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis and immune infiltration score were performed. A total of 4 DEGs were screened out. PPP1R1A was differentially distributed and expressed in BALB/c mice and human tissues. PPP1R1A expression was higher in normal tissues than that in tumor tissues, and patients with higher PPP1R1A expression had better clinical outcome in HCC. In addition, we constructed miR-21-3p/TAL1/PPP1R1A transcriptional network. Furthermore, PPP1R1A may modulate the activation of PI3K-Akt pathway, cell cycle, glycogen metabolism and the recruitment of M2 macrophage in HCC. This study may help to clarify the function and mechanism of PPP1R1A in HCC and provide a potential biomarker for tumor prevention and treatment.

Identification of G6PC as a potential prognostic biomarker in hepatocellular carcinoma based on bioinformatics analysis

Hepatocellular carcinoma (HCC) has high mortality and incidence rates around the world with limited therapeutic options. There is an urgent need for identification of novel therapeutic targets and biomarkers for early diagnosis and predicting patient survival with HCC. Several studies (GSE102083, GSE29722, GSE101685, and GSE112790) from the GEO database in HCC were screened and analyzed by GEO2R, gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis were conducted with the Database for Annotation, Visualization and Integrated Discovery. The protein-protein interaction network was plotted and the module analysis was performed using Search Tool for the Retrieval of Inter-acting Genes/Proteins database and Cytoscape. The expression and survival of key genes were identified using UALCAN, Kaplan-Meier Plotter and ONCOMINE online databases, and the immune infiltration level of key genes was analyzed via the Tumor Immune Estimation Resource (TIMER) database. Through database analysis, eight key genes were finally screened out, and the expressions of cyclin-dependent kinase regulatory subunit 2 and glucose-6-phosphatase catalytic (G6PC), which were closely related to the survival of HCC patients, was detected by using UALCAN. Further analysis on the differential expression of G6PC in multiple cancerous tumors and normal tissues revealed low expression in many solid tumors by Oncomine and TIMER. In addition, Kaplan-Meier plotter and UALCAN database analysis to access diseases prognosis suggested that low expression of G6PC was significantly associated with poor overall survival in HCC patients. Finally, TIMER database analysis showed a significant negative correlation between G6PC and infiltration levels of six kinds of immune cells. The somatic copy number alterations of G6PC were associated with B cells, CD8+ T cells, CD4+ T cells, macrophages, dentritic cells and neutrophils. These bioinformatic data identified G6PC as a potential key gene in the diagnosis and prognosis of HCC.

Identification of epigenetic dysregulation gene markers and immune landscape in kidney renal clear cell carcinoma by comprehensive genomic analysis

Kidney cancer is one the most lethal cancers of the urinary system, but current treatments are limited and its prognosis is poor. This study focused on kidney renal clear cell carcinoma (KIRC) and analyzed the relationship between epigenetic alterations and KIRC prognosis, and explored the prognostic significance of these findings in KIRC patients. Based on multi-omics data, differentially expressed histone-modified genes were identified using the R package limma package. Gene enhancers were detected from data in the FANTOM5 database. Gene promoters were screened using the R package ChIPseeker, and the Bumphunter in the R package CHAMP was applied to screen differentially methylated regions (DMR). Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis and Gene Ontology (GO) functional enrichment analysis of genes was performed using the R package clusterProfiler. We identified 51 dysregulated epigenetic protein coding genes (epi-PCGs) from 872 epi-PCGs, and categorized three molecular subtypes (C1, C2, and C3) of KIRC samples with significantly different prognosis. Notably, among the three molecular subtypes, we found a markedly differential immune features in immune checkpoints, cytokines, immune signatures, and immune cell distribution. C2 subtype had significantly lower enrichment score of IFNγ, cytotoxic score (CYT), and angiogenesis. In addition, an 8-gene signature containing 8 epi-PCGs (ETV4, SH2B3, FATE1, GRK5, MALL, HRH2, SEMA3G, and SLC10A6) was developed for predicting KIRC prognosis. Prognosis of patients with a high 8-gene signature score was significantly worse than those with a low 8-gene signature score, which was also validated by the independent validation data. The 8-gene signature had a better performance compared with previous signatures of KIRC. Overall, this study highlighted the important role of epigenetic regulation in KIRC development, and explored prognostic epi-PCGs, which may provide a guidance for exploiting further pathological mechanisms of KIRC and for developing novel drug targets.

Tubulin Isotypes: Emerging Roles in Defining Cancer Stem Cell Niche

Although the role of microtubule dynamics in cancer progression is well-established, the roles of tubulin isotypes, their cargos and their specific function in the induction and sustenance of cancer stem cells (CSCs) were poorly explored. But emerging reports urge to focus on the transport function of tubulin isotypes in defining orchestrated expression of functionally critical molecules in establishing a stem cell niche, which is the key for CSC regulation. In this review, we summarize the role of specific tubulin isotypes in the transport of functional molecules that regulate metabolic reprogramming, which leads to the induction of CSCs and immune evasion. Recently, the surface expression of GLUT1 and GRP78 as well as voltage-dependent anion channel (VDAC) permeability, regulated by specific isotypes of β-tubulins have been shown to impart CSC properties to cancer cells, by implementing a metabolic reprogramming. Moreover, βIVb tubulin is shown to be critical in modulating EphrinB1signaling to sustain CSCs in oral carcinoma. These tubulin-interacting molecules, Ephrins, GLUT1 and GRP78, are also important regulators of immune evasion, by evoking PD-L1 mediated T-cell suppression. Thus, the recent advances in the field implicate that tubulins play a role in the controlled transport of molecules involved in CSC niche. The indication of tubulin isotypes in the regulation of CSCs offers a strategy to specifically target those tubulin isotypes to eliminate CSCs, rather than the general inhibition of microtubules, which usually leads to therapy resistance.

A Novel Machine Learning 13-Gene Signature: Improving Risk Analysis and Survival Prediction for Clear Cell Renal Cell Carcinoma Patients

Simple Summary

Clear cell renal cell carcinoma is a type of kidney cancer which comprises the majority of all renal cell carcinomas. Many efforts have been made to identify biomarkers which could help healthcare professionals better treat this kind of cancer. With extensive public data available, we conducted a machine learning study to determine a gene signature that could indicate patient survival with high accuracy. Through the min-Redundancy and Max-Relevance algorithm we generated a signature of 13 genes highly correlated with patient outcomes. These findings reveal potential strategies for personalized medicine in the clinical practice.

Abstract

Patients with clear cell renal cell carcinoma (ccRCC) have poor survival outcomes, especially if it has metastasized. It is of paramount importance to identify biomarkers in genomic data that could help predict the aggressiveness of ccRCC and its resistance to drugs. Thus, we conducted a study with the aims of evaluating gene signatures and proposing a novel one with higher predictive power and generalization in comparison to the former signatures. Using ccRCC cohorts of the Cancer Genome Atlas (TCGA-KIRC) and International Cancer Genome Consortium (ICGC-RECA), we evaluated linear survival models of Cox regression with 14 signatures and six methods of feature selection, and performed functional analysis and differential gene expression approaches. In this study, we established a 13-gene signature (AR, AL353637.1, DPP6, FOXJ1, GNB3, HHLA2, IL4, LIMCH1, LINC01732, OTX1, SAA1, SEMA3G, ZIC2) whose expression levels are able to predict distinct outcomes of patients with ccRCC. Moreover, we performed a comparison between our signature and others from the literature. The best-performing gene signature was achieved using the ensemble method Min-Redundancy and Max-Relevance (mRMR). This signature comprises unique features in comparison to the others, such as generalization through different cohorts and being functionally enriched in significant pathways: Urothelial Carcinoma, Chronic Kidney disease, and Transitional cell carcinoma, Nephrolithiasis. From the 13 genes in our signature, eight are known to be correlated with ccRCC patient survival and four are immune-related. Our model showed a performance of 0.82 using the Receiver Operator Characteristic (ROC) Area Under Curve (AUC) metric and it generalized well between the cohorts. Our findings revealed two clusters of genes with high expression (SAA1, OTX1, ZIC2, LINC01732, GNB3 and IL4) and low expression (AL353637.1, AR, HHLA2, LIMCH1, SEMA3G, DPP6, and FOXJ1) which are both correlated with poor prognosis. This signature can potentially be used in clinical practice to support patient treatment care and follow-up.

A Mitochondrial Dysfunction and Oxidative Stress Pathway-Based Prognostic Signature for Clear Cell Renal Cell Carcinoma

Mitochondria not only are the main source of ATP synthesis but also regulate cellular redox balance and calcium homeostasis. Its dysfunction can lead to a variety of diseases and promote cancer and metastasis. In this study, we aimed to explore the molecular characteristics and prognostic significance of mitochondrial genes (MTGs) related to oxidative stress in clear cell renal cell carcinoma (ccRCC). A total of 75 differentially expressed MTGs were analyzed from The Cancer Genome Atlas (TCGA) database, including 46 upregulated and 29 downregulated MTGs. Further analysis screened 6 prognostic-related MTGs (ACAD11, ACADSB, BID, PYCR1, SLC25A27, and STAR) and was used to develop a signature. Kaplan-Meier survival and receiver operating characteristic (ROC) curve analyses showed that the signature could accurately distinguish patients with poor prognosis and had good individual risk stratification and prognostic potential. Stratified analysis based on different clinical variables indicated that the signature could be used to evaluate tumor progression in ccRCC. Moreover, we found that there were significant differences in immune cell infiltration between the low- and high-risk groups based on the signature and that ccRCC patients in the low-risk group responded better to immunotherapy than those in the high-risk group (46.59% vs 35.34%, P = 0.008). We also found that the expression levels of these prognostic MTGs were significantly associated with drug sensitivity in multiple ccRCC cell lines. Our study for the first time elucidates the biological function and prognostic significance of mitochondrial molecules associated with oxidative stress and provides a new protocol for evaluating treatment strategies targeting mitochondria in ccRCC patients.

Knockdown of ubiquitin-like modifier-activating enzyme 2 promotes apoptosis of clear cell renal cell carcinoma cells

Small ubiquitin-related modifier (SUMO) proteins are involved in the development of tumors. Ubiquitin-like modifier-activating enzyme 2 (UBA2) is an important member of the SUMO modification system; however, its role in clear cell renal cell carcinoma (ccRCC) is unclear. Therefore, we investigated the expression and function of UBA2 in ccRCC. Both mRNA and protein expression levels of UBA2 were found to be higher in ccRCC than in normal renal tissues and significantly related to the tumor size, Fuhrman grade, and tumor stage. UBA2 knockdown inhibited ccRCC cell growth, promoted apoptosis in vitro and in vivo, and decreased the abundance of a p53 mutant, c-Myc, and key enzymes of the SUMO modification system. Meanwhile, overexpression of UBA2 had the opposite effects. Overexpression of the p53 mutant or c-Myc alleviated the effects of UBA2 knockdown on ccRCC cell proliferation and apoptosis. In conclusion, targeting UBA2 may have a therapeutic potential against ccRCC.

Bioinformatics analysis reveals biomarkers with cancer stem cell characteristics in kidney renal clear cell carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is a renal cortical tumor. KIRC is the most common subtype of kidney cancer, accounting for 70%-80% of kidney cancer. Early identification of the risk of KIRC patients can facilitate more accurate clinical treatment, but there is a lack of effective prognostic markers. We aimed to identify new prognostic biomarkers for KIRC on the basis of the cancer stem cell (CSC) theory.

Methods

RNA-sequencing (RNA-seq) data and related clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Weighted gene co-expression network analysis (WGCNA) was used to identify significant modules and hub genes, and predictive hub genes were used to construct prognostic characteristics.

Results

The messenger RNA expression-based stemness index (mRNAsi) in tumor tissues of patients in the TCGA database is higher than that of the corresponding normal tissues. In addition, some clinical features and results are highly correlated with mRNAsi. WGCNA found that the green module is the most prominent module associated with mRNAsi; the genes in the green module are mainly concentration in Notch binding, endothelial cell development, Notch signaling pathway, and Rap 1 signaling pathway. A protein-protein interaction (PPI) network showed that the top 10 central genes were significantly associated with the transcriptional level. Moreover, the 10 hub genes were up-regulated in KIRC. Regarding survival analysis, the nomogram of the prognostic markers of the seven pivotal genes showed a higher predictive value. The classical receiver operating characteristic (ROC) curve analysis showed that risk score biomarkers had the highest accuracy and specificity with an area under the curve (AUC) value of 0.701.

Conclusions

mRNAsi-related genes may be good prognostic biomarkers for KIRC.

A zinc finger protein gene signature enables bladder cancer treatment stratification

Bladder cancer (BC) is a commonly occurring malignant tumor affecting the urinary tract. Zinc finger proteins (ZNFs) constitute the largest transcription factor family in the human genome and are therefore attractive biomarker candidates for BC prognosis. In this study, we profiled the expression of ZNFs in The Cancer Genome Atlas (TCGA) BC cohort and developed a novel prognostic signature based on 7 ZNF-coding genes. After external validation of the model in the GSE48276 dataset, we integrated the 7-ZNF-gene signature with patient clinicopathological data to construct a nomogram that forecasted 1-, 2-, and 3-year OS with good predictive accuracy. We then accessed The Genomics of Drug Sensitivity in Cancer database to predict the therapeutic drug responses of signature-defined high- and low-risk BC patients in the TCGA cohort. Greater sensitivity to chemotherapy was revealed in the low-risk group. Finally, we conducted gene set enrichment analysis of the signature genes and established, by applying the ESTIMATE algorithm, distinct correlations between the two risk groups and the presence of stromal and immune cell types in the tumor microenvironment. By allowing effective risk stratification of BC patients, our novel ZNF gene signature may enable tailoring more intensive treatment for high-risk patients.

Expression of lipid transport-associated genes in lipid-deprived cancer cells

Cancer cells are known to significantly alter their lipid profiles in response to changes in extracellular lipid availability. Recent studies have shown that in response to lipid deprivation, cancer cells display significant changes in their cellular lipid homeostasis. These changes have been linked to the modulation of de novo lipid synthesis pathways that are markedly altered under lipid-deprived growth conditions. However, the effects of such environment on intracellular lipid trafficking-that could also affect cellular lipid homeostasis-have not been widely investigated. The presented work studies the effect of lipid deprivation on expression of genes for lipid transport proteins (LTPs) in cancer cell lines.

Construction and Validation of a Prognostic Gene-Based Model for Overall Survival Prediction in Hepatocellular Carcinoma Using an Integrated Statistical and Bioinformatic Approach

Hepatocellular carcinoma (HCC) is one of the most common lethal cancers worldwide and is often related to late diagnosis and poor survival outcome. More evidence is demonstrating that gene-based prognostic models can be used to predict high-risk HCC patients. Therefore, our study aimed to construct a novel prognostic model for predicting the prognosis of HCC patients. We used multivariate Cox regression model with three hybrid penalties approach including least absolute shrinkage and selection operator (Lasso), adaptive lasso and elastic net algorithms for informative prognostic-related genes selection. Then, the best subset regression was used to identify the best prognostic gene signature. The prognostic gene-based risk score was constructed using the Cox coefficient of the prognostic gene signature. The model was evaluated by Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analyses. A novel four-gene signature associated with prognosis was identified and the risk score was constructed based on the four-gene signature. The risk score efficiently distinguished the patients into a high-risk group with poor prognosis. The time-dependent ROC analysis revealed that the risk model had a good performance with an area under the curve (AUC) of 0.780, 0.732, 0.733 in 1-, 2- and 3-year prognosis prediction in The Cancer Genome Atlas (TCGA) dataset. Moreover, the risk score revealed a high diagnostic performance to classify HCC from normal samples. The prognosis and diagnosis prediction performances of risk scores were verified in external validation datasets. Functional enrichment analysis of the four-gene signature and its co-expressed genes involved in the metabolic and cell cycle pathways was constructed. Overall, we developed a novel-gene-based prognostic model to predict high-risk HCC patients and we hope that our findings can provide promising insight to explore the role of the four-gene signature in HCC patients and aid risk classification.

Construction of a Novel Multigene Panel Potently Predicting Poor Prognosis in Patients with Clear Cell Renal Cell Carcinoma

Simple Summary

Clear cell renal cell carcinoma (ccRCC) is the predominant cause of kidney cancer death attributed to its prevalence (70%) and its nature being the most aggressive form of kidney cancer. Most ccRCC deaths are resulted from metastasis. It is essential to know which ccRCCs are at risk of metastasis and the development to lethal disease; however, our capacity for such analysis remains poor. To improve this diagnostic capacity, we have examined a comprehensive ccRCC dataset containing 512 patients and have produced a 9-gene signature. This signature is novel; all its 9 components genes are unknown to be related to ccRCC. Importantly, all 9 individual genes possess significant ability in diagnosis of ccRCC metastasis and fatality; the combination of these genes or this signature predicts deadly ccRCCs at an impressive efficiency. This research will open new avenues in ccRCC research and will have a major impact in reducing ccRCC-associated deaths.

Abstract

We observed associations of IQGAP1 downregulation with poor overall survival (OS) in clear cell renal cell carcinoma (ccRCC). Differentially expressed genes (DEGs, n = 611) were derived from ccRCCs with (n = 111) and without IQGAP1 (n = 397) reduction using the TCGA PanCancer Atlas ccRCC dataset. These DEGs exhibit downregulations of immune response and upregulations of DNA damage repair pathways. Through randomization of the TCGA dataset into a training and testing subpopulation, a 9-gene panel (SigIQGAP1NW) was derived; it predicts poor OS in training, testing, and the full population at a hazard ratio (HR) 2.718, p < 2 × 10⁻¹⁶, p = 1.08 × 10⁻⁵, and p < 2 × 10⁻¹⁶, respectively. SigIQGAP1NW independently associates with poor OS (HR 1.80, p = 2.85 × 10⁻⁶) after adjusting for a set of clinical features, and it discriminates ccRCC mortality at time-dependent AUC values of 70% at 13.8 months, 69%/31M, 69%/49M, and 75.3%/71M. All nine component genes of SigIQGAP1NW are novel to ccRCC. The inclusion of RECQL4 (a DNA helicase) in SigIQGAP1NW agrees with IQGAP1 DEGs enhancing DNA repair. THSD7A affects kidney function; its presence in SigIQGAP1NW is consistent with our observed THSD7A downregulation in ccRCC (n = 523) compared to non-tumor kidney tissues (n = 100). Collectively, we report a novel multigene panel that robustly predicts poor OS in ccRCC.