Identification of a three-miRNA signature as a novel potential prognostic biomarker in patients with clear cell renal cell carcinoma

Liquid Biopsy as a New Tool for Diagnosis and Monitoring in Renal Cell Carcinoma

Renal cell carcinoma (RCC) presents a significant diagnostic challenge, particularly in small renal masses. The search for non-invasive screening methods and biomarkers has directed research toward liquid biopsy, which focuses on microRNAs (miRNAs), exosomes, and circulating tumor cells (CTCs). miRNAs are small non-coding RNA molecules that show considerable dysregulation in RCC, and they have potential for both diagnostic and prognostic applications. Research has highlighted their utility on biofluids, such as plasma, serum, and urine, in detecting RCC and characterizing its subtypes. Promising miRNA signatures have been associated with overall survival, suggesting their potential importance in the management of RCC. Exosomes, which carry a variety of molecular components, including miRNAs, are emerging as valuable biomarkers, whereas CTCs, released from primary tumors into the bloodstream, provide critical information on cancer progression. However, translation of these findings into clinical practice requires additional validation and standardization through large-scale studies and robust evidence. Although there are currently no approved diagnostic tests for RCC, the future potential of liquid biopsy in monitoring, treatment decision-making, and outcome prediction in patients with this disease is significant. This review examined and discussed recent developments in liquid biopsy for RCC, assessing both the strengths and limitations of these approaches for managing this disease.

Non-coding transcriptome profiles in clear-cell renal cell carcinoma

Clear-cell renal cell carcinoma (ccRCC) is a common urological malignancy with an increasing incidence. The development of molecular biomarkers that can predict the response to treatment and guide personalized therapy selection would substantially improve patient outcomes. Dysregulation of non-coding RNA (ncRNA) has been shown to have a role in the pathogenesis of ccRCC. Thus, an increasing number of studies are being carried out with a focus on the identification of ncRNA biomarkers in ccRCC tissue samples and the connection of these markers with patients' prognosis, pathological stage and grade (including metastatic potential), and therapy outcome. RNA sequencing analysis led to the identification of several ncRNA biomarkers that are dysregulated in ccRCC and might have a role in ccRCC development. These ncRNAs have the potential to be prognostic and predictive biomarkers for ccRCC, with prospective applications in personalized treatment selection. Research on ncRNA biomarkers in ccRCC is advancing, but clinical implementation remains preliminary owing to challenges in validation, standardization and reproducibility. Comprehensive studies and integration of ncRNAs into clinical trials are essential to accelerate the clinical use of these biomarkers.

Unlocking Precision Medicine: Liquid Biopsy Advancements in Renal Cancer Detection and Monitoring

Renal cell carcinoma (RCC) remains a formidable diagnostic challenge, especially in the context of small renal masses. The quest for non-invasive screening tools and biomarkers has steered research towards liquid biopsy, focusing on microRNAs (miRNAs), exosomes, and circulating tumor cells (CTCs). MiRNAs, small non-coding RNAs, exhibit notable dysregulation in RCC, offering promising avenues for diagnosis and prognosis. Studies underscore their potential across various biofluids, including plasma, serum, and urine, for RCC detection and subtype characterization. Encouraging miRNA signatures show correlations with overall survival, indicative of their future relevance in RCC management. Exosomes, with their diverse molecular cargo, including miRNAs, emerge as enticing biomarkers, while CTCs, emanating from primary tumors into the bloodstream, provide valuable insights into cancer progression. Despite these advancements, clinical translation necessitates further validation and standardization, encompassing larger-scale studies and robust evidence generation. Currently lacking approved diagnostic assays for renal cancer, the potential future applications of liquid biopsy in follow-up care, treatment selection, and outcome prediction in RCC patients are profound. This review aims to discuss and highlight recent advancements in liquid biopsy for RCC, exploring their strengths and weaknesses in the comprehensive management of this disease.

A novel microRNA panel exhibited significant potential in evaluating the progression of laryngeal squamous cell carcinoma

Background

Laryngeal squamous cell carcinoma (LSCC) is a common cancer of the head and neck in humans. The 5-years survival rate of patients with LSCC have declined in the past four decades. microRNAs (miRNAs) has been reported to be capable of predicting the prognosis outcomes of patients with different cancers. However, there are no reports on the usage of multi-miRNAs model as signature for the diagnosis or prognosis of LSCC.

Methods

To establish the miRNAs expression-associated model for diagnosis, prognosis prediction and aided therapy of patients with LSCC, the present study enrolled 107 patients with LSCC in clinic and obtained 117 LSCC samples data from TCGA database for evaluation, respectively. Next generation sequencing (NGS), raw data processing, the least absolute shrinkage and selection operator algorithm, Cox regression analysis, construction of nomogram and cell function assays (including proliferation, migration and invasion assays) were sequentially performed.

Results

There were massively dysregulated miRNAs in the LSCC compared to normal tissues. A six-miRNAs signature consists of miR-137-3p, miR-3934-5p, miR-1276, miR-129-5p, miR-7-5p and miR-105-5p was built for prognosis prediction of LSCC patients. The six-miRNAs signature is strongly associated with the poor overall survival (OS, p = 2.5e-05, HR: 4.30 [2.20-8.50]), progression free interval (PFI, p = 0.025, HR: 1.94 [1.08-3.46]) and disease specific survival (DSS, p = 1.1e-05, HR: 5.00 [2.50-10.00]). A nomogram for prediction of 2-, 3- and 5-years OS was also developed based on the six-miRNAs signature and clinical features. Furthermore, blocking the function of each of the six miRNAs inhibited proliferation, invasion and migration of LSCC cells.

Conclusions

The performance of six-miRNAs signature described in the current study demonstrated remarkable potential for progression assessment of LSCC. Moreover, the six-miRNAs signature may serve as predictive tool for prognosis and therapeutic targets of LSCC in clinic.

Development of a 7-miRNA prognostic signature for patients with bladder cancer

BACKGROUND

Bladder carcinoma (BC) represents one of the most prevalent malignant cancers, while predicting its clinical outcomes using traditional indicators is difficult. This study aimed to develop a miRNA signature for the prognostic prediction of patients with BC.

MATERIALS AND METHODS

MiRNAs that expressed differentially were identified between 413 BC and 19 non-tumor patients, whose prognostic values were evaluated using univariate and multivariate Cox regression analyses. The independent prognostic factors were screened out and were used to establish a signature. The risk score of the signature was calculated. Receiver operating characteristic (ROC) curves and Kaplan-Meier curves were used to verify the predictive performance of the miRNA signature and the risk score. A nomogram was constructed which integrated with the miRNA signature and clinical parameters. Experiments were performed.

RESULTS

7 prognosis related miRNAs were selected as independent risk factors, and a 7-miRNA signature was constructed, with an area under ROC (AUC) of 0.721. The 7-miRNA-signature based risk score acts as an independent prognostic factor, with satisfactory predictive performance (AUC = 0.744). Increased miR-337-3p expressions were detected in tumor samples and BC cell lines than in non-tumorigenic tissues and cell lines. Experiments suggested that miR-337-3p induces the proliferation, migration, and invasion of BC cells.

CONCLUSION

The constructed 7-miRNA signature is a promising biomarker for predicting the prognosis of patients with BC, and miR-337-3p may act as a candidate therapeutic target in BC treatments.

An Integrative Analysis Framework for Identifying the Prognostic Markers from Multidimensional RNA Data of Clear Cell Renal Cell Carcinoma

The altered regulatory status of long noncoding RNA (lncRNA), miRNA, and mRNA and their interactions play critical roles in tumor proliferation, metastasis, and progression, which ultimately influence cancer prognosis. However, there are limited studies of comprehensive identification of prognostic biomarkers from combined data sets of the three RNA types in the highly metastatic clear cell renal cell carcinoma (ccRCC). The current study employed an integrative analysis framework of functional genomics approaches and machine learning methods to the lncRNA, miRNA, and mRNA data and identified 16 RNAs (3 lncRNAs, 6 miRNAs, and 7 mRNAs) of prognostic value, with 9 of them novel. A 16 RNA-based score was established for prognosis prediction of ccRCC with significance (P < 0.0001). The area under the curve for the score model was 0.868 to 0.870 in the training cohort and 0.714 to 0.778 in the validation cohort. Construction of the lncRNA-miRNA-mRNA interaction network showed that the downstream mRNAs and upstream lncRNAs in the network initiated from the miRNA or lncRNA markers exhibit significant enrichment in functional classifications associated with cancer metastasis, proliferation, progression, or prognosis. The functional analysis provided clear support for the role of the RNA biomarkers in predicting cancer prognosis. This study provides promising biomarkers for predicting prognosis of ccRCC using multidimensional RNA data, and these findings are expected to facilitate potential clinical applications of the biomarkers.

A Four-MicroRNA Panel in Serum as a Potential Biomarker for Screening Renal Cell Carcinoma

Background: Renal cell carcinoma (RCC) has been a major health problem and is one of the most malignant tumors around the world. Serum microRNA (miRNA) profiles previously have been reported as non-invasive biomarkers in cancer screening. The aim of this study was to explore serum miRNAs as potential biomarkers for screening RCC. Methods: A three-phase study was conducted to explore serum miRNAs as potential biomarkers for screening RCC. In the screening phase, 12 candidate miRNAs related to RCC were selected for further study by the ENCORI database with 517 RCC patients and 71 NCs. A total of 220 participants [108 RCC patients and 112 normal controls (NCs)] were enrolled for training and validation. The dysregulated candidate miRNAs were further confirmed with 30 RCC patients and 30 NCs in the training phase and with 78 RCC patients and 82 NCs in the validation phase. Receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) were used for assessing the diagnostic value of miRNAs. Bioinformatic analysis and survival analysis were also included in our study. Results: Compared to NCs, six miRNAs (miR-18a-5p, miR-138-5p, miR-141-3p, miR-181b-5p, miR-200a-3p, and miR-363-3p) in serum were significantly dysregulated in RCC patients. A four-miRNA panel was built by combining these candidate miRNAs to improve the diagnostic value with AUC = 0.908. ABCG1 and RNASET2, considered potential target genes of the four-miRNA panel, may play a significant role in the development of RCC. Conclusion: A four-miRNA panel in serum was identified for RCC screening in our study. The four--miRNA panel has a great potential to be a non-invasive biomarker for RCC screening.

Upregulated Transcription Factor PITX1 Predicts Poor Prognosis in Kidney Renal Clear Cell Carcinoma-Based Bioinformatic Analysis and Experimental Verification

Paired-like homeodomain transcription factor 1 (PITX1) is involved in numerous biological processes, including cell growth, progression, and invasion in various malignant tumors. Nevertheless, the relationship between PITX1 and kidney renal clear cell carcinoma (KIRC) remains unclear. The clinical role and functions of PITX1 were analyzed by integrating multiple open-access online datasets. Further experimental verification was performed via quantitative real-time PCR (qRT-PCR) to detect the expression of PITX1 in 10 pairs of KIRC tissues. Our results revealed that PITX1 mRNA was overexpressed in tumor tissues compared with normal tissues in the TCGA-KIRC database (p < 0.001) and numerous independent cohorts (p < 0.05). Further, high expression of PITX1 mRNA was detected in KIRC tissues compared with adjacent normal tissues in our center by qRT-PCR (N = 10, p < 0.05). Logistic regression analysis demonstrated that the PITX1 level was positively associated with KIRC patients, T and M stages, histologic grade, and pathologic stage (all p < 0.05). Survival analysis showed that upregulation of PITX1 mRNA was associated with poor overall survival (OS), disease-free survival (DFS), and disease-specific survival (DSS) (all p < 0.05). Univariate/multivariate Cox hazard regression analysis revealed that PITX1 was an independent risk factor for OS in patients with KIRC (HR = 1.998, p = 0.003). Accordingly, the time-independent receiver operating characteristic (ROC) curve confirmed that PITX1 had good predictive efficacy for OS and DSS. Meanwhile, a prediction model constructed by nomogram was used to predict the OS of KIRC patients, and the calibration plot indicated this model shows high accuracy. We also revealed some downstream target genes of PITX1-related signaling pathways. Our finding suggested that high PITX1 mRNA expression may act as an independent predictive factor of poor prognosis in patients with KIRC. The prognostic model based on the nomogram would be instrumental in evaluating the survival rate in KIRC patients.

Investigation of VHL gene associated with miR-223 in clear cell renal cell carcinoma

BACKGROUND

Clear cell type renal cell carcinoma (ccRCC) is the most common renal cell carcinoma (RCC). In this study, we examined the expressions of VHL and miR-223 in ccRCC patients׳ tissues to investigate the possible role in the development of ccRCC.

METHODS AND RESULTS

This study collected five expression profiles (GSE36139, GSE3, GSE73731, GSE40435, and GSE26032) from Gene Omnibus Data. Expressions of VHL and miR-223 in paraffinized tumor and normal tissues of 100 Turkish patients' ccRCC tissues were determined by bioinformatic data mining and real-time quantitative polymerase chain reaction (qRT-PCR). The VHL gene was subjected to mutational analysis by DNA sequencing, and pVHL was analyzed using western blotting. Our study's t-test and Pearson correlation analysis showed that VHL gene expression in tumoral tissues with a - 0.39-fold decrease was not significantly lower than normal tissues (p = 0.441), and a 0.97-fold increase miR-223 (p = 0.045) was determined by real-time PCR. Also, as a result of DNA sequence analysis performed in the VHL gene, it was found that 26% of the patients have mutations. The mutations for (VHL):c.60C>A (p.Val20=) and (VHL):c.467delA (p.Tyr156Leu) was detected for the first time in Turkish patients.

CONCLUSIONS

The present study demonstrated that the differences in the expression levels of miR-223 have the potential to be biomarkers to determine the poor prognosis in ccRCC.

Construction of Immune-Associated Nomogram for Predicting the Recurrence Survival Risk of Stage I Cervical Cancer

Background

Various studies reported that the prognosis of patients with cervical cancer (CC) was significantly associated with immunity, whereas limited studies have explored whether immune-associated genes could be classifiers for recurrence-free survival (RFS) of stage I CC. Thus, an improved immune-related gene signature for stage I CC patients' prognosis is urgently required.

Materials and Methods

We retrospectively analyzed the gene expression profiles of stage I CC patients in the GSE44001 set from the Gene Expression Omnibus (GEO) database. The stage I CC patients were randomly divided into the training group and the internal validation group. The training patients were adopted to develop a prognostic immune gene-based signature; meanwhile, the internal validation patients were used to validate the power of the selected immune gene-related signature using univariate Cox proportional hazard analysis, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analysis. The accuracy and reliability of the immune gene-related signature were evaluated based on Kaplan-Meier analysis and time-dependent receiver operating characteristic (ROC) curves.

Results

High power of the 8-immune gene signature was found on the basis of ROC analysis (AUC at 1, 3, and 5 years were exhibited in the internal validation group (0.702, 0.715, and 0.728, respectively), external validation group (0.702, 0.825, and 0.842, respectively), and entire GEO dataset (0.840, 0.894, and 0.852, respectively)). Besides, C-index, ROC, calibration plots, and decision curve analysis (DCA) also acted well in our nomogram, suggestive of a high ability of the nomogram to elevate the prognostic prediction of stage I CC patients.

Conclusions

In this study, we successfully constructed an integrated 8-immune gene-based signature which could accurately identify patients with low prognostic risk from those with high prognostic risk. In addition, we developed an immune-related nomogram which can elevate the prognostic prediction of stage I CC patients.

Innovations in Biomaterial Design toward Successful RNA Interference Therapy for Cancer Treatment

Gene regulation using RNA interference (RNAi) therapy has been developed as one of the frontiers in cancer treatment. The ability to tailor the expression of genes by delivering synthetic oligonucleotides to tumor cells has transformed the way scientists think about treating cancer. However, its clinical application has been limited due to the need to deliver synthetic RNAi oligonucleotides efficiently and effectively to target cells. Advances in nanotechnology and biomaterials have begun to address the limitations to RNAi therapeutic delivery, increasing the likelihood of RNAi therapeutics for cancer treatment in clinical settings. Herein, innovations in the design of nanocarriers for the delivery of oligonucleotides for successful RNAi therapy are discussed.

MicroRNAs Targeting HIF-2α, VEGFR1 and/or VEGFR2 as Potential Predictive Biomarkers for VEGFR Tyrosine Kinase and HIF-2α Inhibitors in Metastatic Clear-Cell Renal Cell Carcinoma

Metastatic clear-cell renal cell carcinoma (m-ccRCC) is characterized by increased hypoxia-induced factor (HIF)-2α and vascular endothelial growth factor receptor (VEGFR)-dependent angiogenesis through loss of function of the von Hippel-Lindau protein. VEGFR tyrosine kinase inhibitors (VEGFR-TKIs) are a cornerstone of m-ccRCC treatment, and new treatments targeting HIF-2α are currently under investigation. However, predictive biomarkers for these treatments are lacking. In this retrospective cohort study including 109 patients treated with VEGFR-targeted therapies as first-line treatment, we aimed to study the possible predictive function of microRNAs (miRNAs) targeting HIF-2α, VEGFR1 and VEGFR2. We selected miRNAs inversely correlated with HIF-2α, VEGFR1 and/or VEGFR2 expression and with predicted target sites in the respective genes and subsequently studied their impact on therapeutic outcomes. We identified four miRNAs (miR-34c-5p, miR-221-3p, miR-222-3p and miR-3529-3p) inversely correlated with VEGFR1 and/or VEGFR2 expression and associated with tumor shrinkage and progression-free survival (PFS) upon treatment with VEGFR-TKIs, highlighting the potential predictive value of these miRNAs. Moreover, we identified three miRNAs (miR-185-5p, miR-223-3p and miR-3529-3p) inversely correlated with HIF-2α expression and associated with tumor shrinkage and PFS upon treatment with VEGFR-TKIs. These three miRNAs can have a predictive value not only upon treatment with VEGFR-TKIs but possibly also upon treatment with the upcoming HIF-2α inhibitor belzutifan.

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

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

A Review of Recent Research on the Role of MicroRNAs in Renal Cancer

Renal cell carcinoma (RCC) is a most common type of urologic neoplasms; it accounts for 3% of malignant tumors, with high rates of relapse and mortality. The most common types of renal cancer are clear cell carcinoma (ccRCC), papillary renal cell carcinoma (pRCC), and chromophobe renal carcinoma (chRCC), which account for 90%, 6–15%, and 2–5%, respectively, of all renal malignancies. Although surgical resection, chemotherapy, and radiotherapy are the most common treatment method for those diseases, their effects remain dissatisfactory. Furthermore, recent research shows that the treatment efficacy of checkpoint inhibitors in advanced RCC patients is widely variable. Hence, patients urgently need a new molecular biomarker for early diagnosis and evaluating the prognosis of RCC. MicroRNAs (miRNAs) belong to a family of short, non-coding RNAs that are highly conserved, have long half-life evolution, and post-transcriptionally regulate gene expression; they have been predicted to play crucial roles in tumor metastasis, invasion, angiogenesis, proliferation, apoptosis, epithelial-mesenchymal transition, differentiation, metabolism, cancer occurrence, and treatment resistance. Although some previous papers demonstrated that miRNAs play vital roles in renal cancer, such as pathogenesis, diagnosis, and prognosis, the roles of miRNAs in kidney cancer are still unclear. Therefore, we reviewed studies indexed in PubMed from 2017 to 2020, and found several studies suggesting that there are more than 82 miRNAs involved in renal cancers. The present review describes the current status of miRNAs in RCC and their roles in progression, diagnosis, therapy targeting, and prognosis of RCC.

Utility of a metabolic-associated nomogram to predict the recurrence-free survival of stage I cervical cancer

Aims: To identify metabolism-associated genes (MAGs) that serve as biomarkers to predict prognosis associated with recurrence-free survival (RFS) for stage I cervical cancer (CC). Patients & methods: By analyzing the Gene Expression Omnibus (GEO) database for 258 cases of stage I CC via univariate Cox analysis, LASSO and multivariate Cox regression analysis, we unveiled 11 MAGs as a signature that was also validated using Kaplan-Meier and receiver operating characteristic analyses. In addition, a metabolism-related nomogram was developed. Results: High accuracy of this signature for prediction was observed (area under the curve at 1, 3 and 5 years was 0.964, 0.929 and 0.852 for the internal dataset and 0.759, 0.719 and 0.757 for the external dataset). The high-risk score group displayed markedly worse RFS than did the low-risk score group. The indicators performed well in our nomogram. Conclusions: We identified a novel signature as a biomarker for predicting prognosis and a nomogram to facilitate the individual management of stage I CC patients.

A Biomarker Panel of Radiation-Upregulated miRNA as Signature for Ionizing Radiation Exposure

Ionizing radiation causes serious injury to the human body and has long-time impacts on health. It is important to find optimal biomarkers for the early quick screening of exposed individuals. A series of miRNAs signatures have been developed as the new biomarkers for diagnosis, survival, and prognostic prediction of cancers. Here, we have identified the ionizing radiation-inducible miRNAs profile through microarray analysis. The biological functions were predicted for the top six upregulated miRNAs by 4 Gy γ-rays: miR-1246, miR-1307-3p, miR-3197, miR-4267, miR-5096 and miR-7641. The miRNA-gene network and target gene-pathway network analyses revealed that DNAH3 is the target gene associated with all the six miRNAs. GOLGB1 is related to 4 miRNAs and other 26 genes targeted by 3 miRNAs. The upregulation of fifteen miRNAs were further verified at 4 h and 24 h after 0 to 10 Gy irradiation in the human lymphoblastoid AHH-1 cells, and some demonstrated a dose-dependent increased. Six miRNAs, including miR-145, miR-663, miR-1273g-3p, miR-6090, miR-6727-5p and miR-7641, were validated to be dose-dependently upregulated at 4 h or 24 h post-irradiation in both AHH-1 and human peripheral blood lymphocytes irradiated ex vivo. This six-miRNA signature displays the superiority as a radiation biomarker for the translational application of screening and assessment of radiation exposed individuals.

Epigenetic signature predicts overall survival clear cell renal cell carcinoma

BACKGROUND

Recently, increasing study have found that DNA methylation plays an important role in tumor, including clear cell renal cell carcinoma (ccRCC).

METHODS

We used the DNA methylation dataset of The Cancer Genome Atlas (TCGA) database to construct a 31-CpG-based signature which could accurately predict the overall survival of ccRCC. Meanwhile, we constructed a nomogram to predict the prognosis of patients with ccRCC.

RESULT

Through LASSO Cox regression analysis, we obtained the 31-CpG-based epigenetic signature which were significantly related to the prognosis of ccRCC. According to the epigenetic signature, patients were divided into two groups with high and low risk, and the predictive value of the epigenetic signature was verified by other two sets. In the training set, hazard ratio (HR) = 13.0, 95% confidence interval (CI) 8.0-21.2, P < 0.0001; testing set: HR = 4.1, CI 2.2-7.7, P < 0.0001; entire set: HR = 7.2, CI 4.9-10.6, P < 0.0001, Moreover, combined with clinical indicators, the prediction of 5-year survival of ccRCC reached an AUC of 0.871.

CONCLUSIONS

Our study constructed a 31-CpG-based epigenetic signature that could accurately predicted overall survival of ccRCC and staging progression of ccRCC. At the same time, we constructed a nomogram, which may facilitate the prediction of prognosis for patients with ccRCC.

Identification of the miRNA-mRNA regulatory pathways and a miR-21-5p based nomogram model in clear cell renal cell carcinoma

Background

The purpose of this study was to determine the key microRNAs (miRNAs) and their regulatory networks in clear cell renal cell carcinoma (ccRCC).

Methods

Five mRNA and three microRNA microarray datasets were downloaded from the Gene Expression Omnibus database and used to screen the differentially expressed miRNAs (DEMs) and differentially expressed genes (DEGs). Gene ontology enrichment analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis were performed with Metascape. A miRNA-mRNA network was mapped with the Cytoscape tool. The results were validated with data from The Cancer Genome Atlas (TCGA) and qRT-PCR. A nomogram model based on independent prognostic key DEMs, stage and grade was constructed for further investigation.

Results

A total of 26 key DEMs and 307 DEGs were identified. Dysregulation of four key DEMs (miR-21-5p, miR-142-3p, miR-155-5p and miR-342-5p) was identified to correlate with overall survival. The results were validated with TCGA data and qRT-PCR. The nomogram model showed high accuracy in predicting the prognosis of patients with ccRCC.

Conclusion

We identified 26 DEMs that may play vital roles in the regulatory networks of ccRCC. Four miRNAs (miR-21-5p, miR-142-3p, miR-155-5p and miR-342-5p) were considered as potential biomarkers in the prognosis of ccRCC, among which only miR-21-5p was found to be an independent prognostic factor. A nomogram model was then created on the basis of independent factors for better prediction of prognosis for patients with ccRCC. Our results suggest a need for further experimental validation studies.

Identification of a prognostic gene signature based on an immunogenomic landscape analysis of bladder cancer

Cancer immune plays a critical role in cancer progression. Tumour immunology and immunotherapy are one of the exciting areas in bladder cancer research. In this study, we aimed to develop an immune‐related gene signature to improve the prognostic prediction of bladder cancer. Firstly, we identified 392 differentially expressed immune‐related genes (IRGs) based on TCGA and ImmPort databases. Functional enrichment analysis revealed that these genes were enriched in inflammatory and immune‐related pathways, including in ‘regulation of signaling receptor activity’, ‘cytokine‐cytokine receptor interaction’ and ‘GPCR ligand binding’. Then, we separated all samples in TCGA data set into the training cohort and the testing cohort in a ratio of 3:1 randomly. Data set GSE13507 was set as the validation cohort. We constructed a prognostic six‐IRG signature with LASSO Cox regression in the training cohort, including AHNAK, OAS1, APOBEC3H, SCG2, CTSE and KIR2DS4. Six IRGs reflected the microenvironment of bladder cancer, especially immune cell infiltration. The prognostic value of six‐IRG signature was further validated in the testing cohort and the validation cohort. The results of multivariable Cox regression and subgroup analysis revealed that six‐IRG signature was a clinically independent prognostic factor for bladder cancer patients. Further, we constructed a nomogram based on six‐IRG signature and other clinicopathological risk factors, and it performed well in predict patients' survival. Finally, we found six‐IRG signature showed significant difference in different molecular subtypes of bladder cancer. In conclusions, our research provided a novel immune‐related gene signature to estimate prognosis for patients' survival with bladder cancer.

miR-758-3p Inhibits Proliferation, Migration, and Invasion of Clear Cell Renal Cell Carcinoma and Predicts Favorable Prognosis

Background

Although miR-758-3p has been reported to be associated with multiple cancers, including hepatocellular carcinoma, bladder cancer, gastric cancer and papillary thyroid cancer, its role in clear cell renal cell carcinoma (ccRCC) remains unclear.

Methods

The expression levels of miR-758-3p in ccRCC tissues and cell lines were examined using qRT-PCR. Survival analysis was performed using Kaplan–Meier, while the prognostic significance of miR-758-3p was evaluated by Cox regression analysis. The effects of miR-758-3p on cell proliferation, migration and invasion were analyzed with CCK-8, crystal violet and transwell assays. Luciferase reporter assays were performed to determine the effect of miR-758-3p on MDM2. Western blot was applied to measure the expression of MDM2.

Results

The expression levels of miR-758-3p were down-regulated in human ccRCC tissues and cell lines. Moreover, the expression of miR-758-3p was closely associated with histological grade, TNM stage and vascular invasion. High expression of miR-758-3p was found to be capable of predicting favorable clinical prognosis in ccRCC patients. Additionally, whilst the proliferation, migration and invasion of ccRCC cells were inhibited upon overexpression of miR-758-3p, the effects were reversed upon miR-758-3p knockdown. Moreover, miR-758-3p was a modulator of MDM2 in ccRCC.

Conclusion

This study demonstrated that miR-758-3p is a potential prognostic biomarker for ccRCC patients. Data from this study showed that miR-758-3p exhibits different biological functions that inhibit the progression of ccRCC cells, hence it can be a potential therapeutic target candidate for treating ccRCC.

Prognostic Value and Potential Biological Functions of CLDN8 in Patients with Clear Cell Renal Cell Carcinoma

Purpose

Clear cell renal cell carcinoma (ccRCC) is among the most common malignant tumors worldwide, with a high incidence rate and poor prognosis. Currently, there are no biomarkers that can accurately guide prognostic evaluation and therapeutic strategy for ccRCC. The prognostic value and potential biological function of claudin-8 (CLDN8), a critical component of tight junctions in ccRCC, remain unclear.

Methods

Sequencing data were obtained from The Cancer Genome Atlas, International Cancer Genome Consortium, and Gene Expression Omnibus databases. R packages were used to explore CLDN8 mRNA expression levels and analyze differentially expressed genes. Results were validated in clinical specimens and cell lines, and bioinformatics analyses were conducted to explore the potential biological functions of CLDN8. Finally, functional analyses were carried out using 786–O ccRCC cell line.

Results

Both CLDN8 mRNA and protein expression levels were significantly lower in ccRCC compared with the normal control tissues. Kaplan–Meier analyses showed that low CLDN8 expression levels were associated with the poor overall survival, while univariate and multivariate Cox regression indicated that CLDN8 could serve as an independent prognostic factor in patient with ccRCC. Bioinformatic and Western blot analyses showed that CLDN8 suppressed proliferation, migration, and invasion of 786–O ccRCC cells through the epithelial–mesenchymal transition and AKT pathways.

Conclusion

CLDN8 could serve as an independent prognostic factor in ccRCC, in which it suppresses 786–O proliferation, migration, and invasion through EMT and AKT pathways.

Identification of an 8-miRNA signature as a potential prognostic biomarker for glioma

Background

Glioma is the most common form of primary malignant intracranial tumor.

Methods

In the current study, miRNA matrix were obtained from the Chinese Glioma Genome Atlas (CGGA), and then univariate Cox regression analysis and Lasso regression analysis were utilized to select candidate miRNAs and multivariate Cox regression analysis was applied to establish a miRNA signature for predicting overall survival (OS) of glioma. The signature was assessed with the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and validated by data from Gene Expression Omnibus (GEO).

Results

Eight miRNAs (miR-1246, miR-148a, miR-150, miR-196a, miR-338-3p, miR-342-5p, miR-548h and miR-645) were included in the miRNA signature. The AUC of ROC analysis for 1- and 3-year OS in the CGGA dataset was 0.747 and 0.905, respectively. In the GEO dataset, The AUC for 1- and 3-year was 0.736 and 0.809, respectively. The AUC in both the CGGA and GEO datasets was similar to that based on WHO 2007 classification (0.736 and 0.799) and WHO 2016 classification (0.663 and 0.807). Additionally, Kaplan–Meier plot revealed that high-risk score patients had a poorer clinical outcome. Multivariate Cox regression analysis suggested that the miRNA signature was an independent prognosis-related factor [HR: 6.579, 95% CI [1.227−35.268], p = 0.028].

Conclusion

On the whole, in the present study, based on eight miRNAs, a novel prognostic signature was developed for predicting the 1- and 3- year survival rate in glioma. The results may be conducive to predict the precise prognosis of glioma and to elucidate the underlying molecular mechanisms. However, further experimental researches of miRNAs are needed to validate the findings of this study.

Identification of miRNA signatures for kidney renal clear cell carcinoma using the tensor-decomposition method

Cancer is a highly complex disease caused by multiple genetic factors. MicroRNA (miRNA) and mRNA expression profiles are useful for identifying prognostic biomarkers for cancer. Kidney renal clear cell carcinoma (KIRC), which accounts for more than 70% of all renal malignant tumour cases, was selected for our analysis. Traditional methods of identifying cancer prognostic markers may not be accurate. Tensor decomposition (TD) is a useful method uncovering the underlying low-dimensional structures in the tensor. The TD-based unsupervised feature extraction method was applied to analyse mRNA and miRNA expression profiles. Biological annotations of the prognostic miRNAs and mRNAs were examined utilizing the pathway and oncogenic signature databases DIANA-miRPath and MSigDB. TD identified the miRNA signatures and the associated genes. These genes were found to be involved in cancer-related pathways, and 23 genes were significantly correlated with the survival of KIRC patients. We demonstrated that the results are robust and not highly dependent upon the databases we selected. Compared with traditional supervised methods tested, TD achieves much better performance in selecting prognostic miRNAs and mRNAs. These results suggest that integrated analysis using the TD-based unsupervised feature extraction technique is an effective strategy for identifying prognostic signatures in cancer studies.

Development and validation of an immune prognostic classifier for clear cell renal cell carcinoma

BACKGROUND

Tumor-infiltrating immune cells are indispensable to the progression and prognosis of clear cell renal cell carcinoma (ccRCC).

OBJECTIVE

The aim of this study was to explore the clinical implications of immune cell infiltrates in ccRCC.

METHODS

The Cancer Genome Atlas (TCGA) database (N= 515) and E-MTAB-1980 cohort of patients (N= 101) were adopted to estimate the prognostic value of immune cell infiltration. Twenty-four types of immune cells were evaluated using single-sample gene set enrichment analysis. Cox regression analyses were conducted to develop an immune risk score.

RESULTS

Survival analyses revealed that 13 genes significantly associated with the overall survival (OS). Furthermore, multivariate Cox analysis identified an immune risk score on the basis of mast cells, natural killer CD56bright cells, T helper 17 (Th17) cells, and Th2 cells. The immune risk score was associated with OS, with hazard ratios of 2.72 (95% CI 2.17-3.40) and 3.24 (95% CI 1.64-6.44) in TCGA and E-MTAB-1980 datasets, respectively. This immune risk score was significantly correlated with some immunotherapy-related biomarkers.

CONCLUSIONS

We profiled a prognostic signature and established an immune risk score model for ccRCC, which could provide novel predictive markers for patients with ccRCC and an indicator for immunotherapy response measurement.

miRNA-based biomarkers, therapies, and resistance in Cancer

MicroRNAs (miRNAs), small non-coding RNAs (ncRNAs) of about 22 nucleotides in size, play important roles in gene regulation, and their dysregulation is implicated in human diseases including cancer. A variety of miRNAs could take roles in the cancer progression, participate in the process of tumor immune, and function with miRNA sponges. During the last two decades, the connection between miRNAs and various cancers has been widely researched. Based on evidence about miRNA, numerous potential cancer biomarkers for the diagnosis and prognosis have been put forward, providing a new perspective on cancer screening. Besides, there are several miRNA-based therapies among different cancers being conducted, advanced treatments such as the combination of synergistic strategies and the use of complementary miRNAs provide significant clinical benefits to cancer patients potentially. Furthermore, it is demonstrated that many miRNAs are engaged in the resistance of cancer therapies with their complex underlying regulatory mechanisms, whose comprehensive cognition can help clinicians and improve patient prognosis. With the belief that studies about miRNAs in human cancer would have great clinical implications, we attempt to summarize the current situation and potential development prospects in this review.

Identification of a Glucose Metabolism-related Signature for prediction of Clinical Prognosis in Clear Cell Renal Cell Carcinoma

Background: Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent and invasive histological subtypes among all renal cell carcinomas (RCC). Cancer cell metabolism, particularly glucose metabolism, has been reported as a hallmark of cancer. However, the characteristics of glucose metabolism-related gene sets in ccRCC have not been systematically profiled.

Methods: In this study, we downloaded a gene expression profile and glucose metabolism-related gene set from TCGA (The Cancer Genome Altas) and MSigDB, respectively, to analyze the characteristics of glucose metabolism-related gene sets in ccRCC. We used a multivariable Cox regression analysis to develop a risk signature, which divided patients into low- and high- risk groups. In addition, a nomogram that combined the risk signature and clinical characteristics was created for predicting the 3- and 5-year overall survival (OS) of ccRCC. The accuracy of the nomogram prediction was evaluated using the area under the receiver operating characteristic curve (AUC) and a calibration plot.

Results: A total of 231 glucose metabolism-related genes were found, and 68 differentially expressed genes (DEGs) were identified. After screening by univariate regression analysis, LASSO regression analysis and multivariable Cox regression analysis, six glucose metabolism-related DEGs (FBP1, GYG2, KAT2A, LGALS1, PFKP, and RGN) were selected to develop a risk signature. There were significant differences in the clinical features (Fuhrman nuclear grade and TNM stage) between the high- and low-risk groups. The multivariable Cox regression indicated that the risk score was independent of the prognostic factors (training set: HR=3.393, 95% CI [2.025, 5.685], p<0.001; validation set: HR=1.933, 95% CI [1.130, 3.308], p=0.016). The AUCs of the nomograms for the 3-year OS in the training and validation sets were 0.808 and 0.819, respectively, and 0.777 and 0.796, respectively, for the 5- year OS.

Conclusion: We demonstrated a novel glucose metabolism-related risk signature for predicting the prognosis of ccRCC. However, additional in vitro and in vivo research is required to validate our findings.

Identification of an Autophagy-Related Prognostic Signature for Clear Cell Renal Cell Carcinoma

Abnormal autophagy is closely related to the development of cancer. Many studies have demonstrated that autophagy plays an important role in biological function in clear cell renal cell carcinoma (ccRCC). This study aimed to construct a prognostic signature for ccRCC based on autophagy-related genes (ARGs) to predict the prognosis of ccRCC. Differentially expressed ARGs were obtained from ccRCC RNA-seq data in The Cancer Genome Atlas (TCGA) database. ARGs were enriched by gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). The prognostic ARGs used to construct the risk score models for overall survival (OS) and disease-free survival (DFS) were identified by Cox regression analyses. According to the median value of the risk score, patients were divided into a high-risk group and a low-risk group. The OS and DFS were analyzed by the Kaplan-Meier method. The predictive accuracy was determined by a receiver operating characteristic (ROC) curve analysis. Additionally, we performed stratification analyses based on different clinical variables and evaluated the correlation between the risk score and the clinical variables. The differentially expressed ARGs were mainly enriched in the platinum drug resistance pathway. The prognostic signatures based on 11 ARGs for OS and 5 ARGs for DFS were constructed and showed that the survive time was significantly shorter in the high-risk group than in the low-risk group (P < 0.001). The ROC curve for OS exhibited good predictive accuracy, with an area under the curve value of 0.738. In the stratification analyses, the OS time of the high-risk group was shorter than that of the low-risk group stratified by different clinical variables. In conclusion, an autophagy-related signature for OS we constructed can independently predict the prognosis of ccRCC patient, and provide a deep understanding of the potential biological mechanisms of autophagy in ccRCC.

Nomogram Based on microRNA Signature Contributes to Improve Survival Prediction of Clear Cell Renal Cell Carcinoma

Objective

Numerous microRNAs (miRNAs) have been identified in ccRCC and recommended to be used for predicting clear cell renal cell carcinoma (ccRCC) prognosis. However, it is not clear whether a miRNA-based nomogram results in improved survival prediction in patients with ccRCC.

Methods

miRNA profiles from tumors and normal tissues were downloaded from The Cancer Genome Atlas (TCGA) database and analyzed using the "limma" package. The association between differentially expressed miRNAs and patient prognosis was identified using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Next, all patients were randomly divided into development and validation cohorts at a ratio of 1 : 1. A nomogram was established based on independent prognostic factors in the development cohort. The prognostic performance of the nomogram was validated in both cohorts using the concordance index (C-index) and calibration plots.

Results

Multivariate Cox analysis identified the 13-miRNA signature, as well as AJCC stage and age, as independent prognostic factors after adjusting for other clinical covariates. The nomogram was built based on the independent variables. In the development cohort, the C-index for the constructed nomogram to predict overall survival (OS) was 0.792, which was higher than the C-index (0.731) of the AJCC staging system and C-index (0.778) of the miRNA signature. The nomogram demonstrated good discriminative ability in the validation cohort in predicting OS, with a C-index of 0.762. The calibration plots indicated an excellent agreement between the nomogram predicted survival probability and the actual observed outcomes. Furthermore, decision curve analysis (DCA) indicated that the nomogram was superior to the AJCC staging system in increasing the net clinical benefit.

Conclusions

The novel proposed nomogram based on a miRNA signature is a more reliable and robust tool for predicting the OS of patients with ccRCC compared to AJCC staging system, thus, improving clinical decision-making.

Identification of a three-m6A related gene risk score model as a potential prognostic biomarker in clear cell renal cell carcinoma

Background

Clear cell renal cell carcinoma (ccRCC) is one of the most prevalent malignancies worldwide, N6-methyladenosine (m6A) has been shown to play important roles in regulating gene expression and phenotypes in both health and disease. Here, our purpose is to construct a m6A-regulrator-based risk score (RS) for prediction of the prognosis of ccRCC.

Methods

We used clinical and expression data of m6A related genes from The Cancer Genome Atlas (TCGA) dataset and the Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis to develop an RS to predict survival of patients with ccRCC, and analyzed correlations between RS and other clinical indicators such as age, grade and stage. Validation of this RS was then engaged in another cohort, E-MTAB-1980 from the ArrayExpress dataset. Finally, we used quantitative real-time PCR to analyze the expression profile of genes consists of the RS.

Results

A three-gene RS including METTL3, METTL14 and HNRNPA2B1 which can predict overall survival (OS) of ccRCC patients from TCGA. After applying this RS into the validation cohort from Arrayexpress, we found that it successfully reproduced the result; furthermore, the results of PCR validation were in line with our analysis.

Conclusion

To sum up, our study has identified an RS composed of m6A related genes that may predict the prognosis of ccRCC patients, which might be helpful for future therapeutic strategies. Our results call for further experimental studies for validations.

Association of CXCL13 and Immune Cell Infiltration Signature in Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most commonly diagnosed kidney tumors and is often accompanied by immune cell infiltration. In this study, we attempted to identify microenvironment-associated genes and explore the correlation between CXCL13 and tumor-infiltrating immune cells (TIICs). Gene expression profiles and their corresponding clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. The ESTIMATE (Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data) algorithm was used to calculate immune cell and stromal cell scores, according to which patients were divided into high- and low-score groups, allowing differentially expressed genes (DEGs) to be identified. Functional enrichment and PPI network analysis were used to identify the functions of the DEGs. CIBERSORT algorithm and TIMER analysis were used to evaluate the immune score. Oncomine and TCGA database were used to explore CXCL13 mRNA expression level in ccRCC. High ESTIMATE score was significantly associated with prognosis. Functional enrichment analysis clarified that DEGs were associated with T cell activation, immune response-regulating cell surface receptor signaling pathway, and positive regulation of cytokine production. PPI network was used to identify CXCL13 as a hub gene. And CIBERSORT algorithm and TIMER analysis showed that strong correlation between CXCL13 expression level and TIICs. Oncomine database was used to validate high CXCL13 expression level in ccRCC tissue, compared to normal tissues. In conclusion, we obtained a list of tumor microenvironment-related genes and identified CXCL13 as an immune response biomarker in patients with ccRCC, GSEA analysis, wound healing and transwell assays showed CXCL13 played a role in tumor migration.

An outcome model for human bladder cancer: A comprehensive study based on weighted gene co-expression network analysis

The precision evaluation of prognosis is crucial for clinical treatment decision of bladder cancer (BCa). Therefore, establishing an effective prognostic model for BCa has significant clinical implications. We performed WGCNA and DEG screening to initially identify the candidate genes. The candidate genes were applied to construct a LASSO Cox regression analysis model. The effectiveness and accuracy of the prognostic model were tested by internal/external validation and pan‐cancer validation and time‐dependent ROC. Additionally, a nomogram based on the parameter selected from univariate and multivariate cox regression analysis was constructed. Eight genes were eventually screened out as progression‐related differentially expressed candidates in BCa. LASSO Cox regression analysis identified 3 genes to build up the outcome model in E‐MTAB‐4321 and the outcome model had good performance in predicting patient progress free survival of BCa patients in discovery and test set. Subsequently, another three datasets also have a good predictive value for BCa patients' OS and DFS. Time‐dependent ROC indicated an ideal predictive accuracy of the outcome model. Meanwhile, the nomogram showed a good performance and clinical utility. In addition, the prognostic model also exhibits good performance in pan‐cancer patients. Our outcome model was the first prognosis model for human bladder cancer progression prediction via integrative bioinformatics analysis, which may aid in clinical decision‐making.

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

Clear cell renal cell carcinoma (ccRCC) is a heterogeneous tumor that the underlying molecular mechanisms are largely unclear. This study aimed to elucidate the key candidate genes and pathways in ccRCC by integrated bioinformatics analysis. 1387 differentially expressed genes were identified based on three expression profile datasets, including 673 upregulated genes and 714 downregulated genes. Then we used weighted correlation network analysis to identify 6 modules associated with pathological stage and grade, blue module was the most relevant module. GO and KEGG pathway analyses showed that genes in blue module were enriched in cell cycle and metabolic related pathways. Further, 25 hub genes in blue module were identified as hub genes. Based on GEPIA database, 9 genes were associated with progression and prognosis of ccRCC patients, including PTTG1, RRM2, TOP2A, UHRF1, CEP55, BIRC5, UBE2C, FOXM1 and CDC20. Then multivariate Cox regression showed that the risk score base on 9 key genes signature was a clinically independent prognostic factor for ccRCC patients. Moreover, we screened out several new small molecule drugs that have the potential to treat ccRCC. Few of them were identified as biomarkers in ccRCC. In conclusion, our research identified 9 potential prognostic genes and several candidate small molecule drugs for ccRCC treatment.