MicroRNAs Possibly Involved in the Development of Bone Metastasis in Clear-Cell Renal Cell Carcinoma

HAGLR, A Long Non-coding RNA of Potential Tumor Suppressive Function in Clear Cell Renal Cell Carcinoma: Diagnostic and Prognostic Implications

Research works suggested the role of long non-coding RNAs (lncRNAs) in pathogenesis of clear cell renal cell carcinoma (ccRCC). lncRNA HAGLR is studied in several malignancies, but not in ccRCC. From The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset, we analyzed molecular alterations of HAGLR and constructed a competitive endogenous RNA (ceRNA) network with related miRNAs and mRNAs. Gene Ontology analysis was done to identify important pathways enriched with HAGLR recovered mRNAs. Clinical importance of HAGLR and related mRNAs was assessed and, the impact of selected mRNA-encoding genes on tumor immune infiltration was studied using TIMER. HAGLR expression was reduced in ccRCC than in normal kidneys, and correlated significantly with gene promoter methylation. Low HAGLR level in tumors showed diagnostic potency, and was associated with clinicopathological parameters (stage/grade/metastasis) and poor patient survival. The HAGLR-associated ceRNA network constituted 13 miRNAs and 23 mRNAs differentially expressed in the TCGA-KIRC dataset. From HAGLR recovered mRNA-encoding genes, we developed a 5-gene (PAQR5, ARHGAP24, HABP4, PDLIM5, and RPS6KA2) prognostic signature in the training dataset and validated it in testing as well as entire datasets. The expression level of signature genes showed negative correlation with tumor infiltration of immune cells having adverse impact on ccRCC prognosis and also with tumor derived chemokines facilitating the infiltration. In conclusion, HAGLR seemed to play a tumor suppressive role in ccRCC. HAGLR and associated gene signature may have implementation in improving existing prognostic measure and developing effective immunotherapeutic strategies for ccRCC.

Profiling of Serum miRNAs Constructs a Diagnostic 3-miRNA Panel for Clear-Cell Renal Cell Carcinoma

BACKGROUND

Renal cell carcinoma (RCC) carries significant morbidity and mortality globally with an increasing incidence per year predominantly represented by clear-cell renal cell carcinoma (ccRCC) which accounts for 70-80% of all RCC cases. MicroRNAs(miRNAs) implicate tumor development and progression in epigenetic mechanisms and available profiling of serum miRNAs potentiate them as diagnostic markers for various cancers.

MATERIALS AND METHODS

A total of 108 ccRCC patients and 112 normal controls were enrolled. A 3-stage experiment was conducted to identify differentially expressed serum miRNAs in ccRCC and establish a diagnostic miRNAs panel. Additionally, bioinformatic analysis was employed to predict selected miRNAs' target genes, preform functional annotation and explore the roles in ccRCC.

RESULTS

MiR-429, miR-10a-5p, miR-154-5p were found to be up-regulated miRNAs. Inversely, miR-27a-3p and miR-221-3p were found to be down-regulated miRNAs. These 5 miRNAs were selected to construct diagnostic panel by backward stepwise logistic regression analysis and ultimately a 3-miRNA panel (miR-429, miR-10a-5p and miR-27a-3p) was established [area under the curve (AUC) = 0.897, sensitivity = 85.0%, specificity = 83.3%].

CONCLUSION

The panel of 3-miRNA holds promise as a novel, convenient, and noninvasive diagnostic method for early detection of ccRCC.

Identification of differentially expressed genes associated with the pathogenesis of gastric cancer by bioinformatics analysis

AIM

Gastric cancer (GC) is one of the most diagnosed cancers worldwide. GC is a heterogeneous disease whose pathogenesis has not been entirely understood. Besides, the GC prognosis for patients remains poor. Hence, finding reliable biomarkers and therapeutic targets for GC patients is urgently needed.

METHODS

GSE54129 and GSE26942 datasets were downloaded from Gene Expression Omnibus (GEO) database to detect differentially expressed genes (DEGs). Then, gene set enrichment analyses and protein-protein interactions were investigated. Afterward, ten hub genes were identified from the constructed network of DEGs. Then, the expression of hub genes in GC was validated. Performing survival analysis, the prognostic value of each hub gene in GC samples was investigated. Finally, the databases were used to predict microRNAs that could regulate the hub genes. Eventually, top miRNAs with more interactions with the list of hub genes were introduced.

RESULTS

In total, 203 overlapping DEGs were identified between both datasets. The main enriched KEGG pathway was "Protein digestion and absorption." The most significant identified GO terms included "primary alcohol metabolic process," "basal part of cell," and "extracellular matrix structural constituent conferring tensile strength." Identified hub modules were COL1A1, COL1A2, TIMP1, SPP1, COL5A2, THBS2, COL4A1, MUC6, CXCL8, and BGN. The overexpression of seven hub genes was associated with overall survival. Moreover, among the list of selected miRNAs, hsa-miR-27a-3, hsa-miR-941, hsa-miR-129-2-3p, and hsa-miR-1-3p, were introduced as top miRNAs targeting more than five hub genes.

CONCLUSIONS

The present study identified ten genes associated with GC, which may help discover novel prognostic and diagnostic biomarkers as well as therapeutic targets for GC. Our results may advance the understanding of GC occurrence and progression.

MiRNAs and snoRNAs in Bone Metastasis: Functional Roles and Clinical Potential

Bone is a frequent site of metastasis. Bone metastasis is associated with a short-term prognosis in cancer patients, and current treatments aim to slow its growth, but are rarely curative. Thus, revealing molecular mechanisms that explain why metastatic cells are attracted to the bone micro-environment, and how they successfully settle in the bone marrow-taking advantage over bone resident cells-and grow into macro-metastasis, is essential to propose new therapeutic approaches. MicroRNAs and snoRNAs are two classes of small non-coding RNAs that post-transcriptionally regulate gene expression. Recently, microRNAs and snoRNAs have been pointed out as important players in bone metastasis by (i) preparing the pre-metastatic niche, directly and indirectly affecting the activities of osteoclasts and osteoblasts, (ii) promoting metastatic properties within cancer cells, and (iii) acting as mediators within cells to support cancer cell growth in bone. This review aims to highlight the importance of microRNAs and snoRNAs in metastasis, specifically in bone, and how their roles can be linked together. We then discuss how microRNAs and snoRNAs are secreted by cancer cells and be found as extracellular vesicle cargo. Finally, we provide evidence of how microRNAs and snoRNAs can be potential therapeutic targets, at least in pre-clinical settings, and how their detection in liquid biopsies can be a useful diagnostic and/or prognostic biomarker to predict the risk of relapse in cancer patients.

Construction and validation of a novel ten miRNA-pair based signature for the prognosis of clear cell renal cell carcinoma

BACKGROUND

Clear cell renal cell carcinoma (ccRCC) is the most predominate pathological subtype of renal cell carcinoma, causing a recurrence or metastasis rate as high as 20% to 40% after operation, for which effective prognostic signature is urgently needed.

METHODS

The mRNA and miRNA profiles of ccRCC specimens were collected from the Cancer Genome Atlas. MiRNA-pair risk score (miPRS) for each miRNA pair was generated as a signature and validated by univariate and multivariate Cox proportional hazards regression analysis. Functional enrichment was performed, and immune cells infiltration, as well as tumor mutation burden (TMB), and immunophenoscore (IPS) were evaluated between high and low miPRS groups. Target gene-prediction and differentially expressed gene-analysis were performed based on databases of miRDB, miRTarBase, and TargetScan. Multivariate Cox proportional hazards regression analysis was adopted to establish the prognostic model and Kaplan-Meier survival analysis was performed.

FINDINGS

A novel 10 miRNA-pair based signature was established. Area under the time-dependent receiver operating curve proved the performance of the signature in the training, validation, and testing cohorts. Higher TMB, as well as the higher CTLA4-negative PD1-negative IPS, were discovered in high miPRS patients. A prognostic model was built based on miPRS (1 year-, 5 year-, 10 year- ROC-AUC=0.92, 0.84, 0.82, respectively).

INTERPRETATION

The model based on miPRS is a novel and valid tool for predicting the prognosis of ccRCC.

FUNDING

This study was supported by research grants from the China National Natural Scientific Foundation (81903972, 82002018, and 82170752) and Shanghai Sailing Program (19YF1406700 and 20YF1406000).

Novel Imaging Methods for Renal Mass Characterization: A Collaborative Review

CONTEXT

The incidental detection of localized renal masses has been rising steadily, but a significant proportion of these tumors are benign or indolent and, in most cases, do not require treatment. At the present time, a majority of patients with an incidentally detected renal tumor undergo treatment for the presumption of cancer, leading to a significant number of unnecessary surgical interventions that can result in complications including loss of renal function. Thus, there exists a clinical need for improved tools to aid in the pretreatment characterization of renal tumors to inform patient management.

OBJECTIVE

To systematically review the evidence on noninvasive, imaging-based tools for solid renal mass characterization.

EVIDENCE ACQUISITION

The MEDLINE database was systematically searched for relevant studies on novel imaging techniques and interpretative tools for the characterization of solid renal masses, published in the past 10 yr.

EVIDENCE SYNTHESIS

Over the past decade, several novel imaging tools have offered promise for the improved characterization of indeterminate renal masses. Technologies of particular note include multiparametric magnetic resonance imaging of the kidney, molecular imaging with targeted radiopharmaceutical agents, and use of radiomics as well as artificial intelligence to enhance the interpretation of imaging studies. Among these, 99mTc-sestamibi single photon emission computed tomography/computed tomography (CT) for the identification of benign renal oncocytomas and hybrid oncocytic chromophobe tumors, and positron emission tomography/CT imaging with radiolabeled girentuximab for the identification of clear cell renal cell carcinoma, are likely to be closest to implementation in clinical practice.

CONCLUSIONS

A number of novel imaging tools stand poised to aid in the noninvasive characterization of indeterminate renal masses. In the future, these tools may aid in patient management by providing a comprehensive virtual biopsy, complete with information on tumor histology, underlying molecular abnormalities, and ultimately disease prognosis.

PATIENT SUMMARY

Not all renal tumors require treatment, as a significant proportion are either benign or have limited metastatic potential. Several innovative imaging tools have shown promise for their ability to improve the characterization of renal tumors and provide guidance in terms of patient management.

Risk Factors, Prognostic Factors, and Nomograms for Bone Metastasis in Patients with Newly Diagnosed Clear Cell Renal Cell Carcinoma: A Large Population-Based Study

Background

This study aimed to investigate risk factors and prognostic factors in patients with clear cell renal cell carcinoma (ccRCC) with bone metastasis (BM) and establish nomograms to provide a quantitative prediction of the risk of BM and survival probability.

Methods

The clinicopathological characteristics of patients with ccRCC between January 2010 and December 2015 were obtained from the Surveillance, Epidemiology and End Results (SEER) database. Independent factors for BM in ccRCC patients were identified using univariate and multivariate logistic regression analyses. Prognostic factors for predicting cancer-specific death were evaluated using univariate and multivariate analyses based on a competing risk regression model. We then constructed a diagnostic nomogram and a prognostic nomogram. The two nomograms were evaluated using calibration curves, receiver operating characteristic curves, and decision curve analysis.

Results

Our study included 34,659 patients diagnosed with ccRCC in the SEER database, with 1,415 patients who presented with bone metastasis. Risk factors for BM in patients with ccRCC included age, stage T, stage N, brain metastasis, liver metastasis, lung metastasis, tumor size, and laterality. Independent prognostic factors for patients with ccRCC patients with BM were Fuhrman grade, tumor size, T stage, N stage, brain metastases, lung metastasis, and surgery. For the diagnostic nomogram, the area under the curve values in the training and testing cohorts were 0.863 (95% CI, 0.851–0.875) and 0.859 (95% CI, 0.839–0.878), respectively. In the prognostic cohort, the area under the curve values for 1-, 2-, and 3-year cancer-specific survival rates in the training cohort were 0.747, 0.774, and 0.780, respectively, and 0.671, 0.706, and 0.696, respectively, in the testing cohort. Through calibration curves and decision curve analyses, the nomograms displayed excellent performance.

Conclusions

Several factors related to the development and prognosis of BM in patients with ccRCC were identified. The nomograms constructed in this study are expected to become effective and precise tools for clinicians to improve cancer management.

ITGB1-DT/ARNTL2 axis may be a novel biomarker in lung adenocarcinoma: a bioinformatics analysis and experimental validation

BACKGROUND

Lung cancer is one of the most lethal malignant tumors that endangers human health. Lung adenocarcinoma (LUAD) has increased dramatically in recent decades, accounting for nearly 40% of all lung cancer cases. Increasing evidence points to the importance of the competitive endogenous RNA (ceRNA) intrinsic mechanism in various human cancers. However, behavioral characteristics of the ceRNA network in lung adenocarcinoma need further study.

METHODS

Groups based on SLC2A1 expression were used in this study to identify associated ceRNA networks and potential prognostic markers in lung adenocarcinoma. The Cancer Genome Atlas (TCGA) database was used to obtain the patients' lncRNA, miRNA, and mRNA expression profiles, as well as clinical data. Informatics techniques were used to investigate the effect of hub genes on prognosis. The Cox regression analyses were performed to evaluate the prognostic effect of hub genes. The methylation, GSEA, and immune infiltration analyses were utilized to explore the potential mechanisms of the hub gene. The CCK-8, transwell, and colony formation assays were performed to detect the proliferation and invasion of lung cancer cells.

RESULTS

We eventually identified the ITGB1-DT/ARNTL2 axis as an independent fact may promote lung adenocarcinoma progression. Furthermore, methylation analysis revealed that hypo-methylation may cause the dysregulated ITGB1-DT/ARNTL2 axis, and immune infiltration analysis revealed that the ITGB1-DT/ARNTL2 axis may affect the immune microenvironment and the progression of lung adenocarcinoma. The CCK-8, transwell, and colonu formation assays suggested that ITGB1-DT/ARNTL2 promotes the progression of lung adenocarcinoma. And hsa-miR-30b-3p reversed the ITGB1/ARNTL2-mediated oncogenic processes.

CONCLUSION

Our study identified the ITGB1-DT/ARNTL2 axis as a novel prognostic biomarker affects the prognosis of lung adenocarcinoma.

Development and Validation of a Predictive Model to Evaluate the Risk of Bone Metastasis in Kidney Cancer

Background

Bone is a common target of metastasis in kidney cancer, and accurately predicting the risk of bone metastases (BMs) facilitates risk stratification and precision medicine in kidney cancer.

Methods

Patients diagnosed with kidney cancer were extracted from the Surveillance, Epidemiology, and End Results (SEER) database to comprise the training group from 2010 to 2017, and the validation group was drawn from our academic medical center. Univariate and multivariate logistic regression analyses explored the statistical relationships between the included variables and BM. Statistically significant risk factors were applied to develop a nomogram. Calibration plots, receiver operating characteristic (ROC) curves, probability density functions (PDF), and clinical utility curves (CUC) were used to verify the predictive performance. Kaplan-Meier (KM) curves demonstrated survival differences between two subgroups of kidney cancer with and without BMs. A convenient web calculator was provided for users via “shiny” package.

Results

A total of 43,503 patients were recruited in this study, of which 42,650 were training group cases and 853 validation group cases. The variables included in the nomogram were sex, pathological grade, T-stage, N-stage, sequence number, brain metastases, liver metastasis, pulmonary metastasis, histological type, primary site, and laterality. The calibration plots confirmed good agreement between the prediction model and the actual results. The area under the curve (AUC) values in the training and validation groups were 0.952 (95% CI, 0.950–0.954) and 0.836 (95% CI, 0.809–0.860), respectively. Based on CUC, we recommend a threshold probability of 5% to guide the diagnosis of BMs.

Conclusions

The comprehensive predictive tool consisting of nomogram and web calculator contributes to risk stratification which helped clinicians identify high-risk cases and provide personalized treatment options.