Gene Expression Analysis of Aggressive Clinical T1 Stage Clear Cell Renal Cell Carcinoma for Identifying Potential Diagnostic and Prognostic Biomarkers

Association of lncRNA and transcriptome intersections with response to targeted therapy in metastatic renal cell carcinoma

Long non-coding RNAs (lncRNAs) serve an important role in cancer progression and may be used as efficient molecular biomarkers. The present study aimed to identify lncRNAs associated with the response to the receptor tyrosine kinase inhibitor sunitinib and transcriptome profile and clinical features of metastatic renal cell carcinoma (mRCC). The gene expression of 84 cancer-associated lncRNAs in tumor and non-malignant tissue samples of 38 patients with mRCC was evaluated using quantitative PCR. In addition, the coding transcriptome was estimated using RNA sequencing in a subgroup of 20 patients and mRNA-lncRNA intersections were identified. In total, 37 and 13 lncRNAs were down- and upregulated, respectively, in tumor compared with non-malignant adjacent tissue samples. A total of 10 and 4 lncRNAs were up- and downregulated, respectively, in good responders to sunitinib compared with poor responders. High expression of HNF1A-AS1 and IPW lncRNAs was associated with prolonged progression-free survival of patients and a high expression of the TUSC7 lncRNA was associated with poor response and worse survival. Significant associations of dysregulated MEG3 and SNHG16 lncRNAs with expression of protein-coding genes representing various pathways, were identified. Furthermore, a significantly higher expression of CLIP4 gene was observed in good responders. The present study revealed promising candidates for predictive and prognostic biomarkers with further therapeutic potential.

Development and validation of a prognostic prediction model for iron metabolism-related genes in patients with pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is one of the most aggressive tumors of the digestive tract, with low surgical resection rate and insensitivity to radiotherapy and chemotherapy. Existing evidence suggests that regulation of ferroptosis can induce PAAD cell death, inhibit tumor growth, and may synergistically improve the sensitivity of other antitumor drugs. However, there is little of systematic research on iron metabolism-related genes in PAAD. In this study, a risk-score system of PAAD iron metabolism-related genes was designed and tested, and verified to be robust. Materials and Methods: The TCGA database was used to download 177 PAAD patients' message RNA (mRNA) expression profiles and clinical characteristics. By identifying dysregulated iron metabolism-related genes between PAAD related tissues and adjacent normal tissues, univariate Cox proportional hazards regression and LASSO regression algorithm were used to establish prognostic risk-score system and construct nomogram to estimate the 1-, 2-, 3-year survival in PAAD patients. Finally, selected genes were validated by quantitative PCR (q-PCR). Results: A 9-gene related to iron metabolism risk-score system of PAAD was constructed and validated. The clinicopathological characteristics of age, histologic grade, pathologic stage, T stage, residual tumor, and primary therapy outcome were all worse in patients with a higher risk-score. Further, immunohistochemistry results of SLC2A1, MBOAT2, XDH, CTSE, MOCOS, and ATP6V0A4 confirmed that patients with higher expression are more malignant. Then, a nomogram with 9-gene risk score system as a separate clinical factor was utilized to foretell the 1-, 2-, 3-year overall survival rate of PAAD patients. Results of q-PCR showed that 8 of the 9 genes screened were significantly up-regulated in at least one PAAD cell line, and one gene was significantly down-regulated in three PAAD cell lines. Conclusion: To conclude, we generated a nine-gene system linked to iron metabolism as an independent indicator for predicting PAAD prognosis, therefore presenting a possible prognostic biomarker and potential treatment targets for PAAD.

Whole-genome sequencing of extrachromosomal circular DNA of cerebrospinal fluid of medulloblastoma

BACKGROUND

Medulloblastoma (MB) is a malignant tumor associated with a poor prognosis in part due to a lack of effective detection methods. Extrachromosomal circular DNA (eccDNA) has been associated with multiple tumors. Nonetheless, little is currently known on eccDNA in MB.

METHODS

Genomic features of eccDNAs were identified in MB tissues and matched cerebrospinal fluid (CSF) and compared with corresponding normal samples using Circle map. The nucleotides on both sides of the eccDNAs' breakpoint were analyzed to understand the mechanisms of eccDNA formation. Bioinformatics analysis combined with the Gene Expression Omnibus (GEO) database identified features of eccDNA-related genes in MB. Lasso Cox regression model, univariate and multivariate Cox regression analysis, time-dependent ROC, and Kaplan-Meier curve were used to assess the potential diagnostic and prognostic value of the hub genes.

RESULTS

EccDNA was profiled in matched tumor and CSF samples from MB patients, and control, eccDNA-related genes enriched in MB were identified. The distribution of eccDNAs in the genome was closely related to gene density and the mechanism of eccDNA formation was evaluated. EccDNAs in CSF exhibited similar distribution with matched MB tissues but were differentially expressed between tumor and normal. Ten hub genes prominent in both the eccDNA dataset and the GEO database were selected to classify MB patients to either high- or low-risk groups, and a prognostic nomogram was thus established.

CONCLUSIONS

This study provides preliminary evidence of the characteristics and formation mechanism of eccDNAs in MB and CSF. Importantly, eccDNA-associated hub genes in CSF could be used as diagnostic and prognostic biomarkers for MB.

Molecular characterization of renal cell carcinoma tumors from a phase III anti-angiogenic adjuvant therapy trial

Multigene assays can provide insight into key biological processes and prognostic information to guide development and selection of adjuvant cancer therapy. We report a comprehensive genomic and transcriptomic analysis of tumor samples from 171 patients at high risk for recurrent renal cell carcinoma post nephrectomy from the S-TRAC trial (NCT00375674). We identify gene expression signatures, including STRAC11 (derived from the sunitinib-treated population). The overlap in key elements captured in these gene expression signatures, which include genes representative of the tumor stroma microenvironment, regulatory T cell, and myeloid cells, suggests they are likely to be both prognostic and predictive of the anti-angiogenic effect in the adjuvant setting. These signatures also point to the identification of potential therapeutic targets for development in adjuvant renal cell carcinoma, such as MERTK and TDO2. Finally, our findings suggest that while anti-angiogenic adjuvant therapy might be important, it may not be sufficient to prevent recurrence and that other factors such as immune response and tumor environment may be of greater importance.

Based on the S-TRAC results, sunitinib is approved as adjuvant treatment for adult patients at high risk of recurrent RCC following nephrectomy. Here, the authors report the results of an integrated multi-omics tumor analysis of 171 patients from the trial and identify specific molecular subtypes as well as potential new targets.

Stress Reactivity, Susceptibility to Hypertension, and Differential Expression of Genes in Hypertensive Compared to Normotensive Patients

Although half of hypertensive patients have hypertensive parents, known hypertension-related human loci identified by genome-wide analysis explain only 3% of hypertension heredity. Therefore, mainstream transcriptome profiling of hypertensive subjects addresses differentially expressed genes (DEGs) specific to gender, age, and comorbidities in accordance with predictive preventive personalized participatory medicine treating patients according to their symptoms, individual lifestyle, and genetic background. Within this mainstream paradigm, here, we determined whether, among the known hypertension-related DEGs that we could find, there is any genome-wide hypertension theranostic molecular marker applicable to everyone, everywhere, anytime. Therefore, we sequenced the hippocampal transcriptome of tame and aggressive rats, corresponding to low and high stress reactivity, an increase of which raises hypertensive risk; we identified stress-reactivity-related rat DEGs and compared them with their known homologous hypertension-related animal DEGs. This yielded significant correlations between stress reactivity-related and hypertension-related fold changes (log2 values) of these DEG homologs. We found principal components, PC1 and PC2, corresponding to a half-difference and half-sum of these log2 values. Using the DEGs of hypertensive versus normotensive patients (as the control), we verified the correlations and principal components. This analysis highlighted downregulation of β-protocadherins and hemoglobin as whole-genome hypertension theranostic molecular markers associated with a wide vascular inner diameter and low blood viscosity, respectively.

Gene Expression Analysis of Aggressive Adult Xp11.2 Translocation Renal Cell Carcinoma at Clinical Stage T1N0M0 to Identify Potential Prognostic and Therapeutic Biomarkers

Xp11.2 translocation renal cell carcinoma (tRCC), involving transcription factor E3 (TFE3) gene fusions, is a rare and aggressive RCC variant when present in adults and has been recently recognized as a unique entity in RCC. Biomarkers and treatment guidelines do not exist for patients with aggressive Xp11.2 tRCC. The aim was to identify and evaluate therapeutic biomarkers for aggressive Xp11.2 tRCC. RNA sequencing was performed using formalin-fixed, paraffin-embedded tissues from 11 adult patients with clinical T1N0M0 Xp11.2 tRCC, including three patients with aggressive characteristics (recurrence or cancer-specific death after nephrectomy). Thirty genes were differentially expressed between the aggressive and non-aggressive groups, even after adjustment, and were associated with KEGG pathways related to the aggressiveness of Xp11.2 tRCC. PIK3R2, involved in various KEGG pathways, including the PI3K/AKT/mTOR pathway, was overexpressed in the Xp11.2 tRCC cell lines UOK120 and UOK146. The PI3K pathway inhibitor LY294002 showed a significant therapeutic benefit. This study provides the first candidate biomarker, PIK3R2, for aggressive clinical T1N0M0 Xp11.2 tRCC. Furthermore, this study is the first to recommend a targeted drug, LY294002, for aggressive Xp11.2 tRCC based on the molecular pathophysiology.

BRCA1-Associated Protein 1 (BAP-1) as a Prognostic and Predictive Biomarker in Clear Cell Renal Cell Carcinoma: A Systematic Review

BACKGROUND: The gene that encodes BRCA1-associated protein 1 (BAP1) has been reported to be dysregulated in several human cancers such as uveal melanoma, malignant pleural mesothelioma, hepatocellular carcinoma, thymic epithelial tumors, and clear-cell renal cell carcinoma (ccRCC). The gene is located on the human chromosome 3p21.3, encoding a deubiquitinase and acts as a classic two-hit tumor suppressor gene. BAP1 predominantly resides in the nucleus, where it interacts with several chromatin-associated factors, as well as regulates calcium signaling in the cytoplasm. As newer therapies continue to evolve for the management of RCC, it is important to understand the role of BAP1 mutation as a prognostic and predictive biomarker. OBJECTIVE: We aimed to systematically evaluate the role of BAP1 mutations in patients with RCC in terms of its impact on prognosis and its role as a predictive biomarker. METHODS: Following PRISMA guidelines, we performed a systematic literature search using PubMed and Embase through March 2021. Titles and abstracts were screened to identify articles for full-text and then a descriptive review was performed. RESULTS: A total of 490 articles were initially identified. Ultimately 71 articles that met our inclusion criteria published between 2012–2021 were included in the analysis. Data were extracted and organized to reflect the role of BAP1 alterations as a marker of prognosis as well as a marker of response to treatments, such as mTOR inhibitors, VEGF tyrosine kinase inhibitors, and immune checkpoint inhibitors. CONCLUSIONS: Alterations in BAP1 appear to be uniformly associated with poor prognosis in patients with RCC. Knowledge gaps remain with regard to the predictive relevance of BAP1 alterations, especially in the context of immunotherapy. Prospective studies are required to more precisely ascertain the predictive value of BAP1 alterations in RCC.

A Personalized Genomics Approach of the Prostate Cancer

Decades of research identified genomic similarities among prostate cancer patients and proposed general solutions for diagnostic and treatments. However, each human is a dynamic unique with never repeatable transcriptomic topology and no gene therapy is good for everybody. Therefore, we propose the Genomic Fabric Paradigm (GFP) as a personalized alternative to the biomarkers approach. Here, GFP is applied to three (one primary—“A”, and two secondary—“B” & “C”) cancer nodules and the surrounding normal tissue (“N”) from a surgically removed prostate tumor. GFP proved for the first time that, in addition to the expression levels, cancer alters also the cellular control of the gene expression fluctuations and remodels their networking. Substantial differences among the profiled regions were found in the pathways of P53-signaling, apoptosis, prostate cancer, block of differentiation, evading apoptosis, immortality, insensitivity to anti-growth signals, proliferation, resistance to chemotherapy, and sustained angiogenesis. ENTPD2, AP5M1 BAIAP2L1, and TOR1A were identified as the master regulators of the “A”, “B”, “C”, and “N” regions, and potential consequences of ENTPD2 manipulation were analyzed. The study shows that GFP can fully characterize the transcriptomic complexity of a heterogeneous prostate tumor and identify the most influential genes in each cancer nodule.

PBRM1 Mutations as a Predictive Biomarker for Immunotherapy in Metastatic Renal Cell Carcinoma: A Systematic Review

INTRODUCTION: Genomic features linked to prediction of response to immunotherapy in metastatic renal cell carcinoma (mRCC) are still lacking. Protein polybromo-1 (PBRM1) mutations have been studied as a potential biomarker of clinical benefit, with conflicting published data so far. MATERIAL AND METHODS: This systematic review was guided by the standards of the PRISMA statement to identify studies involving mRCC, immunotherapy and mutations in PBRM1. The main objective was to assess the relationship between PBRM1 mutations and response to immune checkpoint inhibitors (ICI) in patients with mRCC. RESULTS: After an initial search that identified 422 studies, 8 studies met the eligibility criteria and were selected for the final analysis. Data are included from 2 trials in the first-line treatment setting, and 6 trials in second- or later treatment lines evaluating the relationship between the presence of PBRM1 mutations and clinical benefit (CB) with ICI treatment. Regarding the first-line treatment setting, the analysis of both studies failed to show any CB in patients with PBRM1 mutations treated with ICI. However, for the second- and later treatment lines, the results were mixed. CONCLUSIONS: PBRM1 mutations may be a potential genomic biomarker to predict response to ICI treatment in patients with mRCC, mainly in second- and later treatment lines, but the existence of conflicting data in the literature highlights an important bias in the studies and the need for additional clinical validation in large, prospective trials.

The DEAD/DEAH Box Helicase, DDX11, Is Essential for the Survival of Advanced Clear Cell Renal Cell Carcinoma and Is a Determinant of PARP Inhibitor Sensitivity

Simple Summary

DDX11, a helicase involved in sister chromatid cohesion, was identified as a significant biomarker of aggressive renal cell carcinoma (RCC) in our previous studies. In this study, we evaluated the molecular pathways through which DDX11 is involved in RCC cell survival. Furthermore, we assessed the sensitivity of poly (ADP-ribose) polymerase (PARP) inhibitors, which have not been used in RCC treatment, in association with DDX11 expression. DDX11-deficient RCC inhibited RCC proliferation, caused defects in segregation, and increased apoptosis. DDX11-deficient RCC was associated with increased sensitivity to PARP inhibition. DDX11 could be a novel therapeutic and prognostic biomarker for RCC patients, and this study is the first to suggest the use of PARP inhibitors in DDX11-deficient RCC patients.

Abstract

Genes associated with the DEAD-box helicase DDX11 are significant biomarkers of aggressive renal cell carcinoma (RCC), but their molecular function is poorly understood. We analyzed the molecular pathways through which DDX11 is involved in RCC cell survival and poly (ADP-ribose) polymerase (PARP) inhibitor sensitivity. Immunohistochemistry and immunoblotting determined DDX11 expression in normal kidney tissues, benign renal tumors, and RCC tissues and cell lines. Quantitative polymerase chain reaction validated the downregulation of DDX11 in response to transfection with DDX11-specific small interfering RNA. Proliferation analysis and apoptosis assays were performed to determine the impact of DDX11 knockdown on RCC cells, and the relevant effects of sunitinib, olaparib, and sunitinib plus olaparib were evaluated. DDX11 was upregulated in high-grade, advanced RCC compared to low-grade, localized RCC, and DDX11 was not expressed in normal kidney tissues or benign renal tumors. DDX11 knockdown resulted in the inhibition of RCC cell proliferation, segregation defects, and rapid apoptosis. DDX11-deficient RCC cells exhibited significantly increased sensitivity to olaparib compared to sunitinib alone or sunitinib plus olaparib combination treatments. Moreover, DDX11 could determine PARP inhibitor sensitivity in RCC. DDX11 could serve as a novel therapeutic biomarker for RCC patients who are refractory to conventional targeted therapies and immunotherapies.

Association between visceral adiposity and DDX11 as a predictor of aggressiveness of small clear-cell renal-cell carcinoma: a prospective clinical trial

Background

Visceral fat produces several hormones and cytokines associated with carcinogenesis and tumor progression. Herein, we investigated the association between visceral adiposity and target-gene mRNA expression in patients with localized small clear-cell renal-cell carcinoma (ccRCC).

Methods

We included 200 patients with localized clinical T1a stage ccRCC who had undergone nephrectomy from November 2018 to November 2020 in a prospective clinical trial (NCT03694912). Visceral, subcutaneous, and total adipose tissue in these patients was measured via preoperative computerized tomography of the mid-third lumbar vertebra region. We then examined the association between adiposity and the mRNA levels of PBRM1, BAP1, SETD2, KDM5C, FOXC2, CLIP4, AQP1, DDX11, BAIAP2L1, and TMEM38B in matched frozen tumor tissues and plasma samples.

Results

Upon the stratification of patients into quartiles according to their relative visceral adiposity, high visceral adiposity was found to be significantly associated with low ISUP grade (P = 0.004). Multivariate logistic regression analysis revealed a significant association between frozen tissue DDX11 expression and high visceral adiposity (OR 0.676, 95% CI 0.587–0.779, P < 0.001). Moreover, frozen tissue DDX11 expression was significantly associated with high ISUP grade (OR 1.556, 95% CI 1.223–1.981, P < 0.001). The frozen tissue mRNA expression of DDX11 was identified as a biomarker for visceral adiposity and cancer aggressiveness.

Conclusions

The results obtained herein will aid in inferring the aggressiveness of small ccRCCs, represented by ISUP nuclear grade, in clinical practice. Our findings indicated that DDX11 and visceral fat play active roles in small ccRCC. These roles should be examined in future studies for the possible use of DDX11 and visceral fat as prognostic biomarkers in the treatment of patients with ccRCC.

Trial registration

ClinicalTrials.gov, NCT03694912, Registered 3 October 2018.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40170-021-00251-y.

Combining UBR5 and CD163+ tumor-associated macrophages better predicts prognosis of clear cell renal cell carcinoma patients

PURPOSE

Identification of reliable postoperative indicators for accurately evaluating prognosis of clear cell renal cell carcinoma (ccRCC) patients remains an important clinical issue. This study determined the prognostic value of UBR5 expression in ccRCC patients by combining with CD163⁺ tumor-associated macrophages (TAMs) and the established clinical parameters.

METHODS

The expression of UBR5 was analyzed in ccRCC patients from TCGA databases. A total of 310 ccRCC patients were randomly divided into the training and validation cohorts at a 3:2 or 1:1 ratio, and immunohistochemistry (IHC) and statistical analyses were performed to examine the prognostic value of UBR5 and CD163⁺ TAMs.

RESULTS

UBR5 expression was commonly downregulated in human ccRCC specimens, which was associated with TNM stage, SSIGN, WHO/ISUP Grading and poor prognosis of ccRCC patients. In addition, UBR5 expression was negatively correlated with CD163 expression (a TAM marker) in ccRCC tissues, and combining expressions of UBR5 and CD163 better predicted worse overall survival and progression-free survival of ccRCC patients. Even after multivariable adjustment, UBR5, CD163, TNM stage and SSIGN appeared to be independent risk factors. By time-dependent c-index analysis, the integration of intratumoral UBR5 and CD163 achieved higher c-index value than UBR5, CD163, TNM stage or SSIGN alone in predicting ccRCC patients' prognosis. Moreover, the incorporation of both UBR5 and CD163 into the clinical indicators TNM stage or SSIGN exhibited highest c-index value.

CONCLUSIONS

Integrating intratumoral UBR5 and CD163⁺ TAMs with the current clinical parameters achieves better accuracy in predicting ccRCC patients' postoperative prognosis.

CLIP4 Shows Putative Tumor Suppressor Characteristics in Breast Cancer: An Integrated Analysis

Background: CAP-Gly domain containing linker protein family member 4 (CLIP4) plays an important role in cancers. However, its expression, prognostic value, and biological effect in breast cancer remain unclear. Methods: Data on patients diagnosed with breast cancer were retrieved from the TCGA-BRCA and other public omics databases. The expression profile of CLIP4 was analyzed using Oncomine, bc-GenExMiner, and TCGA. The prognostic value of CLIP4 was determined by Kaplan-Meier Plotter and Human Protein Atlas. Identification of genes co-expressed with CLIP4 and potential mechanism analyses were performed using UALCAN, STRING, Metascape, and GSEA. The epigenetic characteristics of CLIP4 were determined by DiseaseMeth and MEXPRESS. Results: CLIP4 was downregulated and its expression was negatively correlated with estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor type 2 (HER2) status, Nottingham prognostic index (NPI), and Scarff-Bloom-Richardson (SBR) grade in breast cancer, whereas it was positively linked to basal-like and triple negative breast cancer status. Ectopic expression of CLIP4 was related with poor prognosis. In the analysis of genes co-expressed with CLIP4, GSEA showed that the Hedgehog (Hh), JAK-STAT, ERBB, Wnt signaling pathway, cell adhesion molecules, and pathways in cancer were dissimilarly enriched in the CLIP4 expression high phenotype. Analysis of the genetics and epigenetics of CLIP4 indicated that its expression was negatively correlated with DNA methylation. Conclusion: Methylated CLIP4 may be a novel prognostic and therapeutic biomarker for breast cancer.

Super-Enhancer Associated Five-Gene Risk Score Model Predicts Overall Survival in Multiple Myeloma Patients

Multiple myeloma (MM) is a malignant plasma cell tumor with high heterogeneity, characterized by anemia, hypercalcemia, renal failure, and lytic bone lesions. Although various powerful prognostic factors and models have been exploited, the development of more accurate prognosis and treatment for MM patients is still facing many challenges. Given the essential roles of super-enhancer (SE) associated genes in the tumorigenesis of MM, we tried to initially screen and identify the significant prognostic factors from SE associated genes in MM by the least absolute shrinkage and selection operator (Lasso) penalized Cox regression, univariate and multivariate Cox regression analysis using GSE24080 and GSE9782 datasets. Risk score model of five genes including CSGALNACT1, FAM53B, TAPBPL, REPIN1, and DDX11, was further constructed and the Kaplan-Meier (K-M) curves showed that the low-risk group seems to have better clinical outcome of survival compared to the high-risk group. Time-dependent receiver operating characteristic (ROC) curves presented the favorable performance of the model. An interactive nomogram consisting of the five-gene risk group and eleven clinical traits was established and identified by calibration curves. Therefore, the risk score model of SE associated five genes developed here could be used to predict the prognosis of MM patients, which may assist the clinical treatment of MM patients in the future.

T1 Stage Clear Cell Renal Cell Carcinoma: A CT-Based Radiomics Nomogram to Estimate the Risk of Recurrence and Metastasis

Objectives

To develop and validate a radiomics nomogram to improve prediction of recurrence and metastasis risk in T1 stage clear cell renal cell carcinoma (ccRCC).

Methods

This retrospective study recruited 168 consecutive patients (mean age, 53.9 years; range, 28–76 years; 43 women) with T1 ccRCC between January 2012 and June 2019, including 50 aggressive ccRCC based on synchronous metastasis or recurrence after surgery. The patients were divided into two cohorts (training and validation) at a 7:3 ratio. Radiomics features were extracted from contrast enhanced CT images. A radiomics signature was developed based on reproducible features by means of the least absolute shrinkage and selection operator method. Demographics, laboratory variables (including sex, age, Fuhrman grade, hemoglobin, platelet, neutrophils, albumin, and calcium) and CT findings were combined to develop clinical factors model. Integrating radiomics signature and independent clinical factors, a radiomics nomogram was developed. Nomogram performance was determined by calibration, discrimination, and clinical usefulness.

Results

Ten features were used to build radiomics signature, which yielded an area under the curve (AUC) of 0.86 in the training cohort and 0.85 in the validation cohort. By incorporating the sex, maximum diameter, neutrophil count, albumin count, and radiomics score, a radiomics nomogram was developed. Radiomics nomogram (AUC: training, 0.91; validation, 0.92) had higher performance than clinical factors model (AUC: training, 0.86; validation, 0.90) or radiomics signature as a means of identifying patients at high risk for recurrence and metastasis. The radiomics nomogram had higher sensitivity than clinical factors mode (McNemar’s chi-squared = 4.1667, p = 0.04) and a little lower specificity than clinical factors model (McNemar’s chi-squared = 3.2, p = 0.07). The nomogram showed good calibration. Decision curve analysis demonstrated the superiority of the nomogram compared with the clinical factors model in terms of clinical usefulness.

Conclusion

The CT-based radiomics nomogram could help in predicting recurrence and metastasis risk in T1 ccRCC, which might provide assistance for clinicians in tailoring precise therapy.

Prediction of High-Grade Clear Cell Renal Cell Carcinoma Based on Plasma mRNA Profiles in Patients with Localized Pathologic T1N0M0 Stage Disease

A high nuclear grade is crucial to predicting tumor recurrence and metastasis in clear cell renal cell carcinomas (ccRCCs). We aimed to compare the mRNA profiles of tumor tissues and preoperative plasma in patients with localized T1 stage ccRCCs, and to evaluate the potential of the plasma mRNA profile for predicting high-grade ccRCCs. Data from a prospective cohort (n = 140) were collected between November 2018 and November 2019. Frozen tumor tissues and plasma were used to measure PBRM1, BAP1, SET domain-containing 2 (SETD2), KDM5C, FOXC2, CLIP4, AQP1, DDX11, BAIAP2L1, and TMEM38B mRNA levels, and correlation with the Fuhrman grade was investigated. Multivariate logistic regression analysis revealed significant association between high-grade ccRCC and SETD2 and DDX11 mRNA levels in tissues (odds ratio (b) = 0.021, 95% confidence interval (CI): 0.001-0.466, p = 0.014; b = 6.116, 95% CI: 1.729-21.631, p = 0.005, respectively) and plasma (b = 0.028, 95% CI 0.007-0.119, p < 0.001; b = 1.496, 95% CI: 1.187-1.885, p = 0.001, respectively). High-grade ccRCC prediction models revealed areas under the curve of 0.997 and 0.971 and diagnostic accuracies of 97.86% and 92.86% for the frozen tissue and plasma, respectively. SETD2 and DDX11 mRNA can serve as non-invasive plasma biomarkers for predicting high-grade ccRCCs. Studies with long follow-ups are needed to validate the prognostic value of these biomarkers in ccRCCs.