Whole-genome analysis of papillary kidney cancer finds significant noncoding alterations

Comprehensive prognostic assessment in kidney cancer: A multidimensional approach analyzing Fufang Sanling granules, genetic variants, and immune infiltration

This study delves into the potential therapeutic benefits of Fufang Sanling Granules for kidney cancer, focusing on their active components and the underlying mechanisms of their interaction with cancer-related targets. By constructing a drug-active component-target network based on eight herbs, key active compounds such as kaempferol, quercetin, and linolenic acid were identified, suggesting their pivotal roles in modulating immune responses and cellular signaling pathways relevant to cancer progression. The research further identified 51 central drug-disease genes through comprehensive bioinformatics analyses, implicating their involvement in crucial biological processes and pathways. A novel risk score model, encompassing six genes with significant prognostic value for renal cancer, was established and validated, showcasing its effectiveness in predicting patient outcomes through mutation analysis and survival studies. The model's predictive power was further confirmed by its ability to stratify patients into distinct risk groups with significant survival differences, highlighting its potential as a prognostic tool. Additionally, the study explored the relationship between gene expression within the identified black module and the risk score, uncovering significant associations with the extracellular matrix and immune infiltration patterns. This reveals the complex interplay between the tumor microenvironment and cancer progression. The integration of the risk score with clinical parameters through a nomogram significantly improved the model's predictive accuracy, offering a more comprehensive tool for predicting kidney cancer prognosis. In summary, by combining detailed molecular analyses with clinical insights, this study presents a robust framework for understanding the therapeutic potential of Fufang Sanling Granules in kidney cancer. It not only sheds light on the active components and their interactions with cancer-related genes but also introduces a reliable risk score model, paving the way for personalized treatment strategies and improved patient management in the future.

Long noncoding RNA MEG8 induces an imbalance of Th17/Treg cells through the miR-107/STAT3 axis in Henoch-Schonlein purpura rats

T-helper (Th) 17/ T-regulatory (Treg) cell dysregulation underlies the pathogenesis of Henoch-Schonlein purpura (HSP). This research focused on the implication/s of the long noncoding RNA (lncRNAs) maternally expressed gene 8 (MEG8) in Th17 and Treg cell differentiation in HSP rats. MEG8, miR-107, signal transducer and activator of transcription-3 (STAT3), receptor-related orphan receptor γt (RORγt), and the transcription factor forkhead box P3 (Foxp3) expression levels were detected using real-time quantitative polymerase chain reaction and Western blot analyses. Flow cytometry was employed for measuring Th17 and Treg cells within the CD4+ T cell population. The interaction between miR-107 and MEG8 or STAT3 was examined. A low proportion of MEG8 and Treg cells together with Th17 cells were denoted within HSP rats. Moreover, MEG8 overexpression altered the Th17/Treg imbalance in peripheral blood CD4+ T-cell population, and the miR-107 mimic and STAT3 silencing reversed this effect. Thus, MEG8 served as a sponge for miR-107, lowering binding activity to STAT3 and thus overexpressing the molecule. Taken together, MEG8 induces an imbalance of Th17/Treg cells through the miR-107/STAT3 axis in HSP rats.

Noncoding RNA mutations in cancer

Cancer is driven by both germline and somatic genetic changes. Efforts have been devoted to characterizing essential genetic variations in cancer initiation and development. Most attention has been given to mutations in protein-coding genes and associated regulatory elements such as promoters and enhancers. The development of sequencing technologies and in silico and experimental methods has allowed further exploration of cancer predisposition variants and important somatic mutations in noncoding RNAs, mainly for long noncoding RNAs and microRNAs. Association studies including GWAS have revealed hereditary variations including SNPs and indels in lncRNA or miRNA genes and regulatory regions. These mutations altered RNA secondary structures, expression levels, and target recognition and then conferred cancer predisposition to carriers. Whole-exome/genome sequencing comparing cancer and normal tissues has revealed important somatic mutations in noncoding RNA genes. Mutation hotspots and somatic copy number alterations have been identified in various tumor-associated noncoding RNAs. Increasing focus and effort have been devoted to studying the noncoding region of the genome. The complex genetic network of cancer initiation is being unveiled. This article is categorized under: RNA in Disease and Development > RNA in Disease.

The HDAC2-MTA3 interaction induces nonsmall cell lung cancer cell migration and invasion by targeting c-Myc and cyclin D1

Genome-wide association studies have identified numerous single-nucleotide polymorphisms (SNPs) associated with lung cancer; however, the functions of histone deacetylase 2 (HDAC2) rs13213007 and HDAC2 in nonsmall cell lung cancer (NSCLC) remain unclear. Here we identified HDAC2 rs13213007 as a risk SNP and showed that HDAC2 was upregulated in both peripheral blood mononuclear cells (PBMCs) and NSCLC tissues with the rs13213007 A/A genotype compared with those with the rs13213007 G/G or G/A genotype. Patient clinical data indicated strong associations between rs13213007 genotype and N classification. Immunohistochemical staining confirmed that higher expression of HDAC2 was associated with NSCLC progression. Furthermore, we generated 293T cells with the rs13213007 A/A genotype using CRISPR (clustered regularly interspaced short palindromic repeats)/Cas9 gene editing. Chromatin immunoprecipitation sequencing followed by motif analysis showed that HDAC2 can bind to c-Myc in rs13213007 A/A 293T cells. Cell Counting Kit-8, colony formation, wound-healing, and Transwell assays revealed that HDAC2 upregulates c-Myc and cyclin D1 expression and promotes NSCLC cell proliferation, migration, and invasion. Co-immunoprecipitation, quantitative reverse transcription-polymerase chain reaction, and western blot analysis assays showed that MTA3 interacts with HDAC2, decreases HDAC2 expression, and rescues the migration and invasion abilities of NSCLC cells. Taken together, these findings identify HDAC2 as a potential therapeutic biomarker in NSCLC.

The roles of long non‑coding RNAs in renal cell carcinoma (Review)

Long non-coding RNAs (lncRNAs) are involved in the gene expression regulation and usually play important roles in various human cancers, including the renal cell carcinoma (RCC). Dysregulation of certain lncRNAs are associated with the prognosis of patients with RCC. In the present review, several recently studied lncRNAs were discussed and their critical roles in proliferation, migration, invasion, apoptosis and drug resistance of renal cancer cells were revealed. The research on lncRNAs further increases our understanding on the development and progression of RCC. It is suggested that lncRNAs can be used as biomarkers or therapeutic targets for diagnosis or treatment of renal cancer.

NEAT1 can be a diagnostic biomarker in the breast cancer and gastric cancer patients by targeting XIST, hsa-miR-612, and MTRNR2L8: integrated RNA targetome interaction and experimental expression analysis

Background

The most frequent malignancy in women is breast cancer (BC). Gastric cancer (GC) is also the leading cause of cancer-related mortality. Long non-coding RNAs (lncRNAs) are thought to be important neurotic regulators in malignant tumors. In this study, we aimed to evaluate the expression level of NEAT1 and the interaction of this non-coding RNA with correlated microRNAs, lncRNAs, and mRNAs or protein coding genes, experimentally and bioinformatically.

Methods

For the bioinformatics analyses, we performed RNA-RNA and protein–protein interaction analyses, using ENCORI and STRING. The expression analyses were performed by five tools: Microarray data analysis, TCGA data analysis (RNA-seq, R Studio), GEPIA2, ENCORI, and real-time PCR experiment. qRT-PCR experiment was performed on 50 GC samples and 50 BC samples, compared to adjacent control tissue.

Results

Based on bioinformatics and experimental analyses, lncRNA NEAT1 have a significant down-regulation in the breast cancer samples with tumor size lower than 2 cm. Also, it has a significant high expression in the gastric cancer patients. Furthermore, NEAT1 have a significant interaction with XIST, hsa-miR-612 and MTRNR2L8. High expression of NEAT1 have a correlation with the lower survival rate of breast cancer samples and higher survival rate of gastric cancer patients.

Conclusion

This integrated computational and experimental investigation revealed some new aspects of the lncRNA NEAT1 as a potential prognostic biomarker for the breast cancer and gastric cancer samples. Further investigations about NEA1 and correlated mRNAs, lncRNAs, and microRNAs – specially the mentioned RNAs in this study – can lead the researchers to more clear information about the role of NEAT1 in the breast cancer and gastric cancer.

Non-coding driver mutations in human cancer

Tumour formation involves random mutagenic events and positive evolutionary selection acting on a subset of such events, referred to as driver mutations. A decade of careful surveying of tumour DNA using exome-based analyses has revealed a multitude of protein-coding somatic driver mutations, some of which are clinically actionable. Today, a transition towards whole-genome analysis is well under way, technically enabling the discovery of potential driver mutations occurring outside protein-coding sequences. Mutations are abundant in this vast non-coding space, which is more than 50 times larger than the coding exome, but reliable identification of selection signals in non-coding DNA remains a challenge. In this Review, we discuss recent findings in the field, where the emerging landscape is one in which non-coding driver mutations appear to be relatively infrequent. Nevertheless, we highlight several notable discoveries. We consider possible reasons for the relative absence of non-coding driver events, as well as the difficulties associated with detecting signals of positive selection in non-coding DNA.

Whole-genome sequencing of phenotypically distinct inflammatory breast cancers reveals similar genomic alterations to non-inflammatory breast cancers

BACKGROUND

Inflammatory breast cancer (IBC) has a highly invasive and metastatic phenotype. However, little is known about its genetic drivers. To address this, we report the largest cohort of whole-genome sequencing (WGS) of IBC cases.

METHODS

We performed WGS of 20 IBC samples and paired normal blood DNA to identify genomic alterations. For comparison, we used 23 matched non-IBC samples from the Cancer Genome Atlas Program (TCGA). We also validated our findings using WGS data from the International Cancer Genome Consortium (ICGC) and the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We examined a wide selection of genomic features to search for differences between IBC and conventional breast cancer. These include (i) somatic and germline single-nucleotide variants (SNVs), in both coding and non-coding regions; (ii) the mutational signature and the clonal architecture derived from these SNVs; (iii) copy number and structural variants (CNVs and SVs); and (iv) non-human sequence in the tumors (i.e., exogenous sequences of bacterial origin).

RESULTS

Overall, IBC has similar genomic characteristics to non-IBC, including specific alterations, overall mutational load and signature, and tumor heterogeneity. In particular, we observed similar mutation frequencies between IBC and non-IBC, for each gene and most cancer-related pathways. Moreover, we found no exogenous sequences of infectious agents specific to IBC samples. Even though we could not find any strongly statistically distinguishing genomic features between the two groups, we did find some suggestive differences in IBC: (i) The MAST2 gene was more frequently mutated (20% IBC vs. 0% non-IBC). (ii) The TGF β pathway was more frequently disrupted by germline SNVs (50% vs. 13%). (iii) Different copy number profiles were observed in several genomic regions harboring cancer genes. (iv) Complex SVs were more frequent. (v) The clonal architecture was simpler, suggesting more homogenous tumor-evolutionary lineages.

CONCLUSIONS

Whole-genome sequencing of IBC manifests a similar genomic architecture to non-IBC. We found no unique genomic alterations shared in just IBCs; however, subtle genomic differences were observed including germline alterations in TGFβ pathway genes and somatic mutations in the MAST2 kinase that could represent potential therapeutic targets.

lncRNA NEAT1 ameliorates LPS‑induced inflammation in MG63 cells by activating autophagy and suppressing the NLRP3 inflammasome

The mechanisms of inflammation in bone and joint tissue are complex and involve long non‑coding RNAs (lncRNAs), which play an important role in this process. The aim of the present study was to screen out differentially expressed genes in human osteoblasts stimulated by inflammation, and to further explore the mechanisms underlying inflammatory responses and the functional activity of human osteoblasts through bioinformatics methods and in vitro experiments. For this purpose, MG63 cells were stimulated with various concentrations of lipopolysaccharide (LPS) for different periods of time to construct an optimal inflammatory model and RNA sequencing was then performed on these cells. The levels of nuclear enriched abundant transcript 1 (NEAT1), various inflammatory factors, Nod‑like receptor protein 3 (NLRP3) protein and osteogenesis‑related proteins, as well as the levels of cell apoptosis‑ and cell cycle‑related markers were measured in MG63 cells stimulated with LPS, transfected with NEAT1 overexpression plasmid and treated with bexarotene by western blot analysis, RT‑qPCR, immunofluorescence, FISH, TEM and flow cytometry. There were 427 differentially expressed genes in the LPS‑stimulated MG63 cells, in which NEAT1 was significantly downregulated. LPS upregulated the expression of inflammatory cytokines and NLRP3, inhibited the expression of autophagy‑related and osteogenesis‑related proteins, promoted apoptosis and altered the cell cycle, which was partially inhibited by NEAT1 overexpression and promoted by bexarotene. LPS stimulated inflammation in the MG63 cells and inhibited the retinoid X receptor (RXR)‑α to downregulate the expression of NEAT1 and decrease levels of autophagy, which promoted the activation of NLRP3 and the release of inflammatory factors, and impaired the functional activity of osteoblasts, thus promoting the development of inflammation.

Development of an Autophagy Score Signature for Predicting Overall Survival in Papillary Renal Cell Carcinoma

Background

Autophagy is considered to be closely associated with cancer, functioning as either an anticancer or procancer mechanism depending on the cancer stage. However, the prognostic value of autophagy on papillary renal cell carcinoma (pRCC) remains unclear. In this study, our purpose was to determine the autophagy-related mRNA signature to predict the overall survival of patients with pRCC.

Materials and Methods

A total of 284 patients with pathologic confirmed pRCC in The Cancer Genome Atlas (TCGA) dataset were recruited and included. We choose patients who have smoked less than 15 years but staging 3 or 4 (including nontobacco exposure) vs. more than 15 years but staging 1 or 2. Fourteen differentially expressed mRNAs were found with fold change > 2 and P value < 0.001 through limma package after making a pair between nontobacco exposure or less than 15 years and tobacco exposure more than 15 years by matchIt package.

Results

Six mRNAs were identified to be significantly associated with overall survival. Then, using a risk score based on the signature of these six mRNAs, we divided the patients into low-risk and high-risk groups with significantly different OS. Further multivariate Cox regression analyses revealed that the 6-mRNA signature was independent of age, TNM stage, and tumor type. In the present study, a novel 6-mRNA signature that is useful in survival prediction in pRCC patients was developed. If validated, this mRNA signature might assist in selecting high-risk subpopulation that needs more aggressive therapeutic intervention. The risk score involved in several cancer-related pathways was identified using gene set enrichment analysis.

Conclusion

We initially generated a six autophagy-related genes' signature, which correlates with AJCC N stage, tumor type, and pathological stage and independently predicts OS.

Discovery of driver non-coding splice-site-creating mutations in cancer

Non-coding mutations can create splice sites, however the true extent of how such somatic non-coding mutations affect RNA splicing are largely unexplored. Here we use the MiSplice pipeline to analyze 783 cancer cases with WGS data and 9494 cases with WES data, discovering 562 non-coding mutations that lead to splicing alterations. Notably, most of these mutations create new exons. Introns associated with new exon creation are significantly larger than the genome-wide average intron size. We find that some mutation-induced splicing alterations are located in genes important in tumorigenesis (ATRX, BCOR, CDKN2B, MAP3K1, MAP3K4, MDM2, SMAD4, STK11, TP53 etc.), often leading to truncated proteins and affecting gene expression. The pattern emerging from these exon-creating mutations suggests that splice sites created by non-coding mutations interact with pre-existing potential splice sites that originally lacked a suitable splicing pair to induce new exon formation. Our study suggests the importance of investigating biological and clinical consequences of noncoding splice-inducing mutations that were previously neglected by conventional annotation pipelines. MiSplice will be useful for automatically annotating the splicing impact of coding and non-coding mutations in future large-scale analyses.

c-Met expression in renal cell carcinoma with bone metastases

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Bone is a common metastatic site in renal cell carcinoma (RCC).

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HGF/c-Met pathway is particularly relevant in tumors with bone metastases.

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c-Met/HGF pathway is involved in RCC progression, conferring poor prognosis.

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Several c-Met targeting therapies are currently in clinical development.

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c-Met expression is an important therapeutic target in RCC with bone metastases.

Hepatocyte growth factor (HGF)/c-Met pathway is implicated in embryogenesis and organ development and differentiation. Germline or somatic mutations, chromosomal rearrangements, gene amplification, and transcriptional upregulation in MET or alterations in autocrine or paracrine c-Met signalling have been associated with cancer cell proliferation and survival, including in renal cell carcinoma (RCC), and associated with disease progression. HGF/c-Met pathway has been shown to be particularly relevant in tumors with bone metastases (BMs). However, the efficacy of targeting c-Met in bone metastatic disease, including in RCC, has not been proven. Therefore, further investigation is required focusing the particular role of HGF/c-Met pathway in bone microenvironment (BME) and how to effectively target this pathway in the context of bone metastatic disease.

A SNP-mediated lncRNA (LOC146880) and microRNA (miR-539-5p) interaction and its potential impact on the NSCLC risk

BACKGROUND

Many cancer-associated single nucleotide polymorphisms (SNPs) are located in the genomic regions of long non-coding RNAs (lncRNAs). Mechanisms of these SNPs in connection to cancer risk are not fully understood.

METHODS

Association of SNP (rs140618127) in lncRNA LOC146880 with non-small cell lung cancer (NSCLC) was evaluated in a case-control study of 2707 individuals. The mechanism of the SNP's biologic influence was explored with in vitro and in vivo experiments, including plasmid transfection, siRNA knockdown, flow cytometry assessment, and assays of cell proliferation, migration, invasion, and colony formation.

RESULTS

Association analysis showed that A allele of SNP rs140618127 was associated with low risk of NSCLC in the Chinese population. Lab experiments indicated that SNP rs140618127 contained a binding site for miR-539-5p and the binding between miR-539-5p and LOC146880 resulted in declined phosphorylation of an oncogene, ENO1. The reduced phosphorylation of ENO1 led to decreased phosphorylation of PI3K and Akt, which is further linked to the decline in cell proliferation and tumor progression.

CONCLUSION

The study demonstrates that SNP rs140618127 in lncRNA loc146880 provides an alternate binding site for microRNA miR-539-5p which affects the phosphorylation of ENO1 and activation of the PI3K and Akt pathway.

Genetic Alterations in Renal Cancers: Identification of The Mechanisms Underlying Cancer Initiation and Progression and of Therapeutic Targets

Renal cell cancer (RCC) involves three most recurrent sporadic types: clear-cell RCC (70-75%, CCRCC), papillary RCCC (10-15%, PRCC), and chromophobe RCC (5%, CHRCC). Hereditary cases account for about 5% of all cases of RCC and are caused by germline pathogenic variants. Herein, we review how a better understanding of the molecular biology of RCCs has driven the inception of new diagnostic and therapeutic approaches. Genomic research has identified relevant genetic alterations associated with each RCC subtype. Molecular studies have clearly shown that CCRCC is universally initiated by Von Hippel Lindau (VHL) gene dysregulation, followed by different types of additional genetic events involving epigenetic regulatory genes, dictating disease progression, aggressiveness, and differential response to treatments. The understanding of the molecular mechanisms that underlie the development and progression of RCC has considerably expanded treatment options; genomic data might guide treatment options by enabling patients to be matched with therapeutics that specifically target the genetic alterations present in their tumors. These new targeted treatments have led to a moderate improvement of the survival of metastatic RCC patients. Ongoing studies based on the combination of immunotherapeutic agents (immune check inhibitors) with VEGF inhibitors are expected to further improve the survival of these patients.

Using sigLASSO to optimize cancer mutation signatures jointly with sampling likelihood

Multiple mutational processes drive carcinogenesis, leaving characteristic signatures in tumor genomes. Determining the active signatures from a full repertoire of potential ones helps elucidate mechanisms of cancer development. This involves optimally decomposing the counts of cancer mutations, tabulated according to their trinucleotide context, into a linear combination of known signatures. Here, we develop sigLASSO (a software tool at github.com/gersteinlab/siglasso) to carry out this optimization efficiently. sigLASSO has four key aspects: (1) It jointly optimizes the likelihood of sampling and signature fitting, by explicitly factoring multinomial sampling into the objective function. This is particularly important when mutation counts are low and sampling variance is high (e.g., in exome sequencing). (2) sigLASSO uses L1 regularization to parsimoniously assign signatures, leading to sparse and interpretable solutions. (3) It fine-tunes model complexity, informed by data scale and biological priors. (4) Consequently, sigLASSO can assess model uncertainty and abstain from making assignments in low-confidence contexts.

A cluster of long non-coding RNAs exhibit diagnostic and prognostic values in renal cell carcinoma

Kidney cancer ranked in the top 10 for both men and women in the estimated numbers of new cancer cases in the United States in 2018. Targeted therapies have recently been administered to patients with clear cell renal cell carcinoma (ccRCC), but the overall survival of patients at the terminal stage of the disease has not been as good as expected. It is therefore necessary to uncover efficient biomarkers for early diagnosis, and to clarify the molecular mechanisms underlying ccRCC progression and metastasis. Increased evidence has shown that long non-coding RNAs (lncRNAs) play important roles during tumor progression. In this study, 10 candidate lncRNAs with diagnostic and prognostic values in ccRCC were identified: IGFL2-AS1, AC023043.1, AP000439.2, AC124854.1, AL355102.4, TMEM246-AS1, AL133467.3, ZNF582-AS1, LINC01510 and PSMG3-AS1. Enrichment analysis revealed metabolic and functional pathways, which may be closely associated with kidney cancer tumorigenesis. Six representative processes were summarized, namely glycolysis, amino acid metabolism, lipid synthesis, reductive carboxylation, nucleotide metabolism, transmembrane transport and signal transduction. In combination, the present results provided prognostic and diagnostic biomarkers for ccRCC and might pave the way for targeted intervention and molecular therapies in the future.

Current Concepts of Non-Coding RNAs in the Pathogenesis of Non-Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) is a relatively rare malignancy of the urinary tract system. RCC is a heterogenous disease in terms of underlying histology and its associated underlying pathobiology, prognosis and treatment schedule. The most prevalent histological RCC subtype is clear-cell renal cell carcinoma (ccRCC), accounting for about 70-80% of all RCCs. Though the pathobiology and treatment schedule for ccRCC are well-established, non-ccRCC subtypes account for 20%-30% of RCC altogether, and their underlying molecular biology and treatment options are poorly defined. The class of non-coding RNAs-molecules that are generally not translated into proteins-are new cancer drivers and suppressors in all types of cancer. Of these, small non-coding microRNAs (miRNAs) contribute to carcinogenesis by regulating posttranscriptional gene silencing. Additionally, a growing body of evidence supports the role of long non-coding RNAs (lncRNAs) in cancer development and progression. Most studies on non-coding RNAs in RCC focus on clear-cell histology, and there is a relatively limited number of studies on non-ccRCC subtypes. The aim of this review is to give an overview of the current knowledge regarding the role of non-coding RNAs (including short and long non-coding RNAs) in non-ccRCC and to highlight possible implications as diagnostic, prognostic and predictive biomarkers.

Papillary Renal Cell Carcinomas Rewire Glutathione Metabolism and Are Deficient in Both Anabolic Glucose Synthesis and Oxidative Phosphorylation

Papillary renal cell carcinoma (pRCC) is a malignant kidney cancer with a prevalence of 7–20% of all renal tumors. Proteome and metabolome profiles of 19 pRCC and patient-matched healthy kidney controls were used to elucidate the regulation of metabolic pathways and the underlying molecular mechanisms. Glutathione (GSH), a main reactive oxygen species (ROS) scavenger, was highly increased and can be regarded as a new hallmark in this malignancy. Isotope tracing of pRCC derived cell lines revealed an increased de novo synthesis rate of GSH, based on glutamine consumption. Furthermore, profound downregulation of gluconeogenesis and oxidative phosphorylation was observed at the protein level. In contrast, analysis of the The Cancer Genome Atlas (TCGA) papillary RCC cohort revealed no significant change in transcripts encoding oxidative phosphorylation compared to normal kidney tissue, highlighting the importance of proteomic profiling. The molecular characteristics of pRCC are increased GSH synthesis to cope with ROS stress, deficient anabolic glucose synthesis, and compromised oxidative phosphorylation, which could potentially be exploited in innovative anti-cancer strategies.

lncRNASNP2: an updated database of functional SNPs and mutations in human and mouse lncRNAs

Long non-coding RNAs (lncRNAs) are emerging as important regulators in different biological processes through various ways. Because the related data, especially mutations in cancers, increased sharply, we updated the lncRNASNP to version 2 (http://bioinfo.life.hust.edu.cn/lncRNASNP2). lncRNASNP2 provides comprehensive information of SNPs and mutations in lncRNAs, as well as their impacts on lncRNA structure and function. lncRNASNP2 contains 7260238 SNPs on 141353 human lncRNA transcripts and 3921448 SNPs on 117405 mouse lncRNA transcripts. Besides the SNP information in the first version, the following new features were developed to improve the lncRNASNP2. (i) noncoding variants from COSMIC cancer data (859534) in lncRNAs and their effects on lncRNA structure and function; (ii) TCGA cancer mutations (315234) in lncRNAs and their impacts; (iii) lncRNA expression profiling of 20 cancer types in both tumor and its adjacent samples; (iv) expanded lncRNA-associated diseases; (v) optimized the results about lncRNAs structure change induced by variants; (vi) reduced false positives in miRNA and lncRNA interaction results. Furthermore, we developed online tools for users to analyze new variants in lncRNA. We aim to maintain the lncRNASNP as a useful resource for lncRNAs and their variants.

Glutathione Metabolism in Renal Cell Carcinoma Progression and Implications for Therapies

A significantly increased level of the reactive oxygen species (ROS) scavenger glutathione (GSH) has been identified as a hallmark of renal cell carcinoma (RCC). The proposed mechanism for increased GSH levels is to counteract damaging ROS to sustain the viability and growth of the malignancy. Here, we review the current knowledge about the three main RCC subtypes, namely clear cell RCC (ccRCC), papillary RCC (pRCC), and chromophobe RCC (chRCC), at the genetic, transcript, protein, and metabolite level and highlight their mutual influence on GSH metabolism. A further discussion addresses the question of how the manipulation of GSH levels can be exploited as a potential treatment strategy for RCC.

Challenges and opportunities in the proteomic characterization of clear cell renal cell carcinoma (ccRCC): A critical step towards the personalized care of renal cancers

Clear cell renal cell carcinoma (ccRCC) is the most common type of kidney cancer, comprising approximately 75% of all kidney tumors. Recent the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) studies have significantly advanced the molecular characterization of RCC and facilitated the development of targeted therapies. Such advances have improved the median survival of patients with advanced disease from less than 10 months prior to 2004 to 30 months by 2011. However, approximately 30% of localized ccRCC patients will nevertheless develop recurrence or metastasis after surgical resection of their tumor. Therefore, it is critical to further analyze potential tumor-associated proteins and their profiles during disease progression. Over the past decade, tremendous effort has been focused on the study of molecular pathways, including genomics, transcriptomics, and proteomics in order to identify potential molecular biomarkers, as well as to facilitate early detection, monitor tumor progression and uncover potentially therapeutic targets. In this review, we focus on recent advances in the proteomic analysis of ccRCC, current strategies and challenges, and perspectives in the field. This insight will highlight the discovery of tumor-associated proteins, and their potential clinical impact on personalized precision-based care in ccRCC.

The molecular characterization and therapeutic strategies of papillary renal cell carcinoma

Introduction: Papillary renal cell carcinoma (pRCC) is an important subtype of kidney cancer with a problematic pathological classification and highly variable clinical behavior. In this review, we summarize the current progression on pRCC in molecular level. Our findings highlight the need for molecular markers to accurately subtype pRCC and may lead to the development of more targeted agents and better patient stratification in clinical trials for pRCC. Areas covered: This review highlights the need for molecular markers to accurately subtype PRCC and may lead to the development of more targeted agents and better patient stratification in clinical trials for pRCC. Expert commentary: There are mainly two subtypes of pRCC based on histology. However, little is known about the genetic characterization of the sporadic forms of pRCC and there are currently no standard forms of therapy for patients with advanced disease. Both MET inhibitors and immunotherapy may be effective in advanced pRCC treatment. Therefore, understanding the molecular basis of pRCC and identifying the main goal of treatment is crucial for the selection of the best strategy.

Non-Clear Cell Renal Cell Carcinomas: From Shadow to Light

Non-clear cell renal cell carcinomas (RCCs) account for up to 25% of kidney cancers and encompass distinct diseases with distinct pathologic features, different molecular alterations, and various patterns of response to systemic therapies. Recent advances in molecular biology and large collaborative efforts helped to better define the oncogenic mechanisms at play in papillary, chromophobe, collecting duct, medullary, translocation, and sarcomatoid RCCs. Papillary RCCs are divided into several subsets of tumors characterized by distinct gene expression profiles, chromatin remodeling genes, cell cycle changes, and alterations of the MET pathway. Chromophobe RCC genomic analysis revealed mostly metabolic pathway alterations with mitochondrial dysfunctions. Translocation RCCs are characterized by MITF fusions and wide genomic reprogramming. Collecting duct carcinomas are distinct entities from upper tract urothelial carcinomas associated with high T-cell infiltration and metabolic alterations. Medullary RCCs present alterations of the INI1 gene and rhabdoid features at pathologic analysis. Finally, sarcomatoid RCCs represent sarcomatoid differentiation for any subsets of RCCs with specific alterations associated with mesenchymal dedifferentiation. From the standpoint of systemic therapy, more than a decade of using VEGF and mTOR inhibitors showed that they generally had limited efficacy in non-clear cell RCCs compared with clear cell RCCs. MET inhibitors are actively being developed for papillary RCC with a specific focus on MET-driven tumors. Other strategies under investigation include CDK4/6 inhibitors in tumors with cell cycle alterations and EZH2 inhibitors in RCCs with INI1 loss. The emergence of immune checkpoint inhibitors and combination strategies enlarges the spectrum of investigational treatments. Better understanding of driver and passenger alterations and better patient stratification along with dedicated clinical networks will be key to improving the management of these rare tumors.

Long Non-coding RNA NEAT1: A Novel Target for Diagnosis and Therapy in Human Tumors

The nuclear paraspeckle assembly transcript 1 (NEAT1, a long non-coding RNA) is frequently overexpressed in human tumors, and higher NEAT1 expression is correlated with worse survival in cancer patients. NEAT1 drives tumor initiation and progression by modulating the expression of genes involved in the regulation of tumor cell growth, migration, invasion, metastasis, epithelial-to-mesenchymal transition, stem cell-like phenotype, chemoresistance and radioresistance, indicating the potential for NEAT1 to be a novel diagnostic biomarker and therapeutic target. Mechanistically, NEAT1 functions as a scaffold RNA molecule by interacting with EZH2 (a subunit of the polycomb repressive complex) to influence the expression of downstream effectors of EZH2, it also acts as a microRNA (miRNA) sponge to suppress the interactions between miRNAs and target mRNAs, and affects the expression of miR-129 by promoting the DNA methylation of the miR-129 promoter region. Knockdown of NEAT1 via small interfering RNA or short hairpin RNA inhibits the malignant behavior of tumor cells. In this review, we highlight the latest insights into the expression pattern, biological roles and mechanisms underlying the function and regulation of NEAT1 in tumors, and especially focus on its clinical implication as a new diagnostic biomarker and an attractive therapeutic target for cancers.

Integrated analysis of differentially expressed profiles and construction of a competing endogenous long non-coding RNA network in renal cell carcinoma

Background

Long non-coding RNAs (lncRNAs) play crucial roles in the initiation and progression of renal cell carcinoma (RCC) by competing in binding to miRNAs, and related competitive endogenous RNA (ceRNA) networks have been constructed in several cancers. However, the coexpression network has been poorly explored in RCC.

Methods

We collected RCC RNA expression profile data and relevant clinical features from The Cancer Genome Atlas (TCGA). A cluster analysis was explored to show different lncRNA expression patterns. Gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses and gene set enrichment analysis (GSEA) were performed to analyze the functions of the intersecting mRNAs. Targetscan and miRanda bioinformatics algorithms were used to predict potential relationships among RNAs. Univariate Cox proportional hazards regression was conducted to determine the RNA expression levels and survival times.

Results

Bioinformatics analysis revealed that the expression profiles of hundreds of aberrantly expressed lncRNAs, miRNAs, and mRNAs were significantly changed between different stages of tumors and non-tumor groups. By combining the data predicted by databases with intersection RNAs, a ceRNA network consisting of 106 lncRNAs, 26 miRNAs and 69 mRNAs was established. Additionally, a protein interaction network revealed the main hub nodes (VEGFA, NTRK2, DLG2, E2F2, MYB and RUNX1). Furthermore, 63 lncRNAs, four miRNAs and 31 mRNAs were significantly associated with overall survival.

Conclusion

Our results identified cancer-specific lncRNAs and constructed a ceRNA network for RCC. A survival analysis related to the RNAs revealed candidate biomarkers for further study in RCC.

Pan-cancer analysis of long non-coding RNA NEAT1 in various cancers

Changes in the abundance and activity of long non-coding RNAs (lncRNAs) have an important impact on the development of cancer. The nuclear paraspeckle assembly transcript 1 (NEAT1) has been reported to be overexpressed in many types of cancer since its discovery. However, inconsistencies exist as NEAT1 can also function as a tumor suppressor in certain types of cancer, such as acute promyelocytic leukemia. Here we systematically describe our current understanding of NEAT1 in tumor initiation and progression. First, we analyzed the expression patterns of NEAT1 in various normal tissues and malignant cancers using data from public data portals, the Genotype-Tissue Expression Project (GTEx) and the Cancer Genome Atlas (TCGA), together with recent progress in the study of NEAT1 in various types of cancer. Second, we discussed the functions and mechanisms of NEAT1 in modulating tumor activity. Then, the upstream transcription factors and downstream microRNA targets of NEAT1 in the transcription cascade of cancers were also summarized. These data highlight the emerging role of NEAT1 in tumorigenesis, and present promising targetable pathways and clinical opportunities for tumor prevention and classifications.