A Panel of Four-lncRNA Signature as a Potential Biomarker for Predicting Survival in Clear Cell Renal Cell Carcinoma

Leveraging a Genomic Instability-Derived Signature to Predict the Prognosis and Therapy Sensitivity of Clear Cell Renal Cell Carcinoma

BACKGROUND

Kidney cancer is a significant health concern with growing treatment resistance, often linked to genomic instability. This study used datasets from 72 renal and 952 clear cell renal cell carcinoma samples to identify genomic instability-derived lncRNAs and develop a prognostic index (GILPI).

METHODS

The study involved differential expression analysis, weighted gene co-expression network analysis, Cox analyses to construct GILPI, and its validation through survival analysis. SNP, TMB, and MSI data were integrated, and GSEA analysis explored associated pathways. A predictive nomogram was created, and immune cell infiltration was assessed. Targeted treatments for low-GILPI patients were identified through molecular docking and network pharmacology.

RESULTS

GILPI proved reliable in predicting prognosis (P<0.001, AUC=0.68) and in combination with other factors. GSEA revealed distinct pathway enrichments for different GILPI subgroups. The nomogram exhibited strong predictive performance (AUC=0.902). Immune cell differences suggest potential for immunotherapy in high-GILPI patients and targeted treatment in low-GILPI patients. Lapatinib and nilotinib were identified as effective drugs for low-GILPI patients.

CONCLUSION

This study identified a GILPI for kidney cancer prognosis, integrating various factors for a comprehensive assessment. It highlighted potential treatment strategies based on GILPI subgroups, enhancing personalized treatment approaches.

Biomarkers in renal cell carcinoma and their targeted therapies: a review

Renal cell carcinoma (RCC) is one of the most life-threatening urinary malignancies displaying poor response to radiotherapy and chemotherapy. Although in the recent past there have been tremendous advancements in using targeted therapies for RCC, despite that it remains the most lethal urogenital cancer with a 5-year survival rate of roughly 76%. Timely diagnosis is still the key to prevent the progression of RCC into metastatic stages as well as to treat it. But due to the lack of definitive and specific diagnostic biomarkers for RCC and its asymptomatic nature in its early stages, it becomes very difficult to diagnose it. Reliable and distinct molecular markers can not only refine the diagnosis but also classifies the tumors into thier sub-types which can escort subsequent management and possible treatment for patients. Potential biomarkers can permit a greater degree of stratification of patients affected by RCC and help tailor novel targeted therapies. The review summarizes the most promising epigenetic [DNA methylation, microRNA (miRNA; miR), and long noncoding RNA (lncRNA)] and protein biomarkers that have been known to be specifically involved in diagnosis, cancer progression, and metastasis of RCC, thereby highlighting their utilization as non-invasive molecular markers in RCC. Also, the rationale and development of novel molecular targeted drugs and immunotherapy drugs [such as tyrosine kinase inhibitors and immune checkpoint inhibitors (ICIs)] as potential RCC therapeutics along with the proposed implication of these biomarkers in predicting response to targeted therapies will be discussed.

LncRNA PVT1 promotes strong stemness and endothelial progenitor cell characteristics in renal carcinoma stem cells

Renal cancer stem cells (RCSCs) derived from clear cell renal cell carcinoma (ccRCC) tissues with higher microvessel density (MVD) have strong stemness and endothelial progenitor cells-like (EPCs-like) characteristics. A high level of lncRNA PVT1 expression is essential for simultaneously retaining strong RCSC stemness and EPCs-like characteristics. PVT1 binds with TAZ protein and prevents its phosphorylation, which promotes RCSC stemness. Moreover, RCSCs support endothelial differentiation and angiogenesis, which are mediated via the PVT1/miR-15b/KDR axis. This report provides insight into the determinants of RCSC impact on stemness and highlights the critical role of RCSC in angiogenesis. The presented findings suggest that targeting RCSC through PVT1 expression may be a new treatment strategy for ccRCC.

Single-cell and spatial transcriptomics reveal 5-methylcytosine RNA methylation regulators immunologically reprograms tumor microenvironment characterizations, immunotherapy response and precision treatment of clear cell renal cell carcinoma

Clear cell Renal Cell Carcinoma (ccRCC) is a highly heterogeneous disease, making it challenging to predict prognosis and therapy efficacy. In this study, we aimed to explore the role of 5-methylcytosine (m5C) RNA modification in ccRCC and its potential as a predictor for therapy response and overall survival (OS). We established a novel 5-methylcytosine RNA modification-related gene index (M5CRMRGI) and studied its effect on the tumor microenvironment (TME) using single-cell sequencing data for in-depth analysis, and verified it using spatial sequencing data. Our results showed that M5CRMRGI is an independent predictor of OS in multiple datasets and exhibited outstanding performance in predicting the OS of ccRCC. Distinct mutation profiles, hallmark pathways, and infiltration of immune cells in TME were observed between high- and low-M5CRMRGI groups. Single-cell/spatial transcriptomics revealed that M5CRMRGI could reprogram the distribution of tumor-infiltrating immune cells. Moreover, significant differences in tumor immunogenicity and tumor immune dysfunction and exclusion (TIDE) were observed between the two risk groups, suggesting a better response to immune checkpoint blockade therapy of the high-risk group. We also predicted six potential drugs binding to the core target of the M5CRMRGI signature via molecular docking. Real-world treatment cohort data proved once again that high-risk patients were appropriate for immune checkpoint blockade therapy, while low-risk patients were appropriate for Everolimus. Our study shows that the m5C modification landscape plays a role in TME distribution. The proposed M5CRMRGI-guided strategy for predicting survival and immunotherapy efficacy, we reported here, might also be applied to more cancers other than ccRCC.

A novel autophagy-related long non-coding RNAs prognostic risk score for clear cell renal cell carcinoma

BACKGROUND

As the main histological subtype of renal cell carcinoma, clear cell renal cell carcinoma (ccRCC) places a heavy burden on health worldwide. Autophagy-related long non-coding RNAs (ARlncRs) have shown tremendous potential as prognostic signatures in several studies, but the relationship between them and ccRCC still has to be demonstrated.

METHODS

The RNA-sequencing and clinical characteristics of 483 ccRCC patients were downloaded download from the Cancer Genome Atlas and International Cancer Genome Consortium. ARlncRs were determined by Pearson correlation analysis. Univariate and multivariate Cox regression analyses were applied to establish a risk score model. A nomogram was constructed considering independent prognostic factors. The Harrell concordance index calibration curve and the receiver operating characteristic analysis were utilized to evaluate the nomogram. Furthermore, functional enrichment analysis was used for differentially expressed genes between the two groups of high- and low-risk scores.

RESULTS

A total of 9 SARlncRs were established as a risk score model. The Kaplan-Meier survival curve, principal component analysis, and subgroup analysis showed that low overall survival of patients was associated with high-risk scores. Age, M stage, and risk score were identified as independent prognostic factors to establish a nomogram, whose concordance index in the training cohort, internal validation, and external ICGC cohort was 0.793, 0.671, and 0.668 respectively. The area under the curve for 5-year OS prediction in the training cohort, internal validation, and external ICGC cohort was 0.840, 0.706, and 0.708, respectively. GO analysis and KEGG analysis of DEGs demonstrated that immune- and inflammatory-related pathways are likely to be critically involved in the progress of ccRCC.

CONCLUSIONS

We established and validated a novel ARlncRs prognostic risk model which is valuable as a potential therapeutic target and prognosis indicator for ccRCC. A nomogram including the risk model is a promising clinical tool for outcomes prediction of ccRCC patients and further formulation of individualized strategy.

Chromatin regulators-related lncRNA signature predicting the prognosis of kidney renal clear cell carcinoma and its relationship with immune microenvironment: A study based on bioinformatics and experimental validation

Background: Kidney Renal Clear cell carcinoma (KIRC) is a major concern in the urinary system. A lot of researches were focused on Chromatin Regulators (CRs) in tumors. In this study, CRs-related lncRNAs (CRlncRNAs) were investigated for their potential impact on the prognosis of KIRC and the immune microenvironment.

Methods: The TCGA database was used to obtain transcriptome and related clinical information. CRs were obtained from previous studies, whereas CRlncRNAs were obtained by differential and correlation analysis. We screened the lncRNAs for the signature construction using regression analysis and LASSO regression analysis. The effectiveness of the signature was evaluated using the Kaplan-Meier (K-M) curve and Receiver Operating Characteristic curve (ROC). Additionally, we examined the associations between the signature and Tumor Microenvironment (TME), and the efficacy of drug therapy. Finally, we further verified whether these lncRNAs could affect the biological function of KIRC cells by functional experiments such as CCK8 and transwell assay.

Results: A signature consisting of 8 CRlncRNAs was constructed to predict the prognosis of KIRC. Quantitative Real-Time PCR verified the expression of 8 lncRNAs at the cell line and tissue level. The signature was found to be an independent prognostic indicator for KIRC in regression analysis. This signature was found to predict Overall Survival (OS) better for patients in the subgroups of age, gender, grade, stage, M, N0, and T. Furthermore, a significant correlation was found between riskScore and immune cell infiltration and immune checkpoint. Finally, we discovered several drugs with different IC50 values in different risk groups using drug sensitivity analysis. And functional experiments showed that Z97200.1 could affect the proliferation, migration and invasion of KIRC cells.

Conclusion: Overall, the signature comprised of these 8 lncRNAs were reliable prognostic biomarkers for KIRC. Moreover, the signature had significant potential for assessing the immunological landscape of tumors and providing individualized treatment.

lncRNA PVT1: a novel oncogene in multiple cancers

Long noncoding RNAs are involved in epigenetic gene modification, including binding to the chromatin rearrangement complex in pre-transcriptional regulation and to gene promoters in gene expression regulation, as well as acting as microRNA sponges to control messenger RNA levels in post-transcriptional regulation. An increasing number of studies have found that long noncoding RNA plasmacytoma variant translocation 1 (PVT1) plays an important role in cancer development. In this review of a large number of studies on PVT1, we found that PVT1 is closely related to tumor onset, proliferation, invasion, epithelial–mesenchymal transformation, and apoptosis, as well as poor prognosis and radiotherapy and chemotherapy resistance in some cancers. This review comprehensively describes PVT1 expression in various cancers and presents novel approaches to the diagnosis and treatment of cancer.

A somatic mutation-derived LncRNA signatures of genomic instability predicts the prognosis and tumor microenvironment immune characters in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is an aggressive carcinoma with genome instability. Long non-coding RNAs (LncRNAs) have been functionally associated with genomic instability in cancers. However, the identification and prognostic value of lncRNAs related to genome instability have not been explored in hepatocellular carcinoma. In this study, we aim to identify a genomic instability-related lncRNA signature for predicting prognosis and the efficacy of immunotherapy in HCC patients.

METHODS

According to the somatic mutation and transcript data of 364 patients with HCC, we determined differentially expressed genome instability-related lncRNAs (GInLncRNAs). Gene ontology (GO) enrichment analyses and Kyoto Encyclopedia of genes and genomes enrichment analyses revealed the potential functions of genes co-expressed with those lncRNAs involved in cancer development and immune function. We further determined a genome instability-related lncRNA signature (GInLncSig) through Cox regression analysis and LASSO regression analysis. Thereafter, we performed correlation analyses with mutations, clinical stratification analyses, and survival analyses to evaluate GInLncSig predictive function. Subsequently, we construct a nomogram model for prognostic assessments of patients with HCC. Finally, we performed Immunocytes infiltration analysis, gene set enrichment analysis (ssGSEA) of immunity circle-associated pathways, and T cell-inflamed score to explore GInLncSig's potential value in guiding immunotherapy.

RESULTS

We identified 11 independent prognosis-associated GInLncRNAs (AC002511.2, LINC00501, LINC02055, LINC02714, LINC01508, LOC105371967, RP11_96A15.1, RP11_305F18.1, RP11_342M1.3, RP11_432J24.3, U95743.1) to construct a GInLncSig. According to the risk score calculated by GInLncSig, the high-risk group was characterized by a higher somatic mutation count, significantly poorer clinical prognosis, higher T cell-inflamed score, and specific tumor immune infiltration status compared to the low-risk group. Furthermore, we constructed a nomogram model to improve the reliability and clinical utility of predicting the prognosis of patients with HCC.

CONCLUSION

Our study established a reliable prognostic prediction signature that could be a tool for prognosis prediction and a promising predictive biomarker of immunotherapy in hepatocellular carcinoma.

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.

Epigenetic Regulation in Exposome-Induced Tumorigenesis: Emerging Roles of ncRNAs

Environmental factors, including pollutants and lifestyle, constitute a significant role in severe, chronic pathologies with an essential societal, economic burden. The measurement of all environmental exposures and assessing their correlation with effects on individual health is defined as the exposome, which interacts with our unique characteristics such as genetics, physiology, and epigenetics. Epigenetics investigates modifications in the expression of genes that do not depend on the underlying DNA sequence. Some studies have confirmed that environmental factors may promote disease in individuals or subsequent progeny through epigenetic alterations. Variations in the epigenetic machinery cause a spectrum of different disorders since these mechanisms are more sensitive to the environment than the genome, due to the inherent reversible nature of the epigenetic landscape. Several epigenetic mechanisms, including modifications in DNA (e.g., methylation), histones, and noncoding RNAs can change genome expression under the exogenous influence. Notably, the role of long noncoding RNAs in epigenetic processes has not been well explored in the context of exposome-induced tumorigenesis. In the present review, our scope is to provide relevant evidence indicating that epigenetic alterations mediate those detrimental effects caused by exposure to environmental toxicants, focusing mainly on a multi-step regulation by diverse noncoding RNAs subtypes.

Identification of a competing endogenous RNA network related to immune signature in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a fatal cancer of the urinary system. Long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) involving the ccRCC progression. However, the relationship between the ceRNA network and immune signature is largely unknown. In this study, the ccRCC-related gene expression profiles retrieved from the TCGA database were used first to identify the differentially expressed genes through differential gene expression analysis and weighted gene co-expression network analysis. The interaction among differentially expressed lncRNAs, miRNAs, and mRNAs were matched using public databases. As a result, a ceRNA network was developed that contained 144 lncRNAs, 23 miRNAs, as well as 62 mRNAs. Four of 144 lncRNAs including LINC00943, SRD5A3-AS1, LINC02345, and U62317.3 were identified through LASSO regression and Cox regression analyses, and were used to create a prognostic risk model. Then, the ccRCC samples were divided into the high- and low-risk groups depending on their risk scores. ROC curves, Kaplan-Meier survival analysis, and the survival risk plots indicated that the predictive performance of our developed risk model was accurate. Moreover, the CIBERSORT algorithm was used to measure the infiltration levels of immune cells in the ccRCC samples. The further genomic analysis illustrated a positive correlation between most immune checkpoint blockade-related genes and the risk score. In conclusion, the present findings effectually contribute to the comprehensive understanding of the ccRCC pathogenesis, and may offer a reference for developing novel therapeutic and prognostic biomarkers.

Development of a novel five-lncRNA prognostic signature for predicting overall survival in elderly patients with breast cancer

Background

Breast cancer (BC) is an age‐related disease. Long noncoding RNAs (lncRNAs) have been proven to be crucial contributors in tumorigenesis. This study aims to develop a novel lncRNA‐based signature to predict elderly BC patients’ prognosis.

Methods

The RNA expression profiles and corresponding clinical information of 182 elderly BC patients were retrieved from The Cancer Genome Atlas (TCGA). Differentially expressed lncRNAs (DElncRNAs) between BC and adjacent normal samples were used to construct the signature in the training set through univariate Cox regression analysis, LASSO regression analysis, and multivariate Cox regression analysis. Kaplan–Meier analysis and time‐dependent receiver operating characteristic (ROC) analysis were used to evaluate the predictive performance. Besides, we developed the nomogram. Gene set enrichment analysis (GSEA) was performed to reveal the underlying molecular mechanisms.

Results

We constructed the five‐lncRNA signature (including LEF1‐AS1, MEF2C‐AS1, ST8SIA6‐AS1, LINC01224, and LINC02408) in the training set, which successfully divided the patients into low‐ and high‐risk groups with significantly different prognosis (p = 0.000049), and the AUC at 3 and 5 years of the signature was 0.779 and 0.788, respectively. The predictive performance of this signature was validated in the test and entire set. The 5‐lncRNA signature was an independent prognostic factor of OS (p = 0.007) and the nomogram constructed by independent prognostic factors was an accurate predictor of predicting overall survival probability. Besides, several pathways associated with tumorigenesis have been identified by GSEA.

Conclusions

The 5‐lncRNA signature and nomogram are reliable in predicting elderly BC patients’ prognosis and provide clues for clinical decision‐making.

A novel long non-coding RNA-based prognostic signature for renal cell carcinoma patients with stage IV and histological grade G4

This study aimed to establish a lncRNA-based signature for predicting the prognosis of patients with high stage and grade renal cell carcinoma (RCC). According to the Surveillance, Epidemiology, and End Results (SEER) database, sex, age, grade, stage, surgery, chemotherapy, radiation, tumor size, and marital status were the independent prognostic factors for RCC and also had significant correlations with the overall survival through Cox univariate and multivariate analyses. Noticeably, among these influencing factors, the histological classification of undifferentiated group and pathological stage IV had the greatest prognostic risks for RCC patients. Furthermore, based on the samples at stage IV and histological grade G4 from The Cancer Genome Atlas (TCGA) portal, 9 key lncRNAs, including KIAA2012, CCNT2-AS1, ITPKB-AS1, TBX2-AS1, NUTM2A-AS1, LINC02522, LINC02384, LINC01559, and LINC00865 were identified and a prognostic signature was constructed by Lasso analysis and Cox regression model. The Kaplan-Meier analysis suggested that patients at stage IV and histological grade of G4 in high risk score group had a worse overall survival than that in low risk score group. The following receiver operating characteristic curve (ROC) curves also showed that this signature possesses a better predictive power performance. Pathway enrichment analysis discovered that 9 lncRNAs held potential roles in cell division, cell cycle, DNA damage and cytokines levels in RCC. This work indicates that the established 9-lncRNA signature has a good capacity in predicting the prognosis of RCC patients with stage IV and histological grade of G4, and may be helpful for guiding the treatment strategies for RCC patients.

Modulation of Long Non-Coding RNAs by Different Classes of Secondary Metabolites from Plants: A Mini-Review on Antitumor Effects

The broad pharmacological spectrum of plants is related to their secondary metabolism, which is responsible for the synthesis of different compounds that have multiple effects on cellular physiology. Among the biological effects presented by phytochemicals, their use for the prevention and treatment of cancer can be highlighted. This occurs due to several mechanisms of antitumor action demonstrated by these compounds, including regulation of the cell signaling pathways and inhibition of tumor growth. In this way, long non-coding RNAs (lncRNAs) appear to be promising targets for the treatment of cancer. Their deregulation has already been related to a variety of clinical-pathological parameters. However, the effects of secondary metabolites on lncRNAs are still restricted. For this reason, the present review aimed to gather data on phytochemicals with action on lncRNAs in order to confirm their possible antitumor potential. According to the literature, terpenoid and flavonoid are the main examples of secondary metabolites involved with lncRNAs activity. In addition, the lncRNAs H19, CASC2, HOTAIR, NKILA, CCAT1, MALAT1, AFAP1-AS1, MEG3, and CDKN2B-AS1 can be highlighted as important targets in the search for new anti-tumor agents since they act as modulating pathways related to cell proliferation, cell cycle, apoptosis, cell migration and invasion. Finally, challenges for the use of natural products as a commercial drug were also discussed. The low yield, selectivity index and undesirable pharmacokinetic parameters were emphasized as a difficulty for obtaining these compounds on a large scale and for improving the potency of its biological effect. However, the synthesis and/or development of formulations were suggested as a possible approach to solve these problems. All of these data together confirm the potential of secondary metabolites as a source of new anti-tumor agents acting on lncRNAs.

HAR1: an insight into lncRNA genetic evolution

Long noncoding RNAs (lncRNAs) have a wide range of functions in health and disease, but many remain uncharacterized because of their complex expression patterns and structures. The genetic loci encoding lncRNAs can be subject to accelerated evolutionary changes within the human lineage. HAR1 is a region that has a significantly altered sequence compared to other primates and is a component of two overlapping lncRNA loci, HAR1A and HAR1B. Although the functions of these lncRNAs are unknown, they have been associated with neurological disorders and cancer. Here, we explore the current state of understanding of evolution in human lncRNA genes, using the HAR1 locus as the case study.

Comprehensive analysis of lncRNAs as biomarkers for diagnosis, prognosis, and treatment response in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is the most common histological type of renal carcinoma and has a high recurrence rate and poor outcome. Accurate patient risk stratification based on genetic markers can help to identify the high-risk patient for early and further treatments and would promote patient survival. Long non-coding RNAs (lncRNAs) have attracted widespread attention as biomarkers for early diagnosis, treatment, and prognosis because of their high specificity and sensitivity. Here, we performed a systematic search in NCBI PubMed and found 44 lncRNAs as oncogenes, 18 lncRNAs as tumor suppressors, 199 lncRNAs as diagnostic biomarkers, 62 lncRNAs as prognostic biomarkers, and 3 lncRNAs as predictive biomarkers for ccRCC. We also comprehensively discuss the biological functions and molecular regulatory mechanisms of lncRNAs in ccRCC. Overall, the present study is a systemic analysis to assess the expression and clinical value of lncRNAs in ccRCC, and lncRNAs hold promise to be diagnostic, prognostic, and predictive biomarkers.

Potential prognostic biomarkers related to immunity in clear cell renal cell carcinoma using bioinformatic strategy

The clear cell renal cell carcinoma (ccRCC) is the main pathological subtype of renal cell carcinoma. Immune system evasion, one hallmark of cancer, contributes to cancer cells in escaping from the attack of immune cells. In order to identify potential prognostic biomarkers in ccRCC patients and immune cells fraction, we collected and downloaded profiles from The Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database. We obtained 2 modules significantly associated with tumor stage and immune cells; functional enrichment analysis showed that genes in the module ‘yellow’ were significantly enriched in proteins targeting to membrane and ribosome, as well as the oxidative phosphorylation pathway, while genes in the module ‘green’ mainly participate in molecular functions associated with immunity like activation of T cells. Four LncRNAs (LINC00472, AL590094.1, AL365203.3, and AC147651.3) and RPL27A and RPL22L1 in the module ‘yellow’ and two lncRNAs (LINC00426 and AC129507.2) and five protein-coding genes (CSF1, NOD2, ITGAE, CD7, and PDCD1) in the module ‘green’ represented independent prognostic values in patients with ccRCC. Expression of LINC0042, NOD2, CD7, and PDCD1 were significantly correlated with ratio of immune cells (like T cells CD8 and resting mast cells). LINC00426, with significant correlation with immune cell fraction, shows potential prognostic value in ccRCC patients. Our findings provide a strategy in exploring biomarkers with prognostic significance and significant association with the fraction of immune cells.

lncRNA PVT1 in the Pathogenesis and Clinical Management of Renal Cell Carcinoma

LncRNA PVT1 (plasmacytoma variant translocation 1) has become a staple of the lncRNA profile in patients with renal cell carcinoma (RCC). Common dysregulation in renal tumors outlines the essential role of PVT1 in the development of RCC. There is already a plethora of publications trying to uncover the cellular mechanisms of PVT1-mediated regulation and its potential exploitation in management of RCC. In this review, we summarize the literature focused on PVT1 in RCC and aim to synthesize the current knowledge on its role in the cells of the kidney. Further, we provide an overview of the lncRNA profiling studies that have identified a more or less significant association of PVT1 with the clinical behavior of RCC. Based on our search, we analyzed the 17 scientific papers discussed in this review that provide robust support for the indispensable role of PVT1 in RCC development and future personalized therapy.

Construction and Comparison of ceRNA Regulatory Network for Different Age Female Breast Cancer

Studies have shown the difference appearing among the prognosis of patients in different age groups. However, the molecular mechanism implicated in this disparity have not been elaborated. In this study, expression profiles of female breast cancer (BRCA) associated mRNAs, lncRNAs and miRNAs were downloaded from the TCGA database. The sample were manually classified into three groups according to their age at initial pathological diagnosis: young (age ≤ 39 years), elderly (age ≥ 65 years), and intermediate (age 40-64 years). lncRNA-miRNA-mRNA competitive endogenous RNA (ceRNA) network was respectively constructed for different age BRCA. Then, the biological functions of differentially expressed mRNAs (DEmRNAs) in ceRNA network were further investigated by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Finally, survival analysis was used to identify prognostic biomarkers for different age BRCA patients. We identified 13 RNAs, 38 RNAs and 40 RNAs specific to patients aged ≤ 39 years, aged 40-64 years, and aged ≥ 65 years, respectively. Furthermore, the unique pathways were mainly enriched in cytokine-cytokine receptor interaction in patients aged 40-64 years, and were mainly enriched in TGF-beta signaling pathway in patients aged ≥ 65 years. According to the survival analysis, AGAP11, has-mir-301b, and OSR1 were respectively functioned as prognostic biomarkers in young, intermediate, and elderly group. In summary, our study identified the differences in the ceRNA regulatory networks and provides an effective bioinformatics basis for further understanding of the pathogenesis and predicting outcomes for different age BRCA.

Construction of Competitive Endogenous RNA Network and Verification of 3-Key LncRNA Signature Associated With Distant Metastasis and Poor Prognosis in Patients With Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is a common malignancy with high distant metastasis rate. Long non-coding RNAs (LncRNAs) are reported to be upregulated or downregulated in multiple cancers and play a crucial role in the metastasis of tumors or prognosis. Therefore, the purpose of our study is to construct a prognostic signature for ccRCC based on distant metastasis-related lncRNAs and explore the involved potential competitive endogenous RNA (ceRNA) network. The differentially expressed genes (DEGs) screened from the database of the cancer genome atlas (TCGA) were used to construct a co-expression network and identify the distant metastasis-related module by weighted gene co-expression network analysis (WGCNA). Key genes with metastatic and prognostic significance were identified through rigorous screening, including survival analysis, correlation analysis, and expression analyses in stage, grade, and distant metastasis, and were verified in the data set of gene expression omnibus (GEO) and the database from gene expression profiling interactive analysis (GEPIA). The potential upstream miRNAs and lncRNAs were predicted via five online databases and LncBase. Here, we constructed a ceRNA network of key genes that are significantly associated with the distant metastasis and prognosis of patients with ccRCC. The distant metastasis-related lncRNAs were used to construct a risk score model through the univariate, least absolute shrinkage selection operator (LASSO), and multivariate Cox regression analyses, and the patients were divided into high- and low-risk groups according to the median of the risk score. The Kaplan–Meier survival analysis demonstrated that mortality was significantly higher in the high-risk group than in the low-risk group. Considering the other clinical phenotype, the Cox regression analyses indicated that the lncRNAs model could function as an independent prognostic factor. Quantitative real-time (qRT)-PCR in the tissues and cells of ccRCC verified the high-expression level of three lncRNAs. Gene set enrichment analysis (GSEA) revealed that the lncRNA prognostic signature was mainly enriched in autophagy- and immune-related pathways, indicating that the autophagy and immune functions may play an important role in the distant metastasis of ccRCC. In summary, the constructed distant metastasis-related lncRNA signature could independently predict prognosis in patients with ccRCC, and the related ceRNA network provided a new sight on the potential mechanism of distant metastasis and a promising therapeutic target for ccRCC.

Construction of a Glycolysis-related long noncoding RNA signature for predicting survival in endometrial cancer

Background: long noncoding RNA (lncRNA) has been widely studied and understood in various cancer types. However, the expression profiles of glycolysis-related lncRNA in endometrial cancer (EC) have poorly been reported. Methods: In this study, we retrieved the "Glycolysis" gene list from Molecular Signatures Database (MSigDB) and screened prognostic glycolysis-related lncRNA using The Cancer Genome Atlas (TCGA) Uterine Corpus Endometrial Carcinoma (UCEC) RNA-seq dataset. Then, TCGA UCEC patients were randomly divided. Lasso algorithm and multivariate cox regression analyses were then performed to further select hub prognostic lncRNA and to develop a prognostic signature. The efficacy of the signature was also evaluated in the TCGA EC cohort. Moreover, we constructed a nomogram to predict EC patient outcomes. Results: Univariate cox analysis identified thirty-six glycolysis-related lncRNA correlated with EC patient prognosis. Among them, five lncRNA were further selected as hub lncRNA that mostly relate to EC patient outcomes, which are AL121906.2, BOLA3-AS1, LINC01833, AC016405.3, and RAB11B-AS1. A prognostic signature was then built based on the expression and coefficiency of five lncRNA. The efficacy of the signature was validated in part of and the entire TCGA EC cohort. In addition, the risk signature could precisely distinguish high- and low-risk EC patients and predict patient outcomes. The nomogram exhibited absolute concordance between the predictions and actual survival observations. Conclusions: The glycolysis-related lncRNA signature model and the nomogram may provide a new perspective for EC patients outcome prediction in clinical use.

A New Prognostic Risk Model Based on PPAR Pathway-Related Genes in Kidney Renal Clear Cell Carcinoma

OBJECTIVE

This study is aimed at using genes related to the peroxisome proliferator-activated receptor (PPAR) pathway to establish a prognostic risk model in kidney renal clear cell carcinoma (KIRC).

METHODS

For this study, we first found the PPAR pathway-related genes on the gene set enrichment analysis (GSEA) website and found the KIRC mRNA expression data and clinical data through TCGA database. Subsequently, we used R language and multiple R language expansion packages to analyze the expression, hazard ratio analysis, and coexpression analysis of PPAR pathway-related genes in KIRC. Afterward, using the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) website, we established the protein-protein interaction (PPI) network of genes related to the PPAR pathway. After that, we used LASSO regression curve analysis to establish a prognostic survival model in KIRC. Finally, based on the model, we conducted correlation analysis of the clinicopathological characteristics, univariate analysis, and multivariate analysis.

RESULTS

We found that most of the genes related to the PPAR pathway had different degrees of expression differences in KIRC. Among them, the high expression of 27 genes is related to low survival rate of KIRC patients, and the high expression of 13 other genes is related to their high survival rate. Most importantly, we used 13 of these genes successfully to establish a risk model that could accurately predict patients' prognosis. There is a clear correlation between this model and metastasis, tumor, stage, grade, and fustat.

CONCLUSIONS

To the best of our knowledge, this is the first study to analyze the entire PPAR pathway in KIRC in detail and successfully establish a risk model for patient prognosis. We believe that our research can provide valuable data for future researchers and clinicians.

Identification of Immune-Related Cells and Genes in Tumor Microenvironment of Clear Cell Renal Cell Carcinoma

Clear cell renal cell carcinoma (ccRCC) is one of the most common tumors in the urinary system. Progression in immunotherapy has provided novel options for the ccRCC treatment. However, the understanding of the ccRCC microenvironment and the potential therapeutic targets in the microenvironment is still unclear. Here, we analyzed the gene expression profile of ccRCC tumors from the Cancer Genome Atlas (TCGA) and calculated the abundance ratios of immune cells for each sample. Then, seven types of immune cells were found to be correlated to overall survival, and 3863 immune-related genes were identified by analyzing differentially expressed genes. We also found that the function of immune-related genes was mainly focused on ligand-receptor binding and signaling pathway transductions. Additionally, we identified 13 hub genes by analyzing the protein-protein interaction network, and seven of them are related to overall survival. Our study not only expands the understanding of fundamental biological features of microenvironment but also provides potential therapeutic targets.

Identification of a three-long noncoding RNA prognostic model involved competitive endogenous RNA in kidney renal clear cell carcinoma

Background

Long noncoding RNA (lncRNA) is generally identified as competing endogenous RNA (ceRNA) that plays a vital role in the pathogenesis of kidney renal clear cell carcinoma (KIRC), the most common subtype of renal cell carcinoma with poor prognosis and unclear pathogenesis. This study established a novel ceRNA network and thus identified a three-lncRNA prognostic model in KIRC patients.

Methods

Differentially expressed genes (DEGs) were screened out from The Cancer Genome Atlas (TCGA) database. The lncATLAS was applied to determine the differentially expressed lncRNAs (DElncRNAs) of the cytoplasm. The miRcode, miRDB, miRTarBase, and TargetScan databases were utilized to predict the interactions of DElncRNAs, DEmiRNAs, and DEmRNAs. Cytoscape was used to construct the ceRNA network. Then, a lncRNA prognostic model (LPM) was constructed based on ceRNA-related lncRNA that was significantly related to overall survival (OS), and its predictive ability was evaluated. Moreover, an LPM-based nomogram model was constructed. The significantly different expression of genes in the LPM was validated in an independent clinical cohort (N = 21) by quantitative RT-PCR.

Results

A novel ceRNA regulatory network, including 73 lncRNAs, 8 miRNAs, and 21 mRNAs was constructed. Functional enrichment analysis indicated that integral components of membrane and PI3K-Akt signaling pathway represented the most significant GO terms and pathway, respectively. The LPM established based on three lncRNAs (MIAT, LINC00460, and LINC00443) of great prognostic value from the ceRNA network was proven to be independent of conventional clinical parameters to differentiate patients with low or high risk of poor survival, with the AUC of 1-, 5- and 10-year OS were 0.723, 0.714 and 0.826 respectively. Furthermore, the nomogram showed a better predictive value in KIRC patients than individual prognostic parameters. The expression of MIAT and LINC00460 was significantly upregulated in the KIRC samples, while the expression of LINC00443 was significantly downregulated compared with the adjacent normal samples in the clinical cohort, TCGA, and GTEx.

Conclusion

This LPM based on three-lncRNA could serve as an independent prognostic factor with a tremendous predictive ability for KIRC patients, and the identified novel ceRNA network may provide insight into the prognostic biomarkers and therapeutic targets of KIRC.

MIR155HG Knockdown Inhibited the Progression of Cervical Cancer by Binding SRSF1

BACKGROUND

As the fourth most common cancer among women worldwide, cervical cancer lead to 311,000 deaths in 2018. Although the treatments have been developed, the survival rate of cervical cancer remains unsatisfactory. In this study, we aimed to identify differentially expressed lncRNAs (DEIncRNAs) between cervical cancer and adjacent normal tissues using bioinformatics analysis, and further to investigate the biological function of the DEIncRNAs in vitro and in vivo.

METHODS

The expression profiles from two microarray datasets (GSE6791 and GSE63514) were downloaded from GEO for analysis of DEIncRNAs between cervical cancer and adjacent normal cervical tissues. Among all DEIncRNAs, MIR155HG upregulation was identified and selected for further investigation. The effect of MIR155HG knockdown on proliferation, apoptosis and invasion in SiHa and Hela cells were evaluated. In addition, Western blot, RNA immunoprecipitation (RIP) and cell cycle assays were performed to determine the binding target of MIR155HG. Furthermore, the effect of MIR155HG knockdown on tumor growth in vivo was investigated.

RESULTS

The level of MIR155HG was found to be significantly upregulated in cervical cancer tissue compared with adjacent cervical tissue. Knockdown of MIR155HG notably inhibited the proliferation of SiHa and Hela cells by inducing apoptosis. In addition, MIR155HG knockdown decreased cell invasion. Moreover, tumor growth in xenograft was significantly inhibited by MIR155HG knockdown in vivo. Additionally, SRSF1 was identified as the binding protein of MIR155HG.

CONCLUSION

Our findings demonstrated that MIR155HG knockdown inhibited the progression of cervical cancer by binding SRSF1, inspiring the usage of MIR155HG as a potential novel therapy target for the treatment of cervical cancer.