Prediction of bladder cancer outcome by identifying and validating a mutation-derived genomic instability-associated long noncoding RNA (lncRNA) signature

A combination of cuproptosis and lncRNAs predicts the prognosis and tumor immune microenvironment in cervical cancer

BACKGROUND

Cuproptosis induces proteotoxic stress and eventually leads to cell death. However, the relationship between cuproptosis and lncRNAs in cervical cancer has not been fully elucidated. Therefore, we aim to explore the association among lncRNAs, cuproptosis and clinical features in cervical cancer.

METHODS

RNA sequencing, genetic mutations, and clinical data of CESC patients were obtained from TCGA. Cuproptosis-associated genes were gathered. WGCNA was used to cluster important modules, and KEGG, GO, GSEA and GSVA were used to explore functional and pathway enrichment. The association between immune microenvironment and cuproptosis-related lncRNAs was performed by using cibersort algorithm and other platforms, including XCELL, TIMER, QUANTISEQ, MCPCOUNTER and EPIC. Fluorescence quantitative PCR was employed to detect the expression of LINC01833 and LINC02321, and CCK-8 and cell scratch assays were used to assess cell proliferation and migration capabilities after LINCRNA interference.

RESULTS

202 upregulated and 45 downregulated lncRNAs were selected. The survival analysis showed that there was a statistically significant difference in survival rates between the high-risk and low-risk groups. The prognosis of tumour mutation burden and the degree of immune infiltration were differed noticeably between the high-risk and low-risk groups. BHG712, TL-2-105, FR-180204, Masitinib, TAK-715, ODI-027, JW-7-24-2, and OSI-930 had substantially higher IC50 values in the high-risk group. Notably, we found AL360178.1 was associated with RNF44 E3 ubiquitin ligase expression. In cervical cancer cell lines, LINC01833 and LINC02321 displayed significant upregulation. Efficient siRNA transfection led to a decreased expression of LINC01833 and LINC02321. This knockdown significantly hindered both cell proliferation and migration capabilities in cervical cancer cells compared to the negative control.

CONCLUSION

In conclusion, we constructed five cuprotosis-related lncRNA prognostic models, which may be new tumor therapeutic targets for the prevention and treatment of cervical cancer.

A concise review on the role of MIR100HG in human disorders

MIR100HG is a long non-coding RNA (lncRNA) encoded by a locus on chr11:122,028,203-122,556,721. This gene can regulate cell proliferation, apoptosis, cell cycle transition and cell differentiation. MIR100HG was firstly identified through a transcriptome analysis and found to regulate differentiation of human neural stem cells. It is functionally related with a number of signalling pathways such as TGF-β, Wnt, Hippo and ERK/MAPK signalling pathways. Dysregulation of MIR100HG has been detected in a diversity of cancers in association with clinical outcomes. Moreover, it has a role in the pathophysiology of dilated cardiomyopathy, intervertebral disk degeneration and pulmonary fibrosis. The current study summarizes the role of these lncRNAs in human disorders.

A novel cuproptosis-related lncRNAs signature predicts prognosis in bladder cancer

This study constructed a novel cuproptosis-related lncRNAs signature to predict the prognosis of BLCA patients. The Cancer Genome Atlas (TCGA) database was used to retrieve the RNA-seq data together with the relevant clinical information. The cuproptosis-related genes were first discovered. The cuproptosis-related lncRNAs were then acquired by univariate, the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analysis to create a predictive signature. An eight cuproptosis-related lncRNAs (AC005261.1, AC008074.2, AC021321.1, AL024508.2, AL354919.2, ARHGAP5-AS1, LINC01106, LINC02446) predictive signature was created. Compared with the low-risk group, the prognosis was poorer for the high-risk group. The signature served as an independent overall survival (OS) predictor. Receiver operating characteristic (ROC) curve indicated that the signature demonstrated superior predictive ability, as evidenced by the area under the curve (AUC) of 0.782 than the clinicopathological variables. When we performed a subgroup analysis of the different variables, the high-risk group's OS for BLCA patients was lower than that of the low-risk group's patients. Gene Set Enrichment Analysis (GSEA) showed that high-risk groups were clearly enriched in many immune-related biological processes and tumor-related signaling pathways. Single sample gene set enrichment analysis (ssGSEA) revealed that the immune infiltration level was different between the two groups. Finally, quantitative RT-PCR showed that AC005261.1, AC021321.1, AL024508.2, LINC02446 and LINC01106 were lowly expressed in tumor cells, while ARHGAP5-AS1 showed the opposite trend. In summary, the predictive signature can independently predict the prognosis and provide clinical treatment guidance for BLCA patients.

Identification of a novel defined inflammation-related long noncoding RNA signature contributes to predicting prognosis and distinction between the cold and hot tumors in bladder cancer

Purpose

Bladder cancer (BLCA) is one of the most frequently diagnosed urological malignancies and is the 4th most common cancer in men worldwide. Molecular targets expressed in bladder cancer (BLCA) are usually used for developing targeted drug treatments. However, poor prognosis and poor immunotherapy efficacy remain major challenges for BLCA. Numerous studies have shown that long non-coding RNAs (LncRNAs) play an important role in the development of cancer. However, the role of lncRNAs related to inflammation in BLCA and their prognostic value remain unclear. Therefore, this study is aimed to explore new potential biomarkers that can predict cancer prognosis.

Methods

We downloaded BLCA-related RNA sequencing data from The Cancer Genome Atlas (TCGA) and searched for inflammation-related prognostic long non-coding RNAs (lncRNAs) by univariate Cox (uniCox) regression and co-expression analysis. We used the least absolute shrinkage and selection operator (LASSO) analysis to construct an inflammation-related lncRNA prognosis risk model. Samples were divided into high-risk score (HRS) group and low-risk score (LRS) group based on the median value of risk scores. The independent variable factors were identified by univariate Cox (uni-Cox) and multivariate Cox (multi-Cox) regression analyses, and receiver operating characteristic (ROC) curves were used to compare the role of different factors in predicting outcomes. Nomogram and Calibration Plot were generated by the R package rms to analyze whether the prediction results are correct and show good consistency. Correlation coefficients were calculated by Pearson analysis. The Kaplan-Meier method was used to assess the prognostic value. The expression of 7 lncRNAs related with inflammation was also confirmed by qRT-PCR in BLCA cell lines. Kyoto Encyclopedia of Gene and Genome (KEGG) pathways that were significantly enriched (P < 0.05) in each risk group were identified by the GSEA software. The R package pRRophetic was used to predict the IC50 of common chemotherapeutic agents. TIMER, XCELL, QUANTISEQ, MCPCOUNTER, EPIC and CIBERSORT were applied to quantify the relative proportions of infiltrating immune cells. We also used package ggpubr to evaluate TME scores and immune checkpoint activation in LRS and HRS populations. R package GSEABase was used to analyze the activity of immune cells or immune function. Different clusters of principal component analysis (PCA), t-distribution random neighborhood embedding (t-SNE), and Kaplan-Meier survival were analyzed using R package Rtsne’s. The R package ConsensesClusterPlus was used to class the inflammation-related lncRNAs.

Results

In this study, a model containing 7 inflammation-related lncRNAs was constructed. The calibration plot of the model was consistent with the prognosis prediction outcomes. The 1-, 3-, and 5-year ROC curve (AUC) were 0.699, 0.689, and 0.699, respectively. High-risk patients were enriched in lncRNAs related with tumor invasion and immunity, and had higher levels of immune cell infiltration and immune checkpoint activation. Hot tumors and cold tumors were effectively distinguished by clusters 2 and 3 and cluster 1, respectively, which indicated that hot tumors are more susceptible to immunotherapy.

Conclusion

Our study showed that inflammation-related LncRNAs are closely related with BLCA, and inflammation-related lncRNA can accurately predict patient prognosis and effectively differentiate between hot and cold tumors, thus improving individualized immunotherapy for BLCA patients. Therefore, this study provides an effective predictive model and a new therapeutic target for the prognosis and clinical treatment of BLCA, thus facilitating the development of individualized tumor therapy.

LncRNA LINC01833 is a Prognostic Biomarker and Correlates with Immune Infiltrates in Patients with Lung Adenocarcinoma by Integrated Bioinformatics Analysis

Due to the absence of accurate tools for early detection and successful treatment, lung adenocarcinoma (LUAD) is one of the most aggressive tumors with high morbidity and mortality globally. It is absolutely necessary to investigate the process behind its development and search for new biomarkers that could aid in the early detection of LUAD. There is a correlation between the immune microenvironment of the tumor and the prognosis of lung cancer as well as the efficacy of immunotherapy. Long noncoding RNAs (lncRNAs) have been identified as potential prognostic biomarkers linked to immunological activities. In this study, we identified 1 downregulated lncRNA and 76 upregulated lncRNAs in LUAD samples from TCGA datasets. Among the 77 dysregulated lncRNAs, our attention focused on lncRNA LINC01833 (LINC01833). When compared with nontumor specimens, the level of expression of LINC01833 was shown to be significantly elevated in LUAD samples. In addition, the data of the ROC study revealed that LUAD patients with high LINC01833 expression had an AUC value of 0.840 (95% confidence interval: 0.804 to 0.876). There was a correlation between high LINC01833 expression and an advanced clinical stage. Patients who had a high expression of LINC01833 were shown to have a lower overall survival rate (p < 0.001) and a lower disease-specific survival rate (p = 0.004) in comparison to patients who were in the low LINC01833 group, according to the data on survival. In addition, the results of the multivariate analysis revealed that high LINC01833 expression was an independent predictor of poor survival in LUAD. Moreover, the immune analysis revealed that we found that the expression of LINC01833 was positively associated with Th2 cells, aDC, and Tgd, while negatively associated with Mast cells, Tcm, Eosinophils, iDC, DC, Tem, Th17 cells, and pDC. Overall, our data point to the possibility that the unique lncRNA LINC01833 might be employed as a diagnostic and prognostic marker, and as a result, it has a significant impact on clinical practice.

Integrative Analysis Reveals a Nine TP53 Pathway-Related lncRNA Prognostic Signature in Endometrial Cancer

Background

TP53 mutation is a common mutation gene in uterine corpus endometrial carcinoma (UCEC), and the TP53 signaling pathway plays an essential role in the tumorigenesis, progression, and immune infiltration in UCEC. We aimed to discover TP53 pathway-related lncRNAs in UCEC. Materials and methods. 528 UCEC patients with 587 transcriptional profiles were enrolled in this study. We first investigated the differential status of TP53 signaling pathway between tumor and normal tissues by GSEA analysis, then identified TP53 pathway-related lncRNAs, accordingly establishing a nine TP53 pathway related to the lncRNA signature in the training set and verified this signature in the test set. Besides, the interaction network was constructed; the immune infiltration, drug response to cisplatin and paclitaxel, and mutation atlas were investigated. Finally, we performed a subgroup analysis to check the universality of this signature.

Results

A nine TP53 pathway-related lncRNA prognostic signature was constructed and verified superior accuracy in predicting the overall survival of UCEC patients. Besides, high-risk patients showed a poor prognosis, but they were more sensitive to the cisplatin and paclitaxel. Notably, M2 macrophages were higher infiltrated in high-risk patients, and TP53 showed a significantly higher mutation in high-risk patients than low-risk patients.

Conclusions

We constructed and verified a nine TP53 pathway-related lncRNA prognostic signature in UCEC, which also contributes to the decision-making of the chemotherapy.

Pan-cancer analysis of LncRNA XIST and its potential mechanisms in human cancers

Background

Methods

Results

Conclusion

LncmiRHG-MIR100HG: A new budding star in cancer

MIR100HG, also known as lncRNA mir-100-let-7a-2-mir-125b-1 cluster host gene, is a new and critical regulator in cancers in recent years. MIR100HG is dysregulated in various cancers and plays an oncogenic or tumor-suppressive role, which participates in many tumor cell biology processes and cancer-related pathways. The errant expression of MIR100HG has inspired people to investigate the function of MIR100HG and its diagnostic and therapeutic potential in cancers. Many studies have indicated that dysregulated expression of MIR100HG is markedly correlated with poor prognosis and clinicopathological features. In this review, we will highlight the characteristics and introduce the role of MIR100HG in different cancers, and summarize the molecular mechanism, pathways, chemoresistance, and current research progress of MIR100HG in cancers. Furthermore, some open questions in this rapidly advancing field are proposed. These updates clarify our understanding of MIR100HG in cancers, which may pave the way for the application of MIR100HG-targeting approaches in future cancer diagnosis, prognosis, and therapy.

Applications of Exosomes in Diagnosing Muscle Invasive Bladder Cancer

Muscle Invasive Bladder Cancer (MIBC) is a subset of bladder cancer with a significant risk for metastases and death. It accounts for nearly 25% of bladder cancer diagnoses. A diagnostic work-up for MIBC is inclusive of urologic evaluation, radiographic imaging with a CT scan, urinalysis, and cystoscopy. These evaluations, especially cystoscopy, are invasive and carry the risk of secondary health concerns. Non-invasive diagnostics such as urine cytology are an attractive alternative currently being investigated to mitigate the requirement for cystoscopy. A pitfall in urine cytology is the lack of available options with high reliability, specificity, and sensitivity to malignant bladder cells. Exosomes are a novel biomarker source which could resolve some of the concerns with urine cytology, due to the high specificity as the surrogates of tumor cells. This review serves to define muscle invasive bladder cancer, current urine cytology methods, the role of exosomes in MIBC, and exosomes application as a diagnostic tool in MIBC. Urinary exosomes as the specific populations of extracellular vesicles could provide additional biomarkers with specificity and sensitivity to bladder malignancies, which are a consistent source of cellular information to direct clinicians for developing treatment strategies. Given its strong presence and differentiation ability between normal and cancerous cells, exosome-based urine cytology is highly promising in providing a perspective of a patient’s bladder cancer.

Identification of a prognostic classifier based on EMT-related lncRNAs and the function of LINC01138 in tumor progression for lung adenocarcinoma

Purpose: This study aimed to develop a prognostic indicator based on epithelial-mesenchymal transition (EMT)-related long noncoding RNAs (lncRNAs) and explore the function of EMT-related lncRNAs in malignant progression in lung adenocarcinoma (LUAD).

Materials and methods: A LUAD dataset was acquired from The Cancer Genome Atlas (TCGA) to identify prognostic EMT-related lncRNAs via differential expression analysis and univariate Cox regression analysis. Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression analysis was utilized for variable selection and model construction. The EMT-related prognostic index (ERPI) was calculated according to the model and served as a classifier to divide LUAD individuals into high-ERPI and low-ERPI groups. A nomogram incorporating ERPI and clinicopathological variables was constructed. TCGA-LUAD, GSE50081, and GSE31210 were used to test the predictive capacity of the ERPI and nomogram. The characteristics of the tumor microenvironment (TME) were evaluated via the ESTIMATE, TIMER, and ssGSEA algorithms. Gene set variation analysis (GSVA) and ssGSEA were used to annotate the functions of the high-ERPI and low-ERPI groups. CCK8, transwell assay, wound-healing assay, and clone formation assay were conducted to clarify the biological functions of prognostic EMT-related lncRNAs.

Results: Ninety-seven differentially expressed EMT-related lncRNAs were identified, 15 of which were related to overall survival (OS). A prognostic signature was constructed based on 14 prognostic EMT-related lncRNAs to calculate the ERPI of each patient, and the predictive ability of ERPI was verified in TCGA, GSE50081, and GSE31210. The low-ERPI group survived longer and had a lower percentage of patients in advanced stage than the high-ERPI group. The nomogram had the highest predictive accuracy, followed by ERPI and stage. Patients with low ERPI had higher infiltration degree of immune cells and stronger immune responses than those with high ERPI. A series of in vitro experiments demonstrated that knockdown of LINC01138 dampened variability, proliferation, and motility of A549 and H460 cells.

Conclusion: Our study developed a prognostic classifier with robust prognostic performance and clarified the biological functions of LINC01138 in LUAD, aiding in making individual treatments for patients with LUAD and dissecting the mechanism of oncogenesis.

Identification of LncRNA Prognostic Signature Associated With Genomic Instability in Pancreatic Adenocarcinoma

Background

Genomic instability (GI) is a critical feature of cancer which plays a key role in the occurrence and development of pancreatic adenocarcinoma (PAAD). Long non-coding RNA (LncRNA) is an emerging prognostic biomarker because it is involved in regulating GI. Recently, researchers used such GI-related LncRNAs (GILncRNAs) to establish a prognostic signature for patients with cancer and helped in predicting the overall prognosis of the patients. However, it is evident that patients with PAAD still lack such prognostic signature constructed with GILncRNA.

Methods

The present study screened GILncRNAs from 83 patients with PAAD. Prognosis-related GILncRNAs were identified by univariate Cox regression analysis. The correlation coefficients of these GILncRNAs were obtained by multivariate Cox regression analysis and used to construct a signature. The signature in the present study was then assessed through survival analysis, mutation correlation analysis, independent prognostic analysis, and clinical stratification analysis in the training set and validated in the testing as well as all TCGA set. The current study performed external clinical relevance validation of the signature and validated the effect of AC108134.2 in GILncSig on PAAD using in vitro experiments. Finally, the function of GILncRNA signature (GILncSig) dependent on Gene Ontology enrichment analysis was explored and chemotherapeutic drug sensitivity analysis was also performed.

Results

Results of the present study found that a total of 409 GILncRNAs were identified, 5 of which constituted the prognostic risk signature in this study, namely, AC095057.3, AC108134.2, AC124798.1, AL606834.1, and AC104695.4. It was found that the signature of the present study was better than others in predicting the overall survival and applied to patients with PAAD of all ages, genders, and tumor grades. Further, it was noted that the signature of the current study in the GSE102238, was correlated with tumor length, and tumor stage of patients with PAAD. In vitro, functional experiments were used in the present study to validate that AC108134.2 is associated with PAAD genomic instability and progression. Notably, results of the pRRophetic analysis in the current study showed that the high-risk group possessed reverse characteristics and was sensitive to chemotherapy.

Conclusions

In conclusion, it was evident that the GILncSig used in the present study has good prognostic performance. Therefore, the signature may become a potential sensitive biological indicator of PAAD chemotherapy, which may help in clinical decision-making and management of patients with cancer.

Development and validation of Pyroptosis‑related lncRNAs prediction model for bladder cancer

Bladder cancer (BLCA) is one of the highly heterogeneous disorders accompanied by a poor prognosis. The present study aimed to construct a model based on pyroptosis-related long-stranded non-coding RNA (lncRNA) to evaluate the potential prognostic application in bladder cancer. The mRNA expression profiles of bladder cancer patients and corresponding clinical data were downloaded from the public database from The Cancer Genome Atlas (TCGA). Pyroptosis-related lncRNAs were identified by utilizing a co-expression network of pyroptosis-related genes and lncRNAs. The lncRNA was further screened by univariate Cox regression analysis. Finally, eight pyroptosis-related lncRNA markers were established using least absolute shrinkage and selection operator (Lasso) regression and multivariate Cox regression analyses. Patients were separated into high- and low-risk groups based on the performance value of the median risk score. Patients in the high-risk group had significantly poorer overall survival (OS) than those in the low-risk group (P<0.001). In multivariate Cox regression analysis, the risk score was an independent predictive factor of OS (HR > 1, P<0.01). The areas under the curve (AUCs) of the 3- and 5-year OS in the receiver operating characteristic (ROC) curve were 0.742 and 0.739, respectively. In conclusion, these eight pyroptosis-related lncRNA and their markers may be potential molecular markers and therapeutic targets for bladder cancer patients.

Impacts of Chemokine (C-X-C Motif) Receptor 2 C1208T Polymorphism on Cancer Susceptibility

Background

The CXC chemokines belong to a unique family of cytokines that participates in the progression and development of many malignant tumors. Evidence for the relationship between chemokine (C-X-C motif) receptor 2 (CXCR2) C1208T polymorphism and susceptibility to cancer remains inconsistent.

Methods

Odds ratios (ORs), 95% confidence intervals (CIs), and combined analysis were used to investigate the effect of CXCR2 variation on cancer risk. Gene Set Enrichment Analysis (GSEA) and enzyme-linked immunosorbent assay (ELISA) were also used to evaluate the expression of CXCR2 in prostate cancer (PCA).

Results

Across 11 case-control studies, 4,909 cases and 5,884 controls were involved in the current analysis. Individuals with a TT genotype were associated with increased risk of digestive cancer, compared to those with a TC+CC genotype (OR = 1.16, 95%CI = 1.02-1.31, P = 0.025). Individuals carrying the TT genotype had a 39% higher risk of urinary cancer than those carrying CC genotype (OR = 1.39, 95%CI = 1.04-1.87, P = 0.025). Individuals with a TT genotype showed a 56% augmented breast cancer risk, compared to those with a CC genotype (OR = 1.56, 95%CI = 1.03-2.35, P = 0.034). It was found that CXCR2 expression was downregulated in PCA. Compared with PCA subjects carrying the CC genotype, the expression of CXCR2 was decreased in patients with the TT genotype.

Conclusions

The CXCR2 C1208T variation was associated with elevated risk of urinary, breast, and digestive cancer. However, the C1208T polymorphism was correlated with attenuated risk of lung cancer.

Identification of a Somatic Mutation-Derived Long Non-Coding RNA Signatures of Genomic Instability in Renal Cell Carcinoma

Background

Renal cell carcinoma (RCC) is a malignant tumor with high morbidity and mortality. It is characterized by a large number of somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) are widely involved in the expression of genomic instability in renal cell carcinoma. But no studies have identified the genome instability-related lncRNAs (GInLncRNAs) and their clinical significances in RCC.

Methods

Clinical data, gene expression data and mutation data of 943 RCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Based on the mutation data and lncRNA expression data, GInLncRNAs were screened out. Co-expression analysis, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted to explore their potential functions and related signaling pathways. A prognosis model was further constructed based on genome instability-related lncRNAs signature (GInLncSig). And the efficiency of the model was verified by receiver operating characteristic (ROC) curve. The relationships between the model and clinical information, prognosis, mutation number and gene expression were analyzed using correlation prognostic analysis. Finally, the prognostic model was verified in clinical stratification according to TCGA dataset.

Results

A total of 45 GInLncRNAs were screened out. Functional analysis showed that the functional genes of these GInLncRNAs were mainly enriched in chromosome and nucleoplasmic components, DNA binding in molecular function, transcription and complex anabolism in biological processes. Univariate and Multivariate Cox analyses further screened out 11 GInLncSig to construct a prognostic model (AL031123.1, AC114803.1, AC103563.7, AL031710.1, LINC00460, AC156455.1, AC015977.2, 'PRDM16-dt', AL139351.1, AL035661.1 and LINC01606), and the coefficient of each GInLncSig in the model was calculated. The area under the curve (AUC) value of the ROC curve was 0.770. Independent analysis of the model showed that the GInLncSig model was significantly correlated with the RCC patients' overall survival. Furthermore, the GInLncSig model still had prognostic value in different subgroups of RCC patients.

Conclusion

Our study preliminarily explored the relationship between genomic instability, lncRNA and clinical characteristics of RCC patients, and constructed a GInLncSig model consisted of 11 GInLncSig to predict the prognosis of patients with RCC. At the same time, our study provided theoretical support for the exploration of the formation and development of RCC.

Identification of a Nomogram from Ferroptosis-Related Long Noncoding RNAs Signature to Analyze Overall Survival in Patients with Bladder Cancer

Purpose

This study aimed to establish a nomogram to predict the overall survival (OS) of patients with bladder cancer (BC) by ferroptosis-related long noncoding RNAs (FRlncRNAs) signature.

Methods

We obtained FRlncRNAs expression profiles and clinical data of patients with BC from the Cancer Genome Atlas database. The patients were divided into the training set, testing set, and overall set. Lasso regression and multivariate Cox regression were used to establish the FRlncRNAs signature, the prognosis of each group was compared by Kaplan–Meier (K-M) analysis, and the receiver operating characteristic (ROC) curve evaluated the accuracy of the model. The Gene Set Enrichment Analysis (GSEA) was used for the visualization of the functional enrichment for FRlncRNAs. The databases of GEPIA and K-M Plotter were used for subsequent functional analysis of major FRlncRNAs.

Results

Thirteen prognostic FRlncRNAs (LINC00942, MAFG-DT, AL049840.3, AL136084.3, OCIAD1-AS1, AC062017.1, AC008074.2, AC018653.3, AL031775.1, USP30-AS1, LINC01767, AC132807.2, and AL354919.2) were identified to be significantly different, constituting an FRlncRNAs signature. Patients with BC were divided into low-risk group and high-risk group by this signature in the training, testing, and overall sets. K-M analysis showed that the prognosis of patients in the high-risk group was poor and the difference in the subgroup analyses was statistically significant. ROC analysis revealed that the predictive ability of the model was more accurate than traditional assessment methods. A risk score based on FRlncRNAs signature was an independent prognostic factor for the patients with BC (HR = 1.388, 95%CI = 1.228–1.568, P < 0.001). Combining the FRlncRNAs signature and clinicopathological factors, a predictive nomogram was constructed. The nomogram can accurately predict the overall survival of patients and had high clinical practicability. The GSEA analysis showed that the primary pathways were WNT, MAPK, and cell-matrix adhesion signaling pathways. The major FRlncRNAs (MAFG-DT) were associated with poor prognosis in the GEPIA and K-M Plotter database.

Conclusion

Thirteen prognostic FRlncRNAs and their nomogram were accurate tools for predicting the OS of BC, which might be molecular biomarkers and therapeutic targets.

Effect of interleukin-8 receptor B (IL8RB) rs1126579 C>T variation on the risk to cancer

Chemokines are a type of cytokine that participate in the migration of macrophages and monocytes to inflammatory cells. In particular, CXC chemokines are involved in the development of many cancers. Evidence for the association between interleukin-8 receptor B (IL8RB) rs1126579 C > T variation and cancer risk remains contradictory. Here, we utilized a comprehensive analysis containing odds ratios (ORs), regression, and in silico tools to evaluate the effect of IL8RB polymorphism on cancer risk. We further employed Gene set enrichment analysis combined with ELISA to evaluate the IL8RB expression in patients with prostate cancer (PRAD). A total of 5,187 cancer cases and 6,691 controls were included in the present analysis. Individuals with the TT genotype were associated with an increased risk of cancer compared to those with the TC+CC genotype. In a subgroup analysis by type of cancer, individuals with the TT genotype had a 39% increased risk of urinary cancer compared to those with the CC genotype. A subgroup analysis by ethnicity showed that Asians carrying the TC genotype had a 26% lower risk of cancer than those carrying the CC genotype. We found that the expression of IL8RB was down-regulated in PRAD. Compared to that in PRAD subjects carrying the CC genotype, the expression of IL8RB was decreased in patients with the TT+TC genotype. In conclusion, the IL8RB rs1126579 C > T variation may be associated with cancer risk, especially in Asian populations and patients with PRAD.

Long non-coding RNA CRNDE regulates the growth and migration of prostate cancer cells by targeting microRNA-146a-5p

The function of lncRNA CRNDE and its role in prostate cancer (PC) remains unclear. The aim of this study was to determine the expression level of lncRNA CRNDE in PC tissues and to elucidate its role in PC. The expression levels of lncRNA CRNDE were measured by quantitative reverse transcription polymerase chain reaction. The role of lncRNA CRNDE in PC cells was studied using loss-of-function assays in vitro. Cell proliferation, migration, invasion, and apoptosis were assessed via Cell Counting Kit-8, colony formation, flow cytometry, wound healing, and transwell chamber assays. A luciferase reporter assay was used to characterize the interaction between lncRNA CRNDE and miR-146a-5p. In PC tissues, the expression level of lncRNA CRNDE was upregulated. Moreover, knockdown of lncRNA CRNDE suppressed PC cell proliferation and migration and induced apoptosis in vitro. miR-146a-5p was verified as a direct target of lncRNA CRNDE. Moreover, the inhibition of miR-146a-5p partially counteracted the effects of lncRNA CRNDE on PC cell proliferation, migration, and invasion. In conclusion, lncRNA CRNDE may serve as a cancer promoter in PC by targeting miR-146a-5p. Therefore, lncRNA CRNDE could be a promising target for the clinical treatment of PC.