A long non-coding RNA signature to improve prognostic prediction in clear cell renal cell carcinoma

Imaging Techniques and Biochemical Biomarkers: New Insights into Diagnosis of Pancreatic Cancer

Pancreatic cancer (PaC) incidence is increasing, but our current screening and diagnostic strategies are not very effective. However, screening could be helpful in the case of PaC, as recent evidence shows that the disease progresses gradually. Unfortunately, there is no ideal screening method or program for detecting PaC in its early stages. Conventional imaging techniques, such as abdominal ultrasound, CT, MRI, and EUS, have not been successful in detecting early-stage PaC. On the other hand, biomarkers may be a more effective screening tool for PaC and have greater potential for further evaluation compared to imaging. Recent studies on biomarkers and artificial intelligence (AI)-enhanced imaging have shown promising results in the early diagnosis of PaC. In addition to proteins, non-coding RNAs are also being studied as potential biomarkers for PaC. This review consolidates the current literature on PaC screening modalities to provide an organized framework for future studies. While conventional imaging techniques have not been effective in detecting early-stage PaC, biomarkers and AI-enhanced imaging are promising avenues of research. Further studies on the use of biomarkers, particularly non-coding RNAs, in combination with imaging modalities may improve the accuracy of PaC screening and lead to earlier detection of this deadly disease.

Prognostic value of lncRNA AFAP1-AS1 in breast cancer: a meta-analysis and validated study in Chinese population

BACKGROUND

Long non encoding RNA (lncRNA) plays a crucial role in breast cancer. However, the prognostic role of AFAP1-AS1 in breast cancer remains unclear.

AIMS

To investigate the relationship between the expression of long non-coding RNA actin filament-associated protein1 antisense RNA1 (AFAP1-AS1) and prognosis of breast cancer.

METHODS AND RESULTS

Meta-analysis was performed to explore the correlation between AFAP1-AS1 and breast cancer. The AFAP1-AS1expression in patients with breast cancer tissue and adjacent normal tissue from 153 patients was determined by qRT-PCR. Bioinformatics and Cox proportional-hazards risk model were used to explore the relationship between expression of AFAP1-AS1 and prognosis. The combined analysis revealed a significant correlation between AFAP1-AS1 expression and both overall survival (hazard ratios, HR = 2.33, 95%Cl: 1.94-2.81, p < 0.001) as well as disease-free survival/progression-free survival (HR = 2.94, 95%CI: 2.35-3.67, p < 0.001). The relation between expression of AFAP1-AS1 and breast cancer was determined in 153 breast cancer and adjacent normal tissues. The findings revealed a significantly higher AFAP1-AS1expression levels in breast cancer tissues compared to adjacent normal tissues (p < 0.001). Additionally, patients exhibiting heightened levels of AFAP1-AS1 expression were correlated with an unfavorable prognosis (HR = 2.35, 95%CI: 1.47-3.74, p < 0.001), which aligns consistently with the findings of the pooled analysis. The subgroup analysis of clinical characteristics revealed a significant association between high expression of AFAP1-AS1 and TNM stage (HR = 1.72, 95%CI: 1.11-2.65, p = 0.015).

CONCLUSION

This study demonstrated that AFAP1-AS1 acts as an oncogene and may serve as a novel prognostic marker for breast cancer, particularly in the Chinese population.

Constructing a novel signature and predicting the immune landscape of colon cancer using N6-methylandenosine-related lncRNAs

Background: Colon cancer (CC) is a prevalent malignant tumor that affects people all around the world. In this study, N6-methylandenosine-related long non-coding RNAs (m6A-related lncRNAs) in 473 colon cancers and 41 adjacent tissues of CC patients from The Cancer Genome Atlas (TCGA) were investigated. Method: The Pearson correlation analysis was conducted to examine the m6A-related lncRNAs, and the univariate Cox regression analysis was performed to screen 38 prognostic m6A-related lncRNAs. The least absolute shrinkage and selection operator (LASSO) regression analysis were carried out on 38 prognostic lncRNAs to develop a 14 m6A-related lncRNAs prognostic signature (m6A-LPS) in CC. The availability of the m6A-LPS was evaluated using the Kaplan-Meier and Receiver Operating Characteristic (ROC) curves. Results: Three m6A modification patterns with significantly different N stages, survival time, and immune landscapes were identified. It has been discovered that the m6A-LPS, which is based on 14 m6A-related lncRNAs (TNFRSF10A-AS1, AC245041.1, AL513550.1, UTAT33, SNHG26, AC092944.1, ITGB1-DT, AL138921.1, AC099850.3, NCBP2-AS1, AL137782.1, AC073896.3, AP006621.2, AC147651.1), may represent a new, promising biomarker with great potential. It was re-evaluated in terms of survival rate, clinical features, tumor infiltration immune cells, biomarkers related to Immune Checkpoint Inhibitors (ICIs), and chemotherapeutic drug efficacy. The m6A-LPS has been revealed to be a novel potential and promising predictor for evaluating the prognosis of CC patients. Conclusion: This study revealed that the risk signature is a promising predictive indicator that may provide more accurate clinical applications in CC therapeutics and enable effective therapy strategies for clinicians.

Establishment of a prognostic ferroptosis- and immune-related long noncoding RNAs profile in kidney renal clear cell carcinoma

Background: Ferroptosis and immunity are novel treatments that target several cancers, including kidney renal clear cell carcinoma (KIRC). Long noncoding RNAs (lncRNAs) are an important class of gene expression regulators that play fundamental roles in the regulation of ferroptosis and immunity. We aimed to identify ferroptosis- and immune-related lncRNAs as biomarkers in patients with KIRC.

Methods: Corresponding data for each patient with KIRC were obtained from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were used to identify candidate biomarkers followed by least absolute shrinkage and selection operator (LASSO) regression analyses, weighted gene coexpression network analysis (WGCANA), and gene set enrichment analysis (GSEA).

Results: Three ferroptosis- and immune-related differentially expressed lncRNAs (FI-DELs) (AC124854.1, LINC02609, and ZNF503-AS2) were markedly and independently correlated with the overall survival (OS) of patients with KIRC. The area under the curve (AUC) value of the prognostic model in the entire group using the three FI-DELs was > 0.70. The sensitivity and specificity of the diagnostic model using the three FI-DELs were 0.8586 and 0.9583, respectively.

Conclusion: The present study found that AC124854.1, LINC02609, and ZNF503-AS2 were considerably and independently correlated with the OS of patients with KIRC, suggesting that the three FI-DELs could be used as prognostic and diagnostic biomarkers for patients with KIRC.

PINTology: A short history of the lncRNA LINC-PINT in different diseases

LINC-PINT is a p53-induced long intergenic noncoding transcript that plays a crucial role in many diseases, especially cancer. This long noncoding RNA (lncRNA) gene produces in total 102 (LNCipedia) alternatively spliced variants (LINC-PINT:1 to LINC-PINT:102). The functions of known variants include RNA transcripts, host transcripts for circular RNA (circRNA) generation and as sources for the translation of short peptides. In most human tumors, LINC-PINT is down-regulated where it serves as a tumor suppressor. However, the diversity of its functions in other maladies signifies its general clinical importance. Current LINC-PINT molecular functions include RNA-protein interactions, miRNA sponging and epigenetic modulation with these mechanisms operating in different cellular contexts to exert effects on biological processes ranging from DNA damage responses, cell cycle and growth arrest, senescence, cell migration and invasion, and apoptosis. Genetic polymorphisms in LINC-PINT have also been functionally associated with cancer and other pathologies including the autoimmune diseases pemphigus foliaceus and arthritis. Hence, LINC-PINT shows great potential as a clinical biomarker, especially for the diagnosis and prognosis of cancer. In this review, we explore the current knowledge highlighting the distinctive molecular functions of LINC-PINT in specific cancers and other disease states. This article is categorized under: RNA in Disease and Development > RNA in Disease.

LINC00174 promotes cell proliferation and metastasis in renal clear cell carcinoma by regulating miR-612/FOXM1 axis

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) is the most common pathological subtype of kidney tumor. Reportedly, LINC00174 is a key regulator in cancer progression. This study aims to clarify the role and molecular mechanism of LINC00174 in the progression of KIRC.

METHODS

LINC00174 expression in KIRC and its prognostic value were analyzed by bioinformatics. LINC00174, miR-612 and FOXM1 mRNA expression levels in KIRC clinical samples and cell lines were detected by qRT-PCR. After LINC00174 was overexpressed or knocked down, CCK-8, BrdU and Transwell assays were adopted to evaluate the proliferation and metastatic potential of KIRC cells. Bioinformatics and dual luciferase reporter assays were employed to validate the targeting relationship between miR-612 and LINC00174 or FOXM1 mRNA, respectively. Western blot assay was performed to detect FOXM1 protein expression in KIRC cells.

RESULTS

LINC00174 expression and FOXM1 expression were up-regulated in 42 cases of KIRC tissues (P < 0.001), while miR-612 expression was down-regulated (P < 0.001). LINC00174 overexpression or miR-612 inhibitor promoted the viability and proliferation of KIRC cells (P < 0.01). Migration and invasion of KIRC cells were promoted when the cells were transfected with LINC00174 overexpression or miR-612 inhibitor (P < 0.05). LINC00174 can competitively bind with miR-612 to repress the expression of miR-612, in turn up-regulate the expression of FOXM1 mRNA.

CONCLUSION

LINC00174 facilitates the proliferation and metastatic potential of KIRC cells via regulating the miR-612/FOXM1 axis.

LINC00488 Induces Tumorigenicity in Retinoblastoma by Regulating microRNA-30a-5p/EPHB2 Axis

OBJECTIVE

LINC00488 confers oncogenic activity in the progression of some tumors. Hence, the target of the study was about to specify LINC00488-mediated network in retinoblastoma (RB).

METHODS

LINC00488 expression was tested in RB clinical tissues. siRNA targeting LINC00488 or miR-30a-5p mimic was introduced into RB cell line (Y79) to observe cellular biological functions. The relationship between LINC00488, miR-30a-5p and EPHB2 was verified. Afterward, the role of miR-30a-5p involved in RB through targeted regulation of EPHB2 was probed in vitro and in vivo.

RESULTS

LINC00488 was induced in RB tissue and cells. LINC00488 knockdown or miR-30a-5p upregulation depressed the malignant activities of Y79 cells. LINC00488 could sponge miR-30a-5p that targeted EPHB2. EPHB2, and EPHB2 overexpression counteracted miR-30a-5p restoration-induced inhibition of Y79 cell development in vitro and in vivo.

CONCLUSION

LINC00488 induces tumorigenicity in RB by binding to miR-30a-5p to target EPHB2, which may offer a new clue of RB treatment from an lncRNA-miRNA-mRNA network.

Testing lncRNAs signature as clinical stage–related prognostic markers in gastric cancer progression using TCGA database

LncRNA expression can be conducive to gastric cancer (GC) prognosis. The objective of this study is to ascertain five specific lncRNAs involved in tumor progression of GC and their role as prognostic markers to diagnose clinical stage-wise GC. High-throughput RNA sequencing data were obtained from The Cancer Genome Atlas (TCGA) database and performed genome-wide lncRNA expression analysis using edgeR package, Bioconductor.org , and R-statistical computing to analyze differentially expressed lncRNA analysis. Cutoff parameters were FDR < 0.05 and |Log2FC| > 2. Total 351 tumor samples with differentially expressed lncRNAs were divided into group-1 lncRNAs such as AC019117.2 and LINC00941, and group-2 lncRNAs such as LINC02410, AC012317.2, and AC141273.1 by 2:1. The Spearman correlation coefficients ( p < 0.05) and correlation test function (cor.test ()) were performed for lncRNAs as per clinical stage. Cytoscape software was used to construct lncRNA–mRNA interaction networks. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway ( p < 0.05) analysis were conducted using the clusterProfiler package. Kaplan–Meier survival analysis was performed to determine the overall survival of patients based on the expression of five lncRNAs in different clinical stages of GC. AC019117.2 and LINC00941 of group 1 inferred a positive correlation with clinical stages of stage I to stage IV, and their expressions were higher in tumor tissues than normal tissues. On the contrary, LINC02410, AC012317.2, and AC141273.1 of group 2 exhibited a negative correlation with clinical stage, and they exhibited more expression in normal tissues compared to tumor tissues. GO and KEGG pathway analysis reported that AC019117.2 may interact with LINC00941 via ITGA3 and trophoblast glycoprotein (TPBG) to foster tumor progression. Tumor-specific group-1 lncRNAs were conducive to the poor overall survival and exhibited a positive correlation with the clinical stages of stage I to stage IV in GC as per the lncRNA–mRNA networking analysis. These five lncRNAs could be considered as clinically useful lncRNA-based prognostic markers to predict clinical stage-wise GC progression.

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.

Comprehensive analyses of correlation and survival reveal informative lncRNA prognostic signatures in colon cancer

BACKGROUND

Colon cancer is a commonly worldwide cancer with high morbidity and mortality. Long non-coding RNAs (lncRNAs) are involved in many biological processes and are closely related to the occurrence of colon cancer. Identification of the prognostic signatures of lncRNAs in colon cancer has great significance for its treatment.

METHODS

We first identified the colon cancer-related mRNAs and lncRNAs according to the differential analysis methods using the expression data in TCGA. Then, we performed correlation analysis between the identified mRNAs and lncRNAs by integrating their expression values and secondary structure information to estimate the co-regulatory relationships between the cancer-related mRNAs and lncRNAs. Besides, the competing endogenous RNA regulation network based on co-regulatory relationships was constructed to reveal cancer-related regulatory patterns. Meanwhile, we used traditional regression analysis (univariate Cox analysis, random survival forest analysis, and lasso regression analysis) to screen the cancer-related lncRNAs. Finally, by combining the identified colon cancer-related lncRNAs according to the above analyses, we constructed a risk prognosis model for colon cancer through multivariate Cox analysis and also validated the model in the colon cancer dataset in TCGA cohorts.

RESULTS

Six lncRNAs were found highly correlated with the overall survival of colon cancer patients, and a risk prognosis model based on them was constructed to predict the overall survival of colon cancer patients. In particular, EVX1-AS, ZNF667-AS1, CTC-428G20.6, and CTC-297N7.9 were first reported to be related to colon cancer by using our model, among which EVX1-AS and ZNF667-AS1 have been predicted to be related to colon cancer in LncRNADisease database.

CONCLUSIONS

This study identified the potential regulatory relationships between lncRNAs and mRNAs by integrating their expression values and secondary structure information and presented a significant 6-lncRNA risk prognosis model to predict the overall survival of colon cancer patients.

A Nomogram Combining a Four-Gene Biomarker and Clinical Factors for Predicting Survival of Melanoma

Background

Currently there is no effective prognostic indicator for melanoma, the deadliest skin cancer. Thus, we aimed to develop and validate a nomogram predictive model for predicting survival of melanoma.

Methods

Four hundred forty-nine melanoma cases with RNA sequencing (RNA-seq) data from TCGA were randomly divided into the training set I (n = 224) and validation set I (n = 225), 210 melanoma cases with RNA-seq data from Lund cohort of Lund University (available in GSE65904) were used as an external test set. The prognostic gene biomarker was developed and validated based on the above three sets. The developed gene biomarker combined with clinical characteristics was used as variables to develop and validate a nomogram predictive model based on 379 patients with complete clinical data from TCGA (Among 470 cases, 91 cases with missing clinical data were excluded from the study), which were randomly divided into the training set II (n = 189) and validation set II (n = 190). Area under the curve (AUC), concordance index (C-index), calibration curve, and Kaplan-Meier estimate were used to assess predictive performance of the nomogram model.

Results

Four genes, i.e., CLEC7A, CLEC10A, HAPLN3, and HCP5 comprise an immune-related prognostic biomarker. The predictive performance of the biomarker was validated using tROC and log-rank test in the training set I (n = 224, 5-year AUC of 0.683), validation set I (n = 225, 5-year AUC of 0.644), and test set I (n = 210, 5-year AUC of 0.645). The biomarker was also significantly associated with improved survival in the training set (P < 0.01), validation set (P < 0.05), and test set (P < 0.001), respectively. In addition, a nomogram combing the four-gene biomarker and six clinical factors for predicting survival in melanoma was developed in the training set II (n = 189), and validated in the validation set II (n = 190), with a concordance index of 0.736 ± 0.041 and an AUC of 0.832 ± 0.071.

Conclusion

We developed and validated a nomogram predictive model combining a four-gene biomarker and six clinical factors for melanoma patients, which could facilitate risk stratification and treatment planning.

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 and validation of an autophagy-related long noncoding RNA signature for prognosis prediction in kidney renal clear cell carcinoma patients

Purpose

The purpose of this study was to identify autophagy‐associated long noncoding RNAs (ARlncRNAs) using the kidney renal clear cell carcinoma (KIRC) patient data from The Cancer Genome Atlas (TCGA) database and to construct a prognostic risk‐related ARlncRNAs signature to accurately predict the prognosis of KIRC patients.

Methods

The KIRC patient data were originated from TCGA database and were classified into a training set and testing set. Seven prognostic risk‐related ARlncRNAs, identified using univariate, lasso, and multivariate Cox regression analysis, were used to construct prognostic risk‐related signatures. Kaplan–Meier curves and receiver operating characteristic (ROC) curves as well as independent prognostic factor analysis and correlation analysis with clinical characteristics were utilized to evaluate and verify the specificity and sensitivity of the signature in training set and testing set, respectively. Two nomograms were established to predict the probable 1‐, 3‐, and 5‐year survival of the KIRC patients. In addition, the lncRNA‐mRNA co‐expression network was constructed and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to identify biological functions of ARlncRNAs.

Results

We constructed and verified a prognostic risk‐related ARlncRNAs signature in training set and testing set, respectively. We found the survival time of KIRC patients with low‐risk scores was significantly better than those with high‐risk scores in training set and testing set. ROC curves suggested that the area under the ROC (AUC) value for prognostic risk score signature was 0.81 in training set and 0.705 in testing set. And AUC values corresponding to 1‐, 3‐, and 5 years of OS were 0.809, 0.753, and 0.794 in training set and 0.698, 0.682, and 0.754 in testing set, respectively. We established the two nomograms that confirmed high C‐index and accomplished good prediction accuracy.

Conclusions

We constructed a prognostic risk‐related ARlncRNAs signature that could accurately predict the prognosis of KIRC patients.

Long non-coding RNA LINC00488 facilitates thyroid cancer cell progression through miR-376a-3p/PON2

Objective: Long non-coding RNAs (lncRNAs) recently have been identified as influential indicators in a variety of malignancies. The aim of the present study was to identify a functional lncRNA LINC00488 and its effects on thyroid cancer in the view of cell proliferation and apoptosis.

Methods: In order to evaluate the effects of LINC00488 on the cellular process of thyroid cancer, we performed a series of in vitro experiments, including cell counting kit-8 (CCK-8) assay, EdU (5-ethynyl-2′-deoxyuridine) assay, flow cytometry, transwell chamber assay, Western blot and RT-qPCR. The target gene of LINC00488 was then identified by bioinformatics analysis (DIANA and TargetScan). Finally, a series of rescue experiments was conducted to validate the effect of LINC00488 and its target genes on proliferation, migration, invasion and apoptosis of thyroid cancer.

Results: Our findings revealed that LINC00488 was highly expressed in thyroid cancer cell lines (BCPAP, BHP5-16, TPC-1 and CGTH-W3) and promoted the proliferation, migration and invasion, while inhibited the apoptosis of thyroid cancer cells (BCPAP and TPC-1). The results of bioinformatics analysis and dual luciferase reporter gene assay showed that LINC00488 could directly bind to miR-376a-3p and down-regulated the expression level of miR-376a-3p. In addition, Paraoxonase-2 (PON2) was a target gene of miR-376a-3p and negatively regulated by miR-376a-3p. Rescue experiment indicated that LINC00488 might enhance PON2 expression by sponging miR-376a-3p in thyroid cancer.

Conclusion: Taken together, our study revealed that lncRNA LINC00488 acted as an oncogenic gene in the progression of thyroid cancer via regulating miR-376a-3p/PON2 axis, which indicated that LINC00488-miR-376a-3p-PON2 axis could serve as novel biomarkers or potential targets for the treatment of thyroid cancer.

Radiomics-based machine learning model to predict risk of death within 5-years in clear cell renal cell carcinoma patients

PURPOSE

The aim of this study was to develop radiomics-based machine learning models based on extracted radiomic features and clinical information to predict the risk of death within 5 years for prognosis of clear cell renal cell carcinoma (ccRCC) patients.

METHODS

According to image quality and clinical data availability, we eventually selected 70 ccRCC patients that underwent CT scans. Manual volume-of-interest (VOI) segmentation of each image was performed by an experienced radiologist using the 3D slicer software package. Prior to feature extraction, image pre-processing was performed on CT images to extract different image features, including wavelet, Laplacian of Gaussian, and resampling of the intensity values to 32, 64 and 128 bin levels. Overall, 2544 3D radiomics features were extracted from each VOI for each patient. Minimum Redundancy Maximum Relevance (MRMR) algorithm was used as feature selector. Four classification algorithms were used, including Generalized Linear Model (GLM), Support Vector Machine (SVM), K-nearest Neighbor (KNN) and XGBoost. We used the Bootstrap resampling method to create validation sets. Area under the receiver operating characteristic (ROC) curve (AUROC), accuracy, sensitivity, and specificity were used to assess the performance of the classification models.

RESULTS

The best single performance among 8 different models was achieved by the XGBoost model using a combination of radiomic features and clinical information (AUROC, accuracy, sensitivity, and specificity with 95% confidence interval were 0.95-0.98, 0.93-0.98, 0.93-0.96 and ~1.0, respectively).

CONCLUSIONS

We developed a robust radiomics-based classifier that is capable of accurately predicting overall survival of RCC patients for prognosis of ccRCC patients. This signature may help identifying high-risk patients who require additional treatment and follow up regimens.

Long non-coding RNA LINC01234 regulates proliferation, migration and invasion via HIF-2α pathways in clear cell renal cell carcinoma cells

Long non-coding RNAs (lncRNAs) have been proved to have an important role in different malignancies including clear cell renal cell carcinoma (ccRCC). However, their role in disease progression is still not clear. The objective of the study was to identify lncRNA-based prognostic biomarkers and further to investigate the role of one lncRNA LINC01234 in progression of ccRCC cells. We found that six adverse prognostic lncRNA biomarkers including LINC01234 were identified in ccRCC patients by bioinformatic analysis using The Cancer Genome Atlas database. LINC01234 knockdown impaired cell proliferation, migration and invasion in vitro as compared to negative control. Furthermore, the epithelial-mesenchymal transition was inhibited after LINC01234 knockdown. Additionally, LINC01234 knockdown impaired hypoxia-inducible factor-2a (HIF-2α) pathways, including a suppression of the expression of HIF-2α, vascular endothelial growth factor A, epidermal growth factor receptor, c-Myc, Cyclin D1 and MET. Together, these datas showed that LINC01234 was likely to regulate the progression of ccRCC by HIF-2α pathways, and LINC01234 was both a promising prognostic biomarker and a potential therapeutic target for ccRCC.

Quantitative Proteomics Analysis Indicates That Upregulation of lncRNA HULC Promotes Pathogenesis of Glioblastoma Cells

Purpose

Glioblastoma (GBM) is an aggressive central nervous system (CNS) cancer and a serious threat to human health. The long noncoding RNA (lncRNA) HULC has been implicated in GBM, but the molecular mechanism is uncertain. This study used quantitative proteomic analysis for global identification of HULC-regulated proteins in glioblastoma cells and identification of potential biomarkers.

Materials and Methods

qRT-PCR was used to determine the expression of HULC in U87 cells stably transfected with HULC or an empty vector (control). The CCK-8 assay, transwell assay, and wound-scratch assay were used to measure cell proliferation, invasion, and migration. Quantitative proteomics using Tandem Mass Tag (TMT) labeling, high-performance liquid chromatography (HPLC) fractionation, and liquid chromatography–mass spectrometry (LC-MS/MS) analysis were used to identify differentially expressed proteins (DEPs). Screened proteins were validated by parallel reaction monitoring (PRM) and Western blotting.

Results

Overexpression of HULC led to increased cell proliferation, invasion, and migration. HULC overexpression also led to significant upregulation of 37 proteins and downregulation of 78 proteins. Bioinformatics analysis indicated these proteins had roles in cellular component, biological process, and molecular function. PRM results of 8 of these proteins (PTK2, TNC, ITGAV, LASP1, MAPK14, ITGA1, GNA13, RRAS) were consistent with the LC-MS/MS and Western blotting results.

Conclusion

The results of present study suggest that lncRNA HULC promotes GBM cell proliferation, invasion, and migration by regulating RRAS expression, suggesting that RRAS may be a potential biomarker or therapeutic target for this cancer.

lncRNA MALAT1 contributes to neuropathic pain development through regulating miR-129-5p/HMGB1 axis in a rat model of chronic constriction injury

Backgrounds: Mounting studies pay attention to the functional roles of long non-coding RNAs (lncRNAs) in many human diseases including neuropathic pain. LncRNA MALAT1 has been indicated to serve as a critical mediator in neuropathic pain with unclear mechanisms. The present study aims to explore the functional roles of MALAT1 in neuropathic pain progression and the related mechanisms.Methods: Bilateral sciatic nerves were ligated to induce chronic constriction injury (CCI) in order to establish the neuropathic pain rat model followed by behavioral tests, RT-qPCR, Western blotting, and ELISA. Dual luciferase activity assay was performed to determine the binding effect between MALAT1 or HMGB1 and miR-129-5p.Results: The mRNA levels of MALAT1 were significantly enhanced in CCI rats. MALAT1 inhibition repressed the development of neuropathic pain and neuroinflammation. Additionally, miR-129-5p was decreased and HMGB1 was increased, both of which could be rectified by MALAT1 inhibition. Meanwhile, MALAT1 targeted miR-129-5p/HMGB1 axis. Finally, miR-129-5p suppression attenuated the inhibitory effect of MALAT1 inhibition on neuropathic pain and neuroinflammation development in CCI rats.Conclusion: The present study demonstrates that MALAT1 might modulate neuropathic pain via targeting miR-129-5p/HMGB1 axis. These findings may lead to a promising and efficacious clinical approach for the treatment of neuropathic pain.