A prognostic 4-lncRNA expression signature for lung squamous cell carcinoma

Identification of prognosis-related lncRNAs and cell validation in lung squamous cell carcinoma based on TCGA data

Objective

To discern long non-coding RNAs (lncRNAs) with prognostic relevance in the context of lung squamous cell carcinoma (LUSC), we intend to predict target genes by leveraging The Cancer Genome Atlas (TCGA) repository. Subsequently, we aim to investigate the proliferative potential of critical lncRNAs within the LUSC milieu.

Methods

DESeq2 was employed to identify differentially expressed genes within the TCGA database. Following this, we utilized both univariate and multivariate Cox regression analyses to identify lncRNAs with prognostic relevance. Noteworthy lncRNAs were selected for validation in cell lines. The intracellular localization of these lncRNAs was ascertained through nucleocytoplasmic isolation experiments. Additionally, the impact of these lncRNAs on cellular proliferation, invasion, and migration capabilities was investigated using an Antisense oligonucleotides (ASO) knockdown system.

Results

Multivariate Cox regression identified a total of 12 candidate genes, consisting of seven downregulated lncRNAs (BRE-AS1, CCL15-CCL14, DNMBP-AS1, LINC00482, LOC100129034, MIR22HG, PRR26) and five upregulated lncRNAs (FAM83A-AS1, LINC00628, LINC00923, LINC01341, LOC100130691). The target genes associated with these lncRNAs exhibit significant enrichment within diverse biological pathways, including metabolic processes, cancer pathways, MAPK signaling, PI3K-Akt signaling, protein binding, cellular components, cellular transformation, and other functional categories. Furthermore, nucleocytoplasmic fractionation experiments demonstrated that LINC00923 and LINC01341 are predominantly localized within the cellular nucleus. Subsequent investigations utilizing CCK-8 assays and colony formation assays revealed that the knockdown of LINC00923 and LINC01341 effectively suppressed the proliferation of H226 and H1703 cells. Additionally, transwell assays showed that knockdown of LINC00923 and LINC01341 significantly attenuated the invasive and migratory capacities of H226 and H1703 cells.

Conclusion

This study has identified 12 candidate lncRNA associated with prognostic implications, among which LINC00923 and LINC01341 exhibit potential as markers for the prediction of LUSC outcomes.

Prognostic Signature and Tumor Immune Landscape of N7-Methylguanosine-Related lncRNAs in Hepatocellular Carcinoma

Despite great advances in the treatment of liver hepatocellular carcinoma (LIHC), such as immunotherapy, the prognosis remains extremely poor, and there is an urgent need to develop novel diagnostic and prognostic markers. Recently, RNA methylation-related long non-coding RNAs (lncRNAs) have been demonstrated to be novel potential biomarkers for tumor diagnosis and prognosis as well as immunotherapy response, such as N6-methyladenine (m6A) and 5-methylcytosine (m5C). N7-Methylguanosine (m7G) is a widespread RNA modification in eukaryotes, but the relationship between m7G-related lncRNAs and prognosis of LIHC patients as well as tumor immunotherapy response is still unknown. In this study, based on the LIHC patients’ clinical and transcriptomic data from TCGA database, a total of 992 m7G-related lncRNAs that co-expressed with 22 m7G regulatory genes were identified using Pearson correlation analysis. Univariate regression analysis was used to screen prognostic m7G-related lncRNAs, and the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression were applied to construct a 9-m7G-related-lncRNA risk model. The m7G-related lncRNA risk model was validated to exhibit good prognostic performance through Kaplan–Meier analysis and ROC analysis. Together with the clinicopathological features, the m7G-related lncRNA risk score was found to be an independent prognostic factor for LIHC. Furthermore, the high-risk group of LIHC patients was unveiled to have a higher tumor mutation burden (TMB), and their tumor microenvironment was more prone to the immunosuppressive state and exhibited a lower response rate to immunotherapy. In addition, 47 anti-cancer drugs were identified to exhibit a difference in drug sensitivity between the high-risk and low-risk groups. Taken together, the m7G-related lncRNA risk model might display potential value in predicting prognosis, immunotherapy response, and drug sensitivity in LIHC patients.

LncRNA FGD5-AS1 functions as an oncogene to upregulate GTPBP4 expression by sponging miR-873-5p in hepatocellular carcinoma

The long non-coding FGD5-AS1 (LncFGD5-AS1) has been reported to be a novel carcinogenic gene and participant in regulating tumor progression by sponging microRNAs (miRNAs). However, the pattern of expression and the biological role of FGD5-AS1 in hepatocellular carcinoma (HCC) remains largely unknown. The expression level of FGD5-AS1 in tumor tissues and cell lines was measured by RT-qPCR. CCK-8, EdU, flow cytometry, wound healing and transwell chamber assays were performed to investigate the role of FGD5-AS1 in cell proliferation, apoptosis, migration, and invasion in HCC. Dual luciferase reporter, and RNA pull-down assays were performed to identify the regulatory interactions among FGD5-AS1, miR-873-5p and GTP-binding protein 4 (GTPBP4). We found that the expression of FGD5-AS1 was upregulated in HCC tissues and cell lines. Moreover, the knockdown of FGD5-AS1 suppressed cell proliferation, migration and invasion, and induced apoptosis in HCC cells. Further studies demonstrated that FGD5-AS1 could function as a competitive RNA by sponging miR-873-5p in HCC cells. Moreover, GTPBP4 was identified as direct downstream target of miR-873-5p in HCC cells and FGD5-AS1mediated the effects of GTPBP4 by competitively binding with miR-873-5p. Taken together, this study demonstrated the regulatory role of FGD5-AS1 in the progression of HCC and identified the miR-873-5p/GTPBP4 axis as the direct downstream pathway. It represents a promising novel therapeutic strategy for HCC patients.

Long non‑coding RNA 00858 knockdown alleviates bladder cancer via regulation of the miR‑3064‑5p/CTGF axis

The long non-coding RNA 00858 (LINC00858) has been reported to be an oncogene for various cancer diseases, including osteosarcoma and colorectal cancer. However, the expression pattern and function of LINC00858 in bladder cancer remain largely unknown. The expression level of LINC00858 was measured in tumor tissues and cell lines by RT-qPCR. The role of LINC00858 in bladder cancer cells were studied by gain- and loss-of-function strategies in vitro. Cell proliferation, migration and invasion were assessed by CCK-8, colony formation, wound healing and Transwell chamber assays. At the molecular level, dual luciferase reporter and RNA RIP assays were performed to identify the interaction among LINC00858, microRNA (miR)-3064-5p and cellular communication network factor 2 (CTGF). The results revealed that the expression level of LINC00858 was upregulated in bladder cancer tissues and cell lines including T24, J82 and 5637. Moreover, knockdown of LINC00858 suppressed cell proliferation, migration and invasion in vitro. Mechanistically, LINC00858 functioned as a competitive RNA to increase the expression level of oncogene CTGF by sequestering miR-3064-5p. In conclusion, LINC00858 knockdown inhibited the proliferation, migration and invasion of bladder cancer cells via regulation of the miR-3064-5p/CTGF axis.

A Glycolysis-Related Five-Gene Signature Predicts Biochemical Recurrence-Free Survival in Patients With Prostate Adenocarcinoma

Prostate cancer (PCa) is one of the most frequently diagnosed cancers in males worldwide. Approximately 25% of all patients experience biochemical recurrence (BCR) after radical prostatectomy (RP) and BCR indicates increased risk for metastasis and castration resistance. PCa patients with highly glycolytic tumors have a worse prognosis. Thus, this study aimed to explore glycolysis-based predictive biomarkers for BCR. Expression data and clinical information of PCa samples were retrieved from three publicly available datasets. One from The Cancer Genome Atlas (TCGA) dataset was used as the training cohort, and two from the Gene Expression Omnibus (GEO) dataset (GSE54460 and GSE70769) were used as validation cohorts. Using the training cohort, univariate Cox regression survival analysis, robust likelihood-based survival model, and stepwise multiply Cox analysis were sequentially applied to explore predictive glycolysis-related candidates. A five-gene risk score was then constructed based on the Cox coefficient as the following: (−0.8367*GYS2) + (0.3448*STMN1) + (0.3595*PPFIA4) + (−0.1940*KDELR3) + (0.4779*ABCB6). Receiver operating characteristic curve (ROC) analysis was used to identify the optimal cut-off point, and patients were divided into low risk and high risk groups. Kaplan–Meier analysis revealed that high risk group had significantly shorter BCR free survival time as compared with that in low risk group in training and validation cohorts. In conclusion, our data support the glycolysis-based five-gene signature as a novel and robust signature for predicting BCR of PCa patients.

A gene-based risk score model for predicting recurrence-free survival in patients with hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) remains the most frequent liver cancer, accounting for approximately 90% of primary liver cancers worldwide. The recurrence-free survival (RFS) of HCC patients is a critical factor in devising a personal treatment plan. Thus, it is necessary to accurately forecast the prognosis of HCC patients in clinical practice.

METHODS

Using The Cancer Genome Atlas (TCGA) dataset, we identified genes associated with RFS. A robust likelihood-based survival modeling approach was used to select the best genes for the prognostic model. Then, the GSE76427 dataset was used to evaluate the prognostic model's effectiveness.

RESULTS

We identified 1331 differentially expressed genes associated with RFS. Seven of these genes were selected to generate the prognostic model. The validation in both the TCGA cohort and GEO cohort demonstrated that the 7-gene prognostic model can predict the RFS of HCC patients. Meanwhile, the results of the multivariate Cox regression analysis showed that the 7-gene risk score model could function as an independent prognostic factor. In addition, according to the time-dependent ROC curve, the 7-gene risk score model performed better in predicting the RFS of the training set and the external validation dataset than the classical TNM staging and BCLC. Furthermore, these seven genes were found to be related to the occurrence and development of liver cancer by exploring three other databases.

CONCLUSION

Our study identified a seven-gene signature for HCC RFS prediction that can be used as a novel and convenient prognostic tool. These seven genes might be potential target genes for metabolic therapy and the treatment of HCC.

A Prognostic 14-Gene Expression Signature for Lung Adenocarcinoma: A Study Based on TCGA Data Mining

Background

Lung adenocarcinoma (LUAD), a major and fatal subtype of lung cancer, caused lots of mortalities and showed different outcomes in prognosis. This study was to assess key genes and to develop a prognostic signature for the patient therapy with LUAD.

Method

RNA expression profile and clinical data from 522 LUAD patients were accessed and downloaded from the Cancer Genome Atlas (TCGA) database. Differentially expressed genes (DEGs) were extracted and analyzed between normal tissues and LUAD samples. Then, a 14-DEG signature was developed and identified for the survival prediction in LUAD patients by means of univariate and multivariate Cox regression analyses. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed to predict the potential biological functions and pathways of these DEGs.

Results

Twenty-two out of 5924 DEGs in the TCGA dataset were screened and associated with the overall survival (OS) of LUAD patients. 14CID="C008" value=" "DEGs were finally selected and included in our development and validation model by risk score analysis. The ROC analysis indicated that the specificity and sensitivity of this profile signature were high. Further functional enrichment analyses indicated that these DEGs might regulate genes that affect the function of release of sequestered calcium ion into cytosol and pathways that associated with vibrio cholerae infection.

Conclusion

Our study developed a novel 14-DEG signature providing more efficient and persuasive prognostic information beyond conventional clinicopathological factors for survival prediction of LUAD patients.

Six-gene-based prognostic model predicts overall survival in patients with uveal melanoma

BACKGROUND

Uveal melanoma (UM) is the most common primary intraocular tumor in adults, which has a high mortality rate and worse prognosis. Therefore, early potential molecular detection and prognostic evaluation seem more important for early diagnosis and treatment.

METHODS

Gene expression data were obtained from The Cancer Genome Atlas-Uveal melanomas database. Survival genes were identified by univariate analysis and were regarded to be associated with the overall survival of UM patients. Then, pathway enrichment analysis of these survival genes was performed. Robust likelihood-based survival model and multivariate survival analysis were conducted to identify more reliable genes and the prognostic signature for UM survival prediction. Two internal datasets and another two UM datasets from Gene Expression Omnibus (GEO) were used for the validation of prognostic signature.

RESULTS

Firstly, 2,010 survival genes were screened by univariate survival analysis. GO and KEGG analysis revealed that these genes were mainly involved in pathways such as mRNA processing, RNA splicing, spliceosome and ubiquitin mediated proteolysis. Secondly, a six-gene signature was identified by Robust likelihood-based survival model approach. The gene expression of the six genes can successfully divide UM samples into high- and low-risk groups and have strong survival prediction ability. What's more, the expression of six genes was compared in 80 healthy adipose tissue samples obtained from GTEx (Genotype-Tissue Expression) database and further validated in internal datasets and GEO datasets, which also can predict UM patient survival.

CONCLUSIONS

The six genes (SH2D3A, TMEM201, LZTS1, CREG1, NIPA1 and HIST1H4E) model might play a vital role in prognosis of UM, which should be helpful for further insight into the treatment of uveal melanoma.

LncRNA STARD13-AS blocks lung squamous carcinoma cells growth and movement by targeting miR-1248/C3A

BACKGROUND

This research aims to illustrate the effect of lncRNA StAR Related Lipid Transfer Domain Containing 13 antisense RN (STARD13-AS)/miR-1248/C3A on lung squamous carcinoma cells growth and metastasis.

METHODS

Bioinformatics analysis was applied to detect the expression of STARD13-AS/miR-1248/C3A in lung cancer samples and establish the ceRNA network. Transfection was performed to construct over-expression or knockdown models. PCR was implemented to examine the transfection efficiency. The biological function including growth, invasion and migration of LUSC cells were estimated by CCK-8 analysis, colony formation assay and transwell assay. Luciferase assay was executed to analyze the relationship between C3A and miR-1248, as well as miR-1248 and STARD13-AS.

RESULTS

By consulting the TCGA database and GEPIA website, we found that C3A expression was significantly reduced in LUSC samples. Additionally, we also discovered that miR-1248, which was a downstream target of STARD13-AS, was presented as an upstream regulator of C3A. Moreover, STARD13-AS was under expressed in LUSC cells and has a negative effect on LUSC cells growth ability. C3A expression was co-regulated by miR-1248 and STARD13-AS. Importantly, the inhibitory effect of C3A or the promoting effect of miR-1248 on LUSC cells growth, invasion and migration abilities can be regulated by STARD13-AS.

CONCLUSIONS

Our findings revealed that overexpression of STARD13-AS restricted the growth and aggressiveness of LUSC cells via regulating miR-1248/C3A.

Long Noncoding MAGI2-AS3 Suppresses Several Cellular Processes of Lung Squamous Cell Carcinoma Cells by Regulating miR-374a/b-5p/CADM2 Axis

Background

Lung squamous cell carcinoma (LUSC) accounts for approximately 30% of all lung cancers that possesses the highest occurrence and mortality in all cancer types. Long noncoding RNAs have been reported to modulate tumor development for several decades.

Aim of the Study

This research aims to investigate the role of MAGI2-AS3 in LUSC.

Methods

RT-qPCR tested genes (including MAGI2-AS3, miR-374a/b-5p and CADM2) expression. Cell proliferation was detected by colony formation and EdU assays. Cell migration and invasion were evaluated by transwell assay. Flow cytometry analysis of apoptotic cells and Western blot analysis on apoptosis-related genes were applied to measure cell apoptosis. Nuclear-cytoplasmic fractionation and FISH assay positioned MAGI2-AS3. The combination between miR-374a/b-5p and MAGI2-AS3 (or CADM2) was determined by luciferase reporter assay and RIP assay.

Results

MAGI2-AS3 inhibited the proliferative, migratory and invasive capability of LUSC cells with upregulated expression. Additionally, MAGI2-AS3 overexpression promoted cell apoptosis. We discovered that MAGI2-AS3 was located in the cytoplasm. Hereafter, we found out that MAGI2-AS3 targeted miR-374a/b-5p. CADM2 was targeted by miR-374a/b-5p. Finally, rescue assays indicated that the promoting effects of miR-374a/b-5p amplification on biological activities were restored by CADM2 addition.

Conclusion

In conclusion, lncRNA MAGI2-AS3 suppressed LUSC by regulating miR-374a/b-5p/CADM2 axis, which might potentially serve as a therapeutic marker for LUSC patients.

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion: Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

Identification of a 6-lncRNA prognostic signature based on microarray re-annotation in gastric cancer

Gastric cancer (GC) remains an important malignancy worldwide with poor prognosis. Long noncoding RNAs (lncRNAs) can markedly affect cancer progression. Moreover, lncRNAs have been proposed as diagnostic or prognostic biomarkers of GC. Therefore, the current study aimed to explore lncRNA‐based prognostic biomarkers for GC. LncRNA expression profiles from the Gene Expression Omnibus (GEO) database were first downloaded. After re‐annotation of lncRNAs, a univariate Cox analysis identified 177 prognostic lncRNA probes in the training set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 (n = 225). Multivariate Cox analysis of each lncRNA with clinical characteristics as covariates identified a total of 46 prognostic lncRNA probes. Robust likelihood‐based survival and least absolute shrinkage and selection operator (LASSO) models were used to establish a 6‐lncRNA signature with prognostic value. Receiver operating characteristic (ROC) curve analyses were employed to compare survival prediction in terms of specificity and sensitivity. Patients with high‐risk scores exhibited a significantly worse overall survival (OS) than patients with low‐risk scores (log‐rank test P‐value <.0001), and the area under the ROC curve (AUC) for 5‐year survival was 0.77. A nomogram and forest plot were constructed to compare the clinical characteristics and risk scores by a multivariable Cox regression analysis, which suggested that the 6‐lncRNA signature can independently make the prognosis evaluation of patients. Single‐sample GSEA (ssGSEA) was used to determine the relationships between the 6‐lncRNA signature and biological functions. The internal validation set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62254 (n = 75) and the external validation set http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE57303 (n = 70) were successfully used to validate the robustness of our 6‐lncRNA signature. In conclusion, based on the above results, the 6‐lncRNA signature can effectively make the prognosis evaluation of GC patients.

Circulating plasma lncRNAs as novel markers of EGFR mutation status and monitors of epidermal growth factor receptor-tyrosine kinase inhibitor therapy

Background

Epidermal growth factor receptor (EGFR) gene mutations predict tumor response to EGFR tyrosine kinase inhibitors (EGFR‐TKIs) in non‐small cell lung cancer (NSCLC). However, even patients with EGFR‐sensitive mutations in NSCLC have limited efficacy with EGFR‐TKI. Studies have shown that long noncoding RNA (lncRNA) is related to diagnosis and prognosis with NSCLC. This study aimed to explore the correlation between lncRNA in NSCLC patients with EGFR mutation status and EGFR‐TKI efficacy.

Methods

The amplification‐refractory mutation system method was used to test the EGFR mutation status in tumor tissues and pleural effusions of NSCLC patients. Three EGFR‐mutant patients and three EGFR wild‐type patients were selected. Differential lncRNA was performed on the pleural effusions of the two selected groups of patients using Clariom D Human chip technology. Five lncRNAs significantly associated with EGFR mutation status were screened by FC value and GO analysis, and then evaluated by real‐time quantitative polymerase chain reaction in NSCLC patients' pleural effusions. Three were further analyzed in NSCLC patients' plasma.

Results

There were 61 significant differences in lncRNA between EGFR mutation‐positive and wild‐type patients. Among them, SCARNA7, MALAT1, NONHSAT017369, NONHSAT051892, and FTH1P2 were significantly associated with EGFR mutation status. SCARNA7, MALAT1, and NONHSAT017369 showed consistent results with plasma in pleural effusions compared to EGFR wild‐type, all upregulated in the EGFR mutation group.

Conclusion

This study shows that lncRNAs can be used not only as potential biomarkers for predicting the mutation status of EGFR and the efficacy of EGFR‐TKI, but also for monitoring the efficacy of EGFR‐TKI.

A five-long non-coding RNA signature with the ability to predict overall survival of patients with lung adenocarcinoma

An increasing number of studies have indicated that the abnormal expression of certain long non-coding RNAs (lncRNAs) is linked to the overall survival (OS) of patients with lung adenocarcinoma (LUAD). The aim of the present study was to establish an lncRNA signature to predict the survival of patients with LUAD. The gene expression profiles and associated clinical information of patients with LUAD were downloaded from The Cancer Genome Atlas database. The cohort was randomly sub-divided into training and verification cohorts. Univariate Cox regression analysis was performed on differentially expressed lncRNAs in the training cohort to select candidate lncRNAs closely associated with survival. Next, a risk score (RS) model consisting of 5 lncRNAs was established by multivariate Cox regression analysis on candidate lncRNAs. Using the median RS obtained from the training cohort as a cut-off point, patients were classified into high- and low-risk groups. Kaplan-Meier survival analysis revealed a significant difference in OS between high- and low-risk groups. The survival prediction ability of the 5-lncRNA signature was further tested in the verification and total cohorts. The results proved that the 5-lncRNA signature had good reliability and stability in survival prediction for patients with LUAD. The univariate Cox regression analysis for the 5-lncRNA signature in each cohort indicated that the 5-lncRNA signature was closely associated with survival. Multivariate Cox regression analysis and stratification analysis proved that the prognostic signature was an independent predictor of survival for patients with LUAD. In addition, functional enrichment analysis indicated that the 5 prognostic lncRNAs may be involved in the tumorigenesis of LUAD through cancer-associated pathways and biological processes. Taken together, the present study provided a 5-lncRNA signature that may serve as an independent survival predictor for patients with LUAD.

Comprehensive Analysis of a Competing Endogenous RNA Network Identifies Seven-lncRNA Signature as a Prognostic Biomarker for Melanoma

Long non-coding RNAs (LncRNAs) can act as competing endogenous RNA (ceRNA) involving in tumor initiation and progression. Nevertheless, the prognostic roles of lncRNAs in lncRNA-related ceRNA network of melanoma remain elusive. In this study, RNA sequence profiles were downloaded from The Cancer Genome Atlas (TCGA) database, and there were 2020 differentially expressed messenger RNAs (DEmRNAs), 438 differentially expressed lncRNAs (DElncRNAs) and 65 differentially expressed microRNAs (DEmiRNAs) between primary and metastasis melanoma patients. A ceRNA regulatory network was constructed based on the DElncRNAs-DEmiRNAs and DEmiRNAs-DEmRNAs interactions, which contained 39 lncRNAs, 10 miRNAs, and 16 mRNAs. Furthermore, univariate and multivariate Cox regression analysis were carried out to establish a 7-lncRNA prognostic signature. Subsequently, the area under the curve (AUC) value of the receiver operating characteristic (ROC) curve and the Kaplan-Meier risk survival analysis revealed the significant performance of this signature. Finally, pathway enrichment analyses implied that lncRNA MIR205HG and MIAT were associated with multiple cancer-related pathways, especially epidermis development and immune response. The current study provides novel insights into the lncRNA-related ceRNA network and the potential of lncRNAs to be candidate prognostic biomarkers and therapeutic targets in melanoma.

A nomogram based on 9-lncRNAs signature for improving prognostic prediction of clear cell renal cell carcinoma

Background

Abnormal expressions of long noncoding RNAs (lncRNAs) are very common in clear cell renal cell carcinoma (ccRCC), and some of these have been reported to be highly correlated with prognosis of ccRCC patients.

Methods

“edgeR” AND “DEseq” R packages were used to explore differentially expressed genes (DEGs) between normal and tumor tissues of ccRCC samples from The Cancer Genome Atlas (TCGA). Univariable Cox survival analysis, robust likelihood-based survival model and multivariable Cox regression analysis were used to identify prognostic lncRNAs and construct lncRNAs signature. Finally, a graphic nomogram based on the lncRNAs signature was developed to predict 1-, 3- and 5-year survival probability of ccRCC patients by using rms R package.

Results

8413 DEGs including 2740 lncRNAs and 4530 mRNAs were identified between normal and tumor tissues. 395 lncRNAs were found to be associated with prognosis of ccRCC patients (P < 0.05). Among these 395 prognostic lncRNAs, 9 key prognostic lncRNAs (RP13-463N16.6, CTD-2201E18.5, RP11-430G17.3, AC005785.2, RP11-2E11.9, TFAP2A-AS1, RP11-133F8.2, RP11-297L17.2 and RP11-348J24.2) were identified by using robust likelihood-based survival model. A 9-lncRNAs signature was constructed by using estimated regression coefficients of the 9 key prognostic lncRNAs. Results of χ²-test or Fisher’s exact test indicated that the 9-lncRNAs signature was significantly associated with clinicopathological characteristics such as tumor grade, T stage, N stage, M stage, TNM stage and survival outcome of ccRCC patients. Multivariate analysis showed that the 9-lncRNAs signature, age and M stage were independent prognostic factors. Finally, a graphic nomogram based on the lncRNAs signature, age and M stage was developed to predict 1-, 3- and 5-year survival probability of ccRCC patients by using rms R package.

Conclusions

A 9-lncRNAs signature associated with prognosis of ccRCC patients was constructed and a promising prognostic nomogram based on the 9-lncRNAs signature was developed for 1-, 3- and 5-year OS prediction of ccRCC patients in this study.

Prognostic 4-lncRNA-based risk model predicts survival time of patients with head and neck squamous cell carcinoma

Head and neck squamous cell carcinoma (HNSCC) is a common malignant disease with high mortality rates. Recently, long non-coding RNAs (lncRNAs) have been demonstrated to participate in a number of important biological functions and could serve as prognostic biomarkers in the field of oncology. Therefore, the present study aimed to identify an lncRNA-based model that was associated with prognosis. RNA-sequencing data was downloaded from The Cancer Genome Atlas and R software was used to analyze the data. Univariate analyses, robust likelihood analyses and multivariate analyses were performed to screen out key lncRNA candidates associated with prognosis and construct a risk model. A Kaplan-Meier plot was constructed for survival analysis. LncBase and Starbase were used to identify the miRNA and protein targets. Gene set enrichment analysis was used for functional analysis. As a result, a 4-lncRNA (ALMS1-IT1, RP11-359J14.2, CTB-178M22.2 and RP11-347C18.5) based risk model was identified and patients in the high-risk group were revealed to have a lower survival rate than patients in the low-risk group. A nomogram that could predict the survival of patients was plotted. A total of 79 target miRNAs and 61 target proteins were identified. The gene set enrichment analysis results revealed that nutrient metabolism pathways were enriched in the high-risk group and immune regulation pathways were enriched in the low-risk group. In summary, a 4-lncRNA based risk model was identified that was associated with prognosis, which may serve as a prognosis prediction biomarker for HNSCC.

Long non-coding RNA LINC00858 promotes cells proliferation, migration and invasion by acting as a ceRNA of miR-22-3p in colorectal cancer

Though long non-coding RNA LINC00858 (LINC00858) has been shown to be involved in tumours of other tissues, its involvement in colorectal cancer (CRC) is still unknown. We aimed to investigated expression and mechanism LINC00858 in human CRC. In this study, we firstly found that LINC00858 expression was significantly up-regulated in both CRC tissues and cell lines by both online data and RT-PCR assay. Then, clinical assay revealed that high LINC00858 expression was significantly associated with advanced clinical progression and poor prognosis. Multivariate analysis demonstrated that high LINC00858 expression was an independent poor prognostic factor for CRC patients. Moreover, lost-of-function assay indicated that knockdown of LINC00858 suppressed CRC cells proliferation, migration and invasion, and promoted apoptosis. Mechanistically, bioinformatics analysis, dual-luciferase reporter assays, and western blot assays showed that LINC00858 functioned as competing endogenous RNA to repress miR-22-3p, which controlled its down-stream target YWHAZ. Then, we suggested that LINC00858 exerted its function through the miR-22-3p/YWHAZ axis. To our knowledge, this is the first report which showed the role of LINC00858 in the progression of CRC. Our findings indicated that LINC00858 played an important role in CRC, and may serve as a novel prognostic factor and therapeutic target.

A prognostic 11 long noncoding RNA expression signature for breast invasive carcinoma

Breast cancer, the most common cancer in women worldwide, is associated with high mortality. The long non-coding RNAs (lncRNAs) with a little capacity of coding proteins is playing an increasingly important role in the cancer paradigm. Accumulating evidences demonstrate that lncRNAs have crucial connections with breast cancer prognosis while the studies of lncRNAs in breast cancer are still in its primary stage. In this study, we collected 1052 clinical patient samples, a comparatively large sample size, including 13 159 lncRNA expression profiles of breast invasive carcinoma (BRCA) from The Cancer Genome Atlas database to identify prognosis-related lncRNAs. We randomly separated all of these clinical patient samples into training and testing sets. In the training set, we performed univariable Cox regression analysis for primary screening and played the model for Robust likelihood-based survival for 1000 times. Then 11 lncRNAs with a frequency more than 600 were selected for prediction of the prognosis of BRCA. Using the analysis of multivariate Cox regression, we established a signature risk-score formula for 11 lncRNA to identify the relationship between lncRNA signatures and overall survival. The 11 lncRNA signature was validated both in the testing and the complete set and could effectively classify the high-/low-risk group with different OS. We also verified our results in different stages. Moreover, we analyzed the connection between the 11 lncRNAs and the genes of ESR1, PGR, and Her2, of which protein products (ESR, PGR, and HER2) were used to classify the breast cancer subtypes widely. The results indicated correlations between 11 lncRNAs and the gene of PGR and ESR1. Thus, a prognostic model for 11 lncRNA expression was developed to classify the BRAC clinical patient samples, providing new avenues in understanding the potential therapeutic methods of breast cancer.

Expression Profile Analysis Identifies a Novel Five-Gene Signature to Improve Prognosis Prediction of Glioblastoma

Glioblastoma multiforme (GBM) is the most aggressive primary central nervous system malignant tumor. The median survival of GBM patients is 12–15 months, and the 5 years survival rate is less than 5%. More novel molecular biomarkers are still urgently required to elucidate the mechanisms or improve the prognosis of GBM. This study aimed to explore novel biomarkers for GBM prognosis prediction. The gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets of GBM were downloaded. A total of 2241 overlapping differentially expressed genes (DEGs) were identified from TCGA and GSE7696 datasets. By univariate COX regression survival analysis, 292 survival-related genes were found among these DEGs (p < 0.05). Functional enrichment analysis was performed based on these survival-related genes. A five-gene signature (PTPRN, RGS14, G6PC3, IGFBP2, and TIMP4) was further selected by multivariable Cox regression analysis and a prognostic model of this five-gene signature was constructed. Based on this risk score system, patients in the high-risk group had significantly poorer survival results than those in the low-risk group. Moreover, with the assistance of GEPIA http://gepia.cancer-pku.cn/, all five genes were found to be differentially expressed in GBM tissues compared with normal brain tissues. Furthermore, the co-expression network of the five genes was constructed based on weighted gene co-expression network analysis (WGCNA). Finally, this five-gene signature was further validated in other datasets. In conclusion, our study identified five novel biomarkers that have potential in the prognosis prediction of GBM.

Screening responsive or resistant biomarkers of immune checkpoint inhibitors based on online databases

Immune checkpoint inhibitors are a promising strategy in the treatment of cancer, especially advanced types. However, not all patients are responsive to immune checkpoint inhibitors. The response rate depends on the immune microenvironment, tumor mutational burden (TMB), expression level of immune checkpoint proteins, and molecular subtypes of cancers. Along with the Cancer Genome Project, various open access databases, including The Cancer Genome Atlas and Gene Expression Omnibus, provide large volumes of data, which allow researchers to explore responsive or resistant biomarkers of immune checkpoint inhibitors. In this review, we introduced some methodologies on database selection, biomarker screening, current progress of immune checkpoint blockade in solid tumor treatment, possible mechanisms of drug resistance, strategies of overcoming resistance, and indications for immune checkpoint inhibitor therapy.

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (-0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.

Identification of a six-gene signature with prognostic value for patients with endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is frequently diagnosed among women worldwide. However, there are different prognostic outcomes because of heterogeneity. Thus, the aim of the current study was to identify a gene signature that can predict the prognosis of patients with UCEC. UCEC gene expression profiles were first downloaded from the The Cancer Genome Atlas (TCGA) database. After data processing and forward screening, 11 390 key genes were selected. The UCEC samples were randomly divided into training and testing sets. In total, 996 genes with prognostic value were then examined by univariate Cox survival analysis with a P-value <0.01 in the training set. Next, using robust likelihood-based survival modeling, we developed a six-gene signature (CTSW, PCSK4, LRRC8D, TNFRSF18, IHH, and CDKN2A) with a prognostic function in UCEC. A prognostic risk score system was developed by multivariate Cox proportional hazard regression based on this six-gene signature. According to the Kaplan-Meier curve, patients in the high-risk group had significantly poorer overall survival (OS) outcomes than those in the low-risk group (log-rank test P-value <0.0001). This signature was further validated in the testing dataset and the entire TCGA dataset. In conclusion, we conducted an integrated study to develop a six-gene signature for the prognostic prediction of patients with UCEC. Our findings may provide novel biomarkers for prognosis and have significant implications in the understanding of therapeutic targets for UCEC.