LncRNAs as potential diagnostic and prognostic biomarkers in gastric cancer: A novel approach to personalized medicine

Gastric cancer is the third leading cause of cancer death with 5-year survival rate of about 30–35%. Since early detection is associated with decreased mortality, identification of novel biomarkers for early diagnosis and proper management of patients with the best response to therapy is urgently needed. Long noncoding RNAs (lncRNAs) due to their high specificity, easy accessibility in a noninvasive manner, as well as their aberrant expression under different pathological and physiological conditions, have received a great attention as potential diagnostic, prognostic, or predictive biomarkers. They may also serve as targets for treating gastric cancer. In this review, we highlighted the role of lncRNAs as tumor suppressors or oncogenes that make them potential biomarkers for the diagnosis and prognosis of gastric cancer. Relatively, lncRNAs such as H19, HOTAIR, UCA1, PVT1, tissue differentiation-inducing nonprotein coding, and LINC00152 could be potential diagnostic and prognostic markers in patients with gastric cancer. Also, the impact of lncRNAs such as ecCEBPA, MLK7-AS1, TUG1, HOXA11-AS, GAPLINC, LEIGC, multidrug resistance-related and upregulated lncRNA, PVT1 on gastric cancer epigenetic and drug resistance as well as their potential as therapeutic targets for personalized medicine was discussed.     

LncRNA PTCSC3 inhibits triple-negative breast cancer cell proliferation by downregulating lncRNA H19

Long noncoding RNA (lncRNA) PTCSC3 (hereafter PTCSC3 is used to represent lncRNA PTCSC3) inhibits glioma and thyroid cancer, indicating its potential tumor suppression function in other types of cancers. We explored the potential involvement of PTCSC3 in triple-negative breast cancer (TNBC). In the current study, we found that PTCSC3 was downregulated in tumor tissues of patients with TNBC. PTCSC3 expression was positively correlated with plasma levels of PTCSC3. LncRNA H19 was upregulated and was inversely correlated with PTCSC3 in tumor tissues. PTCSC3 overexpression led to downregulated H19 in TNBC cells, while H19 overexpression did not affect PTCSC3 expression. PTCSC3 inhibited and H19 promoted proliferation of TNBC cells. H19 overexpression attenuated the effects of PTCSC3 overexpression. Cancer cell migration and invasion were not significantly affected by PTCSC3 overexpression. Therefore, lncRNA PTCSC3 inhibits TNBC cell proliferation by downregulating lncRNA H19.     

Four long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

The study aimed to identify the long noncoding RNAs (lncRNAs) biomarkers for occurrence and prognosis of patients with hepatocellular carcinoma (HCC), and simultaneously to investigate the potential role of lncRNAs in the oncogenesis of HCC. The lncRNAs expression data and the corresponding clinical information of HCC samples were extracted from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes and lncRNAs were identified and the correlation networks were constructed. In this study, we identified 212 differentially expressed lncRNAs and 7,577 differentially expressed genes between liver HCC tumor tissues and normal tissue samples. And then, combining with clinical information, a total of 11 lncRNAs and 162 genes as HCC biomarkers were identified by comprehensive bioinformatics analysis. Further, through coexpress network analysis, we confirmed four lncRNAs (lncRNA_ANKRD10.IT1, lncRNA_CTD.2583A14.8, lncRNA_RP11.404P21.3, and lncRNA_RP11.488L18.10), which can serve as prognostic biomarkers for HCC. The four lncRNAs identified in this study may serve as a potential therapy target for HCC.     

A novel autophagy-related lncRNA prognostic risk model for breast cancer

Long non-coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy-related lncRNAs in breast cancer by constructing a co-expression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy-related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and time-dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low-risk and high-risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be autophagy-related therapeutic targets in clinical practice.     

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.     

Downregulation of long noncoding RNA LINC00683 associated with unfavorable prognosis in prostate cancer based on TCGA

Prostate cancer (PCa) is the third most common reason of cancer-related deaths in men. Accumulating evidence has shown that dysregulation of long noncoding RNAs (lncRNAs) is closely related to cancer initiation and development. Although large numbers of lncRNAs have been discovered, knowledge regarding their function and physiological/pathological significance remains limited. In this study, we aimed to reveal functional lncRNAs and identify prognosis-related RNAs in PCa by analyzing data from The Cancer Genome Atlas (TCGA). To achieve this, an lncRNA-mRNA coexpression network was constructed by weighted correlation network analysis. Additionally, a subnetwork was extracted from this weighted correlation network, and seven lncRNAs were identified as core nodes. Further Kaplan-Meier survival analysis showed that three lncRNAs (LINC00683, LINC00857, and FENDRR) were significantly downregulated in PCa samples, and there was a strong positive correlation with patient survival. Importantly, LINC00683 has not been fully reported as related with PCa. Additionally, gene set enrichment analysis indicated that LINC00683 might be involved in cancer-related pathways such as the Wnt pathway. Based on the findings of this study, lncRNA LINC00683 is likely to provide a new diagnostic biomarker and therapeutic target for future PCa treatments.     

Long noncoding RNA HOXD-AS1 induces epithelial-mesenchymal transition in breast cancer by acting as a competing endogenous RNA of miR-421

Breast cancer (BCa) is the most common malignant tumor in females. Long noncoding RNAs (lncRNAs) are deregulated in many types of human cancers, including BCa. The purpose of the present study was to examine the expression profile and biological role of HOXD cluster antisense RNA 1 (HOXD-AS1) in BCa. Our results revealed that HOXD-AS1 was upregulated in BCa tissues and cell lines, and high HOXD-AS1 expression was correlated with aggressive clinicopathological characteristics of BCa patients. Further gain-of-function and loss-of-function analysis showed that HOXD-AS1 overexpression promoted, whereas HOXD-AS1 knockdown inhibited BCa cell proliferation, cell cycle progression, migration, and invasion, indicating that HOXD-AS1 may function as a novel oncogene in BCa. Mechanistically, HOXD-AS1 could activate epithelial-mesenchymal transition (EMT) in BCa cells. We further proved that HOXD-AS1 might serve as a competing endogenous RNA of miR-421 in BCa cells, and miR-421 was downregulated and negatively correlated with HOXD-AS1 expression in BCa tissues. Besides, we confirmed that SOX4, a master regulator of EMT, was a direct target gene of miR-421. Further, rescue experiments suggested that miR-421 overexpression partly abrogated the oncogenic role of HOXD-AS1 in BCa cells. Therefore, we shed light on that HOXD-AS1/miR-421/SOX4 axis may be considered as a novel therapeutic target for the treatment of BCa patients.     

Integrated analysis of a competing endogenous RNA network reveals key long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

Growing evidence has revealed that long noncoding RNAs (lncRNAs) have an important impact on tumorigenesis and tumor progression via a mechanism involving competing endogenous RNAs (ceRNAs). However, their use in predicting the survival of a patient with hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to develop a novel lncRNA expression–based risk score system to accurately predict the survival of patients with HCC. In our study, using expression profiles downloaded from The Cancer Genome Atlas database, the differentially expressed messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) were explored in patients with HCC and normal liver tissues, and then a ceRNA network constructed. A risk score system was established between lncRNA expression of the ceRNA network and overall survival (OS) or recurrence-free survival (RFS); it was further analyzed for associations with the clinical features of patients with HCC. In HCC, 473 differentially expressed lncRNAs, 63 differentially expressed miRNAs, and 1417 differentially expressed mRNAs were detected. The ceRNA network comprised 41 lncRNA nodes, 12 miRNA nodes, 24 mRNA nodes, and 172 edges. The lncRNA expression–based risk score system for OS was constructed based on six lncRNAs (MYLK-AS1, AL359878.1, PART1, TSPEAR-AS1, C10orf91, and LINC00501), while the risk score system for RFS was based on four lncRNAs (WARS2-IT1, AL359878.1, AL357060.1, and PART1). Univariate and multivariate Cox analyses showed the risk score systems for OS or RFS were significant independent factors adjusted for clinical factors. Receiver operating characteristic curve analysis showed the area under the curve for the risk score system was 0.704 for OS, and 0.71 for RFS. Our result revealed a lncRNA expression–based risk score system for OS or RFS can effectively predict the survival of patients with HCC and aid in good clinical decision-making.     

CELF2 is a candidate prognostic and immunotherapy biomarker in triple-negative breast cancer and lung squamous cell carcinoma: A pan-cancer analysis

CUGBP Elav-like family member 2(CELF2) plays crucial roles in the development and activation of T cell. However, the impacts of CELF2 on tumour-infiltrating immune cells (TIICs) and clinical outcomes of tumours remain unclear. In this study, we found that elevated CELF2 expression was markedly correlated with prolonged survival in multiple tumours, particularly in breast and lung cancers. Notably, CELF2 only impacted the prognosis of triple-negative breast cancer (TNBC) with lymph node metastasis. Further investigation showed CELF2 expression was positively correlated with the infiltration abundance of dendritic cells (DCs), CD8+ T cells and neutrophils in breast invasive carcinoma (BRCA) and DCs in lung squamous cell carcinoma (LUSC). CELF2 also had strong correlations with markers of diverse TIICs such as T cells, tumour-associated macrophages and DCs in BRCA and LUSC. Importantly, CELF2 was significantly associated with plenty of immune checkpoint molecules (ICMs) and outperformed five prevalent biomarkers including PD-1, PD-L1, CTLA-4, CD8 and tumour mutation burden in predicting immunotherapeutic responses. Immunohistochemistry also revealed lower protein levels of CELF2 in TNBC and LUSC compared to normal tissues, and patients with high expression showed significantly prolonged prognosis. In conclusion, we demonstrated that increased CELF2 expression was closely related to better prognosis and superior TIIC infiltration and ICM expression, particularly in BRCA and LUSC. CELF2 also performed well in evaluating the immunotherapeutic efficacy, suggesting CELF2 might be a promising biomarker.