15-lncRNA-Based Classifier-Clinicopathologic Nomogram Improves the Prediction of Recurrence in Patients with Hepatocellular Carcinoma

Biomarker Potential of LINC00313 in Head and Neck Squamous Cell Carcinoma: Correlation with Epithelial-Mesenchymal Transition and Immune Cell Infiltration

Although LINC00313 is dysregulated in several tumors, its role in head and neck squamous cell carcinoma (HNSC) is not fully understood. The aim of this study was to analyze the role of LINC00313 in HNSC. The clinical information and LINC00313 expression data of HNSC were mined from the TCGA/GEO/cbioportal database. The correlation between LINC00313 expression and immune cell infiltration in HNSC tumors was analyzed by bioinformatics and gene enrichment analysis was performed. LINC00313 was silenced in HNSC cell lines, and changes at the genetic and molecular levels were verified through qRT-PCR and Western blotting. The researchers also validated its functional phenotype through a series of cell function experiments. The results showed that overexpression and copy number variation of LINC00313 in HNSC were associated with poorer prognosis. In addition, LINC00313 expression was significantly negatively correlated with immune cell infiltration. Silencing of LINC00313 in HNSC cells significantly reduced the rate of cell migration. LINC00313 may affect the progression of HNSC by regulating epithelial-mesenchymal transition. In conclusion, LINC00313 is a potential biomarker of HNSC prognosis and a potential target for immunotherapy.

Combining a machine-learning derived 4-lncRNA signature with AFP and TNM stages in predicting early recurrence of hepatocellular carcinoma

BACKGROUND

Near 70% of hepatocellular carcinoma (HCC) recurrence is early recurrence within 2-year post surgery. Long non-coding RNAs (lncRNAs) are intensively involved in HCC progression and serve as biomarkers for HCC prognosis. The aim of this study is to construct a lncRNA-based signature for predicting HCC early recurrence.

METHODS

Data of RNA expression and associated clinical information were accessed from The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) database. Recurrence associated differentially expressed lncRNAs (DELncs) were determined by three DEG methods and two survival analyses methods. DELncs involved in the signature were selected by three machine learning methods and multivariate Cox analysis. Additionally, the signature was validated in a cohort of HCC patients from an external source. In order to gain insight into the biological functions of this signature, gene sets enrichment analyses, immune infiltration analyses, as well as immune and drug therapy prediction analyses were conducted.

RESULTS

A 4-lncRNA signature consisting of AC108463.1, AF131217.1, CMB9-22P13.1, TMCC1-AS1 was constructed. Patients in the high-risk group showed significantly higher early recurrence rate compared to those in the low-risk group. Combination of the signature, AFP and TNM further improved the early HCC recurrence predictive performance. Several molecular pathways and gene sets associated with HCC pathogenesis are enriched in the high-risk group. Antitumor immune cells, such as activated B cell, type 1 T helper cell, natural killer cell and effective memory CD8 T cell are enriched in patients with low-risk HCCs. HCC patients in the low- and high-risk group had differential sensitivities to various antitumor drugs. Finally, predictive performance of this signature was validated in an external cohort of patients with HCC.

CONCLUSION

Combined with TNM and AFP, the 4-lncRNA signature presents excellent predictability of HCC early recurrence.

Development and Validation of a Novel Mitochondrion and Ferroptosis-Related Long Non-Coding RNA Prognostic Signature in Hepatocellular Carcinoma

Mitochondrion and ferroptosis are related to tumorigenesis and tumor progression of hepatocellular carcinoma (HCC). Therefore, this study focused on exploring the participation of lncRNAs in mitochondrial dysfunction and ferroptosis using public datasets from The Cancer Genome Atlas (TCGA) database. We identified the mitochondrion- and ferroptosis-related lncRNAs by Pearson’s analysis and lasso-Cox regression. Moreover, real-time quantitative reverse transcription PCR (RT-qPCR) was utilized to further confirm the abnormal expression of these lncRNAs. Based on eight lncRNAs, the MF-related lncRNA prognostic signature (LPS) with outstanding stratification ability and prognostic prediction capability was constructed. In addition, functional enrichment analysis and immune cell infiltration analysis were performed to explore the possible functions of lncRNAs and their impact on the tumor microenvironment. The pathways related to G2M checkpoint and MYC were activated, and the infiltration ratio of regulatory T cells and M0 and M2 macrophages was higher in the high-risk group. In conclusion, these lncRNAs may affect mitochondria functions, ferroptosis, and immune cell infiltration in HCC through specific pathways, which may provide valuable insight into the progression and therapies of HCC.

LINC01419 Promotes the Proliferation of Hepatoma Cells by Recruiting XRCC5 and Regulating Its Phosphorylation to Repair DNA Damage

Background. Hepatocellular carcinoma (HCC) is one of the most common and fatal malignancies in human beings. Studies have shown that long non-coding RNAs (lncRNAs) play key parts in the occurrence and development of HCC. Although many lncRNAs have been studied in the HCC specifically for DNA damage repair, the role of LINC01419 in cellular DNA damage repair has not yet been studied. Objective. This study is aimed at exploring the biological role of LINC01419 and its potential mechanism in HCC. Methods. qRT-PCR was used to detect the expression level of LINC01419 in HCC tissues and cells, the proteins which were involved were detected by Western blot. Effect of LINC01419 knockdown on cell cycle, apoptosis, DNA damage, cell proliferation, wound healing, colony formation, and migration of HCC cells was studied in vitro. Results. The analysis showed that LINC01419 was overexpressed in HCC tissues and cells. Silencing of LINC01419 expression significantly inhibited the proliferation and migration ability of the HCC cells and resulted in cell cycle arrest at G0/G1 phase. Furthermore, the knockdown of LINC01419 increased the DNA damage, and to some extent, promoted sensitivity of HCC cells to doxorubicin. In addition, we performed RIP analysis which showed XRCC5 as a potential protein related to DNA damage repair in hepatoma cells. Conclusion. In conclusion, the LINC01419 acts as an oncogene in HCC and regulates DNA damage repair through XRCC5 in HCC cells.

Construction and validation of immune-related LncRNAs classifier to predict prognosis and immunotherapy response in laryngeal squamous cell carcinoma

Background

Rapid advances in transcriptomic profiles have resulted in recognizing IRLs (immune-related long noncoding RNAs), as modulators of the expression of genes related to immune cells that mediate immune inhibition as well as immune stimulatory, indicating LncRNAs play fundamental roles in immune modulation. Hence, we establish an IRL classifier to precisely predict prognosis and immunotherapeutic efficiency in laryngeal squamous cell carcinoma (LSCC).

Methods

LSCC RNA-seq (RNA sequencing) datasets, somatic mutation data, and corresponding clinicopathologic information were acquired from TCGA (the Cancer Genome Atlas) and Gene Expression Omnibus (GEO) databases. Spearman correlation analysis identified LncRNAs associated with immune-related genes (IRG). Based on Lasso penalized regression and random forest (RF), we constructed an IRL classifier associated with prognosis. GEO database was utilized to validate the IRL classifier. The predictive precision and clinical application of the IRL classifier were assessed and compared to clinicopathologic features. The immune cell infiltration of LSCC was calculated via CIBERSORTx tools and ssGSEA (single-sample gene set enrichment analysis). Then, we systematically correlated the IRL classifier with immunological characteristics from multiple perspectives, such as immune-related cells infiltrating, tumor microenvironment (TME) scoring, microsatellite instability (MSI), tumor mutation burden (TMB), and chemokines. Finally, the TIDE (tumor immune dysfunction and exclusion) algorithm was used to predict response to immunotherapy.

Results

Based on machine learning approach, three prognosis-related IRLs (BARX1-DT, KLHL7-DT, and LINC02154) were selected to build an IRL classifier. The IRL classifier could availably classify patients into the low-risk and high-risk groups based on the different endpoints, including recurrence-free survival (RFS) and overall survival (OS). In terms of predictive ability and clinical utility, the IRL classifier was superior to other clinical characteristics. Encouragingly, similar results were observed in the GEO databases. Immune infiltration analysis displayed immune cells that are significantly richer in low-risk group, CD8 T cells and activated NK cells via CIBERSORTx algorithm as well as activated CD8 T cell via ssGSEA. Additionally, compared with the high-risk group, immune score, CD8 T effector was higher in the low-risk group, yet stromal score, score of p53 signaling pathway and TGFher in the Tx algorithm, was lower in the low-risk group. Corresponding results were confirmed in GEO dataset. Finally, TIDE analysis uncovered that the IRL classifier may be effectually predict the clinical response of immunotherapy in LSCC.

Conclusion

Based on BARX1-DT, KLHL7-DT, and LINC02154, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, and immunotherapy response in LSCC patients and might facilitate personalized counseling for immunotherapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-022-02608-z.

Construction of immune-related LncRNAs classifier to predict prognosis and immunotherapy response in thymic epithelial tumors

The primary objective of this study was to construct an immune-related long non-coding RNAs (IRLs) classifier to precisely predict the prognosis and immunotherapy response of patients with thymic epithelial tumors (TET).Based on univariable Cox regression analysis and Lasso regression, six prognosis-related IRLs (AC004466.3,AC138207.2, AC148477.2, AL450270.1, HOXB-AS1 and SNHG8) were selected to build an IRL classifier. Importantly, results of qRT-PCR validated that higher expression levels of AC138207.2, AC148477.2, AL450270.1 and SNHG8 as well as lower expression levels of AC004466.3, and HOXB-AS1 in TETs samples compared to normal controls. The IRL classifier could effectively classify patients into the low-risk and high-risk groups based on the different survival parameters. In terms of predictive ability and clinical utility, the IRL classifier was superior to Masaoka staging system. Additionally, IRL classifier is significantly associated with immune cells infiltration (dendritic cells, activated CD4 memory T cells and tumor-infiltrating lymphocyte (TIL), T cell subsets in particular), immune microenvironment (immune score and immune checkpoint inhibitors), and immunogenicity (TMB) in TETs, which hints that IRL classifier is tightly correlated with immune characteristics and might guide more effective immunotherapy strategies for TETs patients. Encouragingly, according to TIDE algorithm, there were more immunotherapy responders in the low-risk IRL subgroup and the IRL score was robustly negatively linked to the immunotherapeutic response. To sum up, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, immunotherapy response in TETs patients, and may facilitate personalized counseling for immunotherapy.

Identification of key regulators associated with colon cancer prognosis and pathogenesis

Colon cancer (CC) is the fourth deadliest cancer in the world. New insights into prognostication might be helpful to define the optimal adjuvant treatments for patients in routine clinical practice. Here, a microarray dataset with 30 primary tumors and 30 normal samples was analyzed using GEO2R to find differentially expressed genes (DEGs). Then, DAVID, KEGG, ChEA and X2K were used to analyze DEGs-related Gene Ontology, pathways, transcription factors (TFs) and kinases, respectively. Protein-protein interaction (PPI) networks were constructed using the STRING database and Cytoscape. The modules and hub genes of DEGs was determined through MCODE and CytoHubba plugins, and the expression of hub genes was verified using GEPIA. To find microRNAs and metabolites associated with DEGs, miRTarBase and HMDB were used, respectively. It was found that 233 and 373 genes were upregulated and downregulated in CC, respectively. GO analysis showed that the upregulated DEGs were mainly involved in mitotic nuclear division and cell division. Top 10 hub genes were identified, including AURKB, CDK1, DLGAP5, AURKA, CCNB2, CCNB1, BUB1B, CCNA2, KIF20A and BUB1. Whereas, FOMX1, E2F7, E2F1, E2F4 and AR were identified as top 5 TFs in CC. Moreover, CDK1, CDC2, MAPK14, ATM and CK2ALPHA was identified as top 5 kinases in CC. miRNAs analysis showed that Hsa-miR-215-5p hsa-miR-193b-3p, hsa-miR-192-5p and hsa-miR-16-5p could target the largest number of CC genes. Taken together, CC-related genes, especially the hub genes, TFs, and metabolites might be used as novel biomarkers for CC, as well as for diagnosis and guiding therapeutic strategies for CC.

Construction of an HCC recurrence model based on the investigation of immune-related lncRNAs and related mechanisms

Long noncoding RNAs (lncRNAs) are emerging as critical regulators of gene expression and play fundamental roles in immune regulation. Growing evidence suggests that immune-related genes and lncRNAs can serve as markers to predict the prognosis of patients with cancers, including hepatocellular carcinoma (HCC). This study aimed to contract an immune-related lncRNA (IR-lncRNA) signature for prospective assessment to predict early recurrence of HCC. A total of 319 HCC samples under radical resection were randomly divided into a training cohort (161 samples) and a testing cohort (158 samples). In the training dataset, univariate, lasso, and multivariate Cox regression analyses identified a 9-IR-lncRNA signature closely related to disease-free survival. Kaplan-Meier analysis, principal component analysis, gene set enrichment analysis, and nomogram were used to evaluate the risk model. The results were further confirmed in the testing cohort. Furthermore, we constructed a competitive endogenous RNA regulatory network. The results of the present study indicated that this 9-IR-lncRNA signature has important clinical implications for improving predictive outcomes and guiding individualized treatment in HCC patients. These IR-lncRNAs and regulated genes may be potential biomarkers associated with the prognosis of HCC.

Long non-coding RNA NORAD promotes pancreatic cancer stem cell proliferation and self-renewal by blocking microRNA-202-5p-mediated ANP32E inhibition

Background

Cancer stem cells (CSCs) are key regulators in the processes of tumor initiation, progression, and recurrence. The mechanism that maintains their stemness remains enigmatic, although the role of several long noncoding RNAs (lncRNAs) has been highlighted in the pancreatic cancer stem cells (PCSCs). In this study, we first established that PCSCs overexpressing lncRNA NORAD, and then investigated the effects of NORAD on the maintenance of PCSC stemness.

Methods

Expression of lncRNA NORAD, miR-202-5p and ANP32E in PC tissues and cell lines was quantified after RNA isolation. Dual-luciferase reporter assay, RNA pull-down and RIP assays were performed to verify the interactions among NORAD, miR-202-5p and ANP32E. We then carried out gain- and loss-of function of miR-202-5p, ANP32E and NORAD in PANC-1 cell line, followed by measurement of the aldehyde dehydrogenase activity, cell viability, apoptosis, cell cycle distribution, colony formation, self-renewal ability and tumorigenicity of PC cells.

Results

LncRNA NORAD and ANP32E were upregulated in PC tissues and cells, whereas the miR-202-5p level was down-regulated. LncRNA NORAD competitively bound to miR-202-5p, and promoted the expression of the miR-202-5p target gene ANP32E thereby promoting PC cell viability, proliferation, and self-renewal ability in vitro, as well as facilitating tumorigenesis of PCSCs in vivo.

Conclusion

Overall, lncRNA NORAD upregulates ANP32E expression by competitively binding to miR-202-5, which accelerates the proliferation and self-renewal of PCSCs.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03052-5.