An immune-related lncRNA signature for patients with anaplastic gliomas

The role of lnc‑MAPKAPK5‑AS1 in immune cell infiltration in hepatocellular carcinoma: Bioinformatics analysis and validation

The oncogenic and tumor suppressor roles of lnc-MAPKAPK5-AS1 in multiple cancers suggest its complexity in modulating cancer progression. The expression and promoter methylation level of lnc-MAPKAPK5-AS1 in hepatocellular carcinoma (HCC) was investigated through data mining from The Cancer Genome Atlas and Gene Expression Omnibus and its significance in prognosis and immunity was explored. lnc-MAPKAPK5-AS1 was co-expressed with its protein-coding gene MAPKAPK5 in HCC and exhibited upregulation in HCC tissues as a result of hypomethylation of its promoter region. High expression of lnc-MAPKAPK5-AS1 was associated with poor prognosis. Enrichment analysis revealed that lnc-MAPKAPK5-AS1 is involved in immune and metabolic-related pathways. Changes in the expression of lnc-MAPKAPK5-AS1 affected plasma cells, T cells CD4+ memory resting, NK cells, macrophages M0/M1, and mast cells resting in the tumor microenvironment. lnc-MAPKAPK5-AS1 was found to correlate with multiple immune checkpoints. Analysis of the Sangerbox database revealed positive relationships between expression of lnc-MAPKAPK5-AS1, tumor mutational burden and microsatellite instability, which suggested that immunotherapy may be effective in tumors with high expression of lnc-MAPKAPK5-AS1. The expression of lnc-MAPKAPK5-AS1 was verified to indicate sensitivity to 16 common targeted drugs. Immunohistochemistry confirmed the expression of MAPKAPK5 protein in HCC and its prognostic significance. Weighted gene co-expression network analysis was applied to identify hub genes related to both immunoreactive score and gene expression. These results revealed that lnc-MAPKAPK5-AS1 may be involved in the occurrence and development of HCC as an oncogene and may represent a potential therapeutic target through modulating the substance metabolism and immune response.

Long Non-Coding RNA AGAP2-AS1: A Comprehensive Overview on Its Biological Functions and Clinical Significances in Human Cancers

Long non-coding RNAs (lncRNAs) are well known for their oncogenic or anti-oncogenic roles in cancer development. AGAP2-AS1, a new lncRNA, has been extensively demonstrated as an oncogenic lncRNA in various cancers. Abundant experimental results have proved the aberrantly high level of AGAP2-AS1 in a great number of malignancies, such as glioma, colorectal, lung, ovarian, prostate, breast, cholangiocarcinoma, bladder, colon and pancreatic cancers. Importantly, the biological functions of AGAP2-AS1 have been extensively demonstrated. It could promote the proliferation, migration and invasion of cancer cells. Simultaneously, the clinical significances of AGAP2-AS1 were also illustrated. AGAP2-AS1 was exceptionally overexpressed in various cancer tissues. Clinical studies disclosed that the abnormal overexpression of AGAP2-AS1 was tightly connected with overall survival (OS), lymph nodes metastasis (LNM), clinical stage, tumor infiltration, high histological grade (HG), serous subtype and PFI times. However, to date, the biological actions and clinical significances of AGAP2-AS1 have not been systematically reviewed in human cancers. In the present review, the authors overviewed the biological actions, potential mechanisms and clinical features of AGAP2-AS1 according to the previous studies. In summary, AGAP2-AS1, as a vital oncogenic gene, is a promising biomarker and potential target for carcinoma prognosis and therapy.

PGM5-AS1 Promotes Progression of Diffuse Large B-Cell Lymphoma and Immune Escape by Regulating miR-503-5p

Purpose

Diffuse large B-cell lymphoma (DLBCL) is a prevalent malignant condition with a dismal prognosis. LncRNA PGM5 antisense RNA 1 (PGM5-AS1) appears to be intricately involved in the progression of DLBCL, yet the modulatory mechanism remains unclear. The purpose of this study was to explore the expression of lncRNA PGM5-AS1 in DLBCL and its effect on the disease progression of DLBCL, as well as to explore its mechanisms.

Patients and Methods

A total of 35 patients were included in the study. The expression levels of PGM5-AS1 and miR-503-5p in DLBCL tumor tissues and cell lines were detected by RT-qPCR. Cell proliferation was assessed using CCK8. Apoptosis rate was determined by flow cytometry. Cell invasion was examined by transwell assays. The specific interaction between PGM5-AS1 and miR-503-5p was verified through dual luciferase reporter gene assays. The immune related factors were detected by ELASA kits. The CD8+ T cells cytotoxicity was evaluated by LDH cytotoxicity kit.

Results

In DLBCL tumor tissues and cells, upregulated PGM5-AS1 expression, downregulated miR-503-5p expression, and elevated PD-L1 expression were observed. PGM5-AS1 functioned as a regulator in controlling DLBCL cell proliferation, apoptosis, and invasion by downregulating miR-503-5p expression. When CD8+ T cells were co-cultured with cells transfected with si-PGM5-AS1, the secretion of immunoregulatory factors increased, and the cytotoxicity of CD8+ T cells increased. These effects were mitigated by miR-503-5p inhibitors.

Conclusion

PGM5-AS1 accelerated DLBCL development and facilitated tumor immune escape through the miR-503-5p. Our discoveries offered an insight into lncRNA PGM5-AS1 serving as a prospective therapeutic target for DLBCL.

Integrating network pharmacology and bioinformatics to explore and experimentally verify the regulatory effect of Buyang Huanwu decoction on glycolysis and angiogenesis after cerebral infarction

ETHNOPHARMACOLOGICAL RELEVANCE

Promoting the recovery of cerebral blood circulation after cerebral infarction (CI) is an important intervention. Buyang Huanwu decoction (BHD) is a classic prescription for treating CI that promotes angiogenesis. Cytoplasmic glycolysis ischaemic-region cells after CI may be highly activated to maintain metabolic activity under hypoxia. From the perspective of long-term maintenance of glycolytic metabolism in the ischaemic area after CI, it may be beneficial to promote angiogenesis and maintain glial cell activation and neuronal survival. In this context, the regulatory relationship of lncRNAs and miRNAs with mRNAs is worthy of attention. Mining the competitive binding relationships among RNAs will aid in the screening of key gene targets post-CI. In this study, network pharmacology and bioinformatics were used to construct a ceRNA network, screen key targets, and explore the effect of glycolysis on angiogenesis during BHD-mediated CI regulation.

AIM OF THE STUDY

This study aimed to explore the effect of BHD on angiogenesis after glycolysis regulation in CI.

MATERIALS AND METHODS

According to the 21 active BHD ingredients we identified by our research team, we conducted network pharmacology. BHD targets that can regulate glycolysis and angiogenesis after CI were screened from the GeneCards, CTD and OMIM databases. We retrieved CI-related datasets from the GEO database and screened for differentially expressed lncRNAs and miRNAs. LncRNA‒miRNA-mRNA/TF targeting relationships were screened and organized with the miRcode, miRDB, TargetScan, miRWalk, and TransmiR v2.0 databases. Cytoscape was used to construct an lncRNA‒miRNA-mRNA/TF ceRNA network. Through BioGPS, key mRNAs/TFs in the network were screened for enrichment analysis. Animal experiments were then conducted to validate some key mRNAs/TFs and enriched signalling pathways.

RESULTS

PFKFB3 and other genes may help regulate glycolysis and angiogenesis through AMPK and other signalling pathways. The anti-CI effect of BHD may involve maintaining activation of genes such as AMPK and PFKFB3 in the ischaemic cortex, maintaining moderate glycolysis levels in brain tissue, and promoting angiogenesis.

CONCLUSION

BHD can regulate glycolysis and promote angiogenesis after CI through multiple pathways and targets, in which AMPK signalling pathway activation may be important.

Immune-related lncRNAs signature and radiomics signature predict the prognosis and immune microenvironment of glioblastoma multiforme

BACKGROUND

Glioblastoma multiforme (GBM) is the most common primary malignant brain tumor in adults. This study aimed to construct immune-related long non-coding RNAs (lncRNAs) signature and radiomics signature to probe the prognosis and immune infiltration of GBM patients.

METHODS

We downloaded GBM RNA-seq data and clinical information from The Cancer Genome Atlas (TCGA) project database, and MRI data were obtained from The Cancer Imaging Archive (TCIA). Then, we conducted a cox regression analysis to establish the immune-related lncRNAs signature and radiomics signature. Afterward, we employed a gene set enrichment analysis (GSEA) to explore the biological processes and pathways. Besides, we used CIBERSORT to estimate the abundance of tumor-infiltrating immune cells (TIICs). Furthermore, we investigated the relationship between the immune-related lncRNAs signature, radiomics signature and immune checkpoint genes. Finally, we constructed a multifactors prognostic model and compared it with the clinical prognostic model.

RESULTS

We identified four immune-related lncRNAs and two radiomics features, which show the ability to stratify patients into high-risk and low-risk groups with significantly different survival rates. The risk score curves and Kaplan-Meier curves confirmed that the immune-related lncRNAs signature and radiomics signature were a novel independent prognostic factor in GBM patients. The GSEA suggested that the immune-related lncRNAs signature were involved in L1 cell adhesion molecular (L1CAM) interactions and the radiomics signature were involved signaling by Robo receptors. Besides, the two signatures was associated with the infiltration of immune cells. Furthermore, they were linked with the expression of critical immune genes and could predict immunotherapy's clinical response. Finally, the area under the curve (AUC) (0.890,0.887) and C-index (0.737,0.817) of the multifactors prognostic model were greater than those of the clinical prognostic model in both the training and validation sets, indicated significantly improved discrimination.

CONCLUSIONS

We identified the immune-related lncRNAs signature and tradiomics signature that can predict the outcomes, immune cell infiltration, and immunotherapy response in patients with GBM.

Mining glycosylation-related prognostic lncRNAs and constructing a prognostic model for overall survival prediction in glioma: A study based on bioinformatics analysis

Dysregulation of protein glycosylation plays a crucial role in the development of glioma. Long noncoding RNA (lncRNAs), functional RNA molecules without protein-coding ability, regulate gene expression and participate in malignant glioma progression. However, it remains unclear how lncRNAs are involved in glycosylation glioma malignancy. Identification of prognostic glycosylation-related lncRNAs in gliomas is necessary. We collected RNA-seq data and clinicopathological information of glioma patients from the cancer genome atlas and Chinese glioma genome atlas. We used the "limma" package to explore glycosylation-related gene and screened related lncRNAs from abnormally glycosylated genes. Using univariate Cox analyses Regression and least absolute shrinkage and selection operator analyses, we constructed a risk signature with 7 glycosylation-related lncRNAs. Based on the median risk score (RS), patients with gliomas were divided into low- and high-risk subgroups with different overall survival rates. Univariate and multivariate Cox analyses regression analyses were performed to assess the independent prognostic ability of the RS. Twenty glycosylation-related lncRNAs were identified by univariate Cox regression analyses. Two glioma subgroups were identified using consistent protein clustering, with the prognosis of the former being better than that of the latter. Least absolute shrinkage and selection operator analysis identified 7 survival RSs for glycosylation-related lncRNAs, which were identified as independent prognostic markers and predictors of glioma clinicopathological features. Glycosylation-related lncRNAs play an important role in the malignant development of gliomas and may help guide treatment options.

The role of long non-coding RNA HCG18 in cancer

The incidence of cancer is increasing worldwide and is becoming the most common cause of death. Identifying new biomarkers for cancer diagnosis and prognosis is important for developing cancer treatment strategies and reducing mortality. Long non-coding RNAs (lncRNAs) are non-coding, single-stranded RNAs that play an important role as oncogenes or tumor suppressors in the occurrence and development of human tumors. Abnormal expression of human leukocyte antigen complex group 18 (HCG18) is observed in many types of cancer, and its imbalance is closely related to cancer progression. HCG18 regulates cell proliferation, invasion, metastasis, and anti-apoptosis through a variety of mechanisms. Therefore, HCG18 is a potential tumor biomarker and therapeutic target. However, the therapeutic significance of HCG18 has not been well studied, and future research may develop new intervention strategies to combat cancer. In this study, we reviewed the biological function, mechanism, and potential clinical significance of HCG18 in various cancers to provide a reference for future research.

Tumor-Derived Biomarkers in Liquid Biopsy of Glioblastoma

There is a pressing clinical need for minimally invasive liquid biopsies to supplement imaging in the treatment of glioblastoma. Diagnostic imaging is often difficult to interpret and the medical community is divided on distinguishing among complete response, partial response, stable disease, and progressive disease. A minimally invasive liquid biopsy would supplement imaging and clinical findings and has the capacity to be helpful in several ways: 1) diagnosis, 2) selection of patients for specific treatments, 3) tracking of treatment response, and 4) prognostic value. The liquid biome is the combination of biological fluids including blood, urine, and cerebrospinal fluid that contain small amounts of tumor cells, DNA/RNA coding material, peptides, and metabolites. Within the liquid biome, 2 broad categories of biomarkers can exist: tumor-derived, which can be directly traced to the tumor, and tumor-associated, which can be traced back to the response of the body to disease. Although tumor-associated biomarkers are promising liquid biopsy candidates, recent advances in biomarker enrichment and detection have allowed concentration on a new class of biomarker: tumor-derived biomarkers. This review focuses on making the distinction between the 2 biomarker categories and highlights promising new direction.

Anesthetic‑specific lncRNA and mRNA profile changes in blood during colorectal cancer resection: A prospective, matched‑case pilot study

Prometastatic and antitumor effects of different anesthetics have been previously analyzed in several studies with conflicting results. Thus, the underlying perioperative molecular mechanisms mediated by anesthetics potentially affecting tumor phenotype and metastasis remain unclear. It was hypothesized that anesthetic‑specific long non‑coding RNA (lncRNA) expression changes are induced in the blood circulation and play a crucial role in tumor outcome. In the present study, high‑throughput sequencing and quantitative PCR were performed in order to identify lncRNA and mRNA expression changes affected by two therapeutic regimes, total intravenous anesthesia (TIVA) and volatile anesthetic gas (VAG) in patients undergoing colorectal cancer (CRC) resection. Total blood RNA was isolated prior to and following resection and characterized using RNA sequencing. mRNA‑lncRNA interactions and their roles in cancer‑related signaling of differentially expressed lncRNAs were identified using bioinformatics analyses. The comparison of these two time points revealed 35 differentially expressed lncRNAs in the TIVA‑group, and 25 in the VAG‑group, whereas eight were shared by both groups. Two lncRNAs in the TIVA‑group, and 23 in the VAG‑group of in silico identified target‑mRNAs were confirmed as differentially regulated in the NGS dataset of the present study. Pathway analysis was performed and cancer relevant canonical pathways for TIVA were identified. Target‑mRNA analysis of VAG revealed a markedly worsened immunological response against cancer. In this proof‑of‑concept study, anesthesic‑specific expression changes in lncRNA and mRNA profiles in blood were successfully identified. Moreover, the data of the present study provide the first evidence that anesthesia‑induced lncRNA pattern changes may contribute further in the observed differences in CRC outcome following tumor resection.

Lipopolysaccharide facilitates immune escape of hepatocellular carcinoma cells via m6A modification of lncRNA MIR155HG to upregulate PD-L1 expression

Recent studies have suggested that the initiation and progression of hepatocellular carcinoma (HCC) are closely associated with lipopolysaccharide (LPS) of intestinal bacteria. However, the role of LPS in immune regulation of HCC remains largely unknown. An orthotopic Hepa1-6 tumor model of HCC was constructed to analyze the effect of LPS on the expression of immune checkpoint molecules PD-1 and PD-L1. Then we verified the regulation of PD-L1 by LPS in HCC cells. Based on the previous finding that lncRNA MIR155HG regulates PD-L1 expression in HCC cells, we analyzed the relationship of LPS signaling pathway molecules with PD-L1 and MIR155HG by bioinformatics. The molecular mechanism of MIR155HG regulating PD-L1 expression induced by LPS was investigated by RNA pull-down followed by mass spectrometry, RNA immunoprecipitation, fluorescence in situ hybridization, and luciferase reporter assay. Finally, the HepG2 xenograft model was established to determine the role of MIR155HG on PD-L1 expression in vivo. We showed that LPS induced PD-1 and PD-L1 expression in mouse tumor tissues and induced PD-L1 expression in HCC cells. Mechanistically, upregulation of METTL14 by LPS promotes the m6A methylation of MIR155HG, which stabilizes MIR155HG relying on the "reader" protein ELAVL1 (also known as HuR)-dependent pathway. Moreover, MIR155HG functions as a competing endogenous RNA (ceRNA) to modulate the expression of PD-L1 by miR-223/STAT1 axis. Our results suggested that LPS plays a critical role in immune escape of HCC through METTL14/MIR155HG/PD-L1 axis. This study provides a new insight for understanding the complex immune microenvironment of HCC. 1. LPS plays a critical role in immune escape of HCC, especially HCC with cirrhosis. 2. Our study reveals that LPS regulates PD-L1 by m6A modification of lncRNA in HCC. 3. MIR155HG plays an important role in LPS induced PD-L1 expression. 4. LPS-MIR155HG-PD-L1 regulatory axis provides a new target for the treatment of HCC.

High level of LncRNA MAPKAPK5-AS1 predicts poor prognosis and contributes to the malignant proliferation and EMT of non-small cell lung cancer via sponging miR-490-3p from HMGB2

BACKGROUND

Patients with non-small cell lung cancer (NSCLC) show a low survival rate, owing to the lack of early diagnostic method and high invasiveness. Long non-coding RNA MAPKAPK5-AS1 that regulates tumor genesis and progression through multiple signals, is upregulated and involved in the growth and apoptosis in lung adenocarcinoma (LUAD).

OBJECTIVE

To investigate whether MAPKAPK5-AS1 affected the malignant progression of NSCLC.

METHODS

The levels of MAPKAPK5-AS1, miR-490-3p and HMGB2 in lung cancer were first analyzed through StarBase website, and confirmed by a quantitative reverse transcriptase-PCR (qRT-PCR) assay. The biological functions of NSCLC cells were examined by CCK-8, 5-ethynyl-2'-deoxyuridine (EdU) and flow cytometry assays. The potential binding sequences lncRNA-miRNA and miRNA-mRNA were predicted by StarBase software and verified via dual luciferase reporter experiment. The effects of MAPKAPK5-AS1 on tumor growth were evaluated in a xenografted mice model.

RESULTS

The expression of MAPKAPK5-AS1 was upregulated in tumor tissues from NSCLC patients. Patients with high expression of MAPKAPK5-AS1 had higher tumor size, advanced TNM stage, higher incidence of lymph node and distant metastasis, and shorter overall survival. Knockdown of MAPKAPK5-AS1 inhibited the proliferation, induced apoptosis and blocked epithelial mesenchymal transformation (EMT) of NSCLC cells. Mechanically, MAPKAPK5-AS1 could upregulate the HMGB2 level in NSCLC cells through competitively binding to miR-490-3p. MiR-490-3p inhibitor reversed the roles of MAPKAPK5-AS1 knockdown on tumor cell proliferation, apoptosis and EMT. Also, HMGB2 knockdown suppressed tumor cell malignant phenotypes. Furthermore, interference of MAPKAPK5-AS1 slowed NSCLC tumor growth in vivo.

CONCLUSION

Knockdown of MAPKAPK5-AS1 inhibited the aggressive tumor phenotypes through miR-490-3p/HMGB2 axis in NSCLC. MAPKAPK5-AS1/miR-490-3p/HMGB2 might be potential biomarkers or therapeutic targets for NSCLC.

Integrated analysis of immune- and apoptosis-related lncRNA-miRNA-mRNA regulatory network in children with Henoch Schönlein purpura nephritis

BACKGROUND

Long noncoding RNAs (lncRNAs) play important roles in the regulation of immunological and apoptotic function. This study aimed to explore the critical immune- and apoptosis-related lncRNAs in the occurrence and development of Henoch-Schönlein purpura nephritis (HSPN) in children.

METHODS

Differential analysis was employed to identify the differentially expressed lncRNAs, as well as the immune- and apoptosis-related mRNAs in children with HSPN. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were used to validate the immunological and apoptotic roles of the differentially expressed immune- and apoptosis-related lncRNAs and mRNAs. Spearman's correlation analysis was performed to analyze the differentially expressed lncRNAs and immune- and apoptosis-related messenger RNAs (mRNAs). Based on the competing endogenous RNA (ceRNA) mechanism, the immune- and apoptosis-related lncRNA-microRNA (miRNA)-mRNA regulatory network was then constructed in children with HSPN. The expression levels of the lncRNAs in the lncRNA-miRNA-mRNA regulatory network were further confirmed by quantitative real-time polymerase chain in the peripheral blood samples of children with HSPN.

RESULTS

By intersecting the differentially expressed immune-related and apoptosis-related genes through GO and KEGG analyses, a total of 43 genes were identified in children with HSPN, and 100 lncRNAs highly correlated with the above genes were identified by correlation analysis. The immune- and apoptosis-related lncRNA-miRNA-mRNA regulatory network was then established based on ceRNA mechanism. Dysregulation of a total of 11 lncRNAs were discovered, including upregulated SNHG3, LINC00152, TUG1, GAS5, FGD5-AS1, DLEU2, and SCARNA9; and downregulated SNHG1, NEAT1, DISC1-IT1, and PVT1. The validation conducted in the clinical samples also suggested that the above lncRNAs in the specific regulatory network may act as potential biomarkers with prognosis in children with HSPN.

CONCLUSIONS

LncRNAs may play essential regulatory roles in the occurrence and development of HSPN in children, and the immune- and apoptosis-related lncRNA-miRNA-mRNA regulatory network might be the underlying molecular mechanism that dissects the disease pathogenesis. In addition, the dysregulated lncRNAs in the regulatory network may be novel biomarkers for the diagnosis and therapy of HSPN in children.

LncRNA SNHG25 Promotes Glioma Progression Through Activating MAPK Signaling

Numerous studies indicated that long non-coding RNAs (lncRNAs) play critical roles in glioma initiation and progression. SNHG25 is a newly identified lncRNA. And the functional role and molecular mechanism of SNHG25 in glioma cells have not been investigated. In this study, we found that SNHG25 was upregulated in glioma cells and tissues. CCK-8, EDU, and colony formation assays demonstrated that SNHG25 knockdown markedly inhibited glioma cell proliferation. In vivo studies showed that SNHG25 knockdown significantly inhibited tumor growth. Further studies indicated that SNHG25 positively regulated MAP2K2 through sponging miR-579-5p. High expression of SNHG25 activated MAPK signaling through MAP2K2. These data suggest that SNHG25 is a potential target and biomarker for glioma.

An immune-related microRNA signature prognostic model for pancreatic carcinoma and association with immune microenvironment

To establish a prognostic model based on immune-related microRNA (miRNA) for pancreatic carcinoma. Weighted correlation network analysis (WGCNA) was performed using the "WGCNA" package to find the key module genes involved in pancreatic carcinoma. Spearman correlation analysis was conducted to screen immune-related miRNAs. Uni- and multi-variate COX regression analyses were carried out to identify miRNAs prognostic for overall survival (OS) of pancreatic carcinoma, which were then combined to generate a prognostic model. Kaplan–Meier survival analysis, receiver operating characteristic (ROC) analysis, distribution plot of survival status in patients and regression analysis were collectively performed to study the accuracy of the model in prognosis. Target genes of the miRNAs in the model were intersected with the key module genes, and a miRNA–mRNA network was generated and visualized by Cytoscape3.8.0. TIMER analysis was conducted to study the abundance of immune infiltrates in tumor microenvironment of pancreatic carcinoma. Expression levels of immune checkpoint genes in subgroups stratified by the model were compared by Wilcoxon test. Gene Set Enrichment Analysis (GSEA) was performed to analyze the enriched signaling pathways between subgroups. Differential analysis revealed 1826 genes differentially up-regulated in pancreatic carcinoma and 1276 genes differentially down-regulated. A total of 700 immune-related miRNAs were obtained, of which 7 miRNAs were significantly associated with OS of patients and used to establish a prognostic model with accurate predictive performance. There were 99 mRNAs overlapped from the 318 target genes of the 7 miRNAs and the key modules genes analyzed by WGCNA. Patient samples were categorized as high or low risk according to the prognostic model, which were significantly associated with dendritic cell infiltration and expression of immune checkpoint genes (TNFSF9, TNFRSF9, KIR3DL1, HAVCR2, CD276 and CD80). GSEA showed remarkably enriched signaling pathways in the two subgroups. This study identified an immune-related 7-miRNA based prognostic model for pancreatic carcinoma, which could be used as a reliable tool for prognosis.

Identification of an immune-related gene signature as a prognostic target and the immune microenvironment for adrenocortical carcinoma

BACKGROUND

Adrenocortical carcinoma (ACC) is a rare endocrine malignancy. Even with complete tumor resection and adjuvant therapies, the prognosis of patients with ACC remains unsatisfactory. In the microtumor environment, the impact of a disordered immune system and abnormal immune responses is enormous. To improve treatment, novel prognostic predictors and treatment targets for ACC need to be identified. Hence, credible prognostic biomarkers of immune-associated genes (IRGs) should be explored and developed.

MATERIAL AND METHODS

We downloaded RNA-sequencing data and clinical data from The Cancer Genome Atlas (TCGA) data set, Genotype-Tissue Expression data set, and Gene Expression Omnibus data set. Gene set enrichment analysis (GSEA) was applied to reveal the potential functions of differentially expressed genes.

RESULTS

GSEA indicated an association between ACC and immune-related functions. We obtained 332 IRGs and constructed a prognostic signature on the strength of 3 IRGs (INHBA, HELLS, and HDAC4) in the training cohort. The high-risk group had significantly poorer overall survival than the low-risk group (p < .001). Multivariate Cox regression was performed with the signature as an independent prognostic indicator for ACC. The testing cohort and the entire TCGA ACC cohort were utilized to validate these findings. Moreover, external validation was conducted in the GSE10927 and GSE19750 cohorts. The tumor-infiltrating immune cells analysis indicated that the quantity of T cells, natural killer cells, macrophage cells, myeloid dendritic cells, and mast cells in the immune microenvironment differed between the low-risk and high-risk groups.

CONCLUSION

Our three-IRG prognostic signature and the three IRGs can be used as prognostic indicators and potential immunotherapeutic targets for ACC. Inhibitors of the three novel IRGs might activate immune cells and play a synergistic role in combination therapy with immunotherapy for ACC in the future.

Identification of an autophagy-related 12-lncRNA signature and evaluation of NFYC-AS1 as a pro-cancer factor in lung adenocarcinoma

Objective: To develop an autophagy-related lncRNA-based risk signature and corresponding nomogram to predict overall survival (OS) for LUAD patients and investigate the possible meaning of screened factors.

Methods: Differentially expressed lncRNAs and autophagy genes were screened between normal and LUAD tumor samples from the TCGA LUAD dataset. Univariate and multivariate Cox regression analyses were performed to construct the lncRNA-based risk signature and nomogram incorporating clinical information. Then, the accuracy and sensitivity were confirmed by the AUC of ROC curves in both training and validation cohorts. qPCR, immunoblot, shRNA, and ectopic expression were used to verify the positive regulation of NFYC-AS1 on BIRC6. CCK-8, immunofluorescence, and flow cytometry were used to confirm the influence of NFYC-AS1 on cell proliferation, autophagy, and apoptosis via BIRC6.

Results: A 12-lncRNA risk signature and a nomogram combining related clinical information were constructed. Furthermore, the abnormal increase of NFYC-AS1 may promote LUAD progression through the autophagy-related gene BIRC6.

Conclusion: 12-lncRNA signature may function as a predictive marker for LUAD patients, and NFYC-AS1 along with BIRC6 may function as carcinogenic factors in a combinatorial manner.

AC099850.3/NCAPG Axis Predicts Poor Prognosis and is Associated with Resistance to EGFR Tyrosine-Kinase Inhibitors in Lung Adenocarcinoma

Background

TKI-acquired resistance markedly interferes with treatment of lung cancer patients with EGFR mutant features. Long non-coding RNAs (lncRNAs) modify EGFR-TKI resistance during tumor progression. Non-structural maintenance of chromosomes condensin I complex subunit G (NCAPG) is a mitosis-related protein that is involved in tumorigenesis. We investigated the potential regulatory lncRNAs of NCAPG in lung adenocarcinoma (LUAD) and assessed their roles in EGFR-TKI resistance.

Methods

Data for 1678 lung cancer patients were retrieved from TCGA and GEO databases and used to evaluate NCAPG and lncRNAs expressions, as well as their prognostic significance in LUAD. Protein levels of NCAPG in LUAD were validated by immuno-histochemistry. To assess the relationship between NCAPG levels and EGFR-TKIs sensitivity, a cohort of 57 LUAD patients administered with EGFR-TKIs was used.

Results

Both NCAPG and lncRNA AC099850.3 were over-expressed in LUAD tissues, and correlated with tumor progression and poor prognosis in LUAD. LncRNA AC099850.3 was identified as a potential regulator of NCAPG expressions. The AC099850.3/NCAGP axis was markedly correlated with EGFR mutations and IC₅₀ of EGFR-TKIs. Besides, elevated NCAPG levels were associated with EGFR-TKIs resistance in 57 LUAD patients undergoing TKIs treatment. Gene set enrichment analysis revealed that both AC099850.3 and NCAGP were abundant in the cell cycle and the p53 signaling pathway.

Conclusion

The AC099850.3/NCAPG axis is a potential prognostic predictor and therapeutic biomarker for EGFR-TKIs in LUAD.

Bioinformatics Analysis for Constructing a Six-Immune-Related Long Noncoding RNA Signature as a Prognostic Model of Hepatocellular Carcinoma

The present study was aimed at identifying the potential prognostic biomarkers of the immune-related long noncoding RNA (IRL) signature for patients with hepatocellular carcinoma (HCC). RNA-sequencing data and clinical information about HCC were obtained from The Cancer Genome Atlas. The IRLs were determined with regard to the coexpression of immune-related genes and differentially expressed lncRNAs. The survival IRLs were obtained using the univariate Cox analysis. Subsequently, the prognosis model was constructed via the multivariate Cox analysis. Subsequently, functional annotation was conducted using Gene Set Enrichment Analysis (GSEA) and principal component analysis (PCA). In total, 341 IRLs were identified, and 6 IRLs were found to have a highly significant association with the prognosis of patients with HCC. The immune prognosis model was constructed with these 6 IRLs (AC099850.4, negative regulator of antiviral response, AL031985.3, PRRT3-antisense RNA1, AL365203.2, and long intergenic nonprotein coding RNA 1224) using the multivariate Cox regression analysis. In addition, immune-related prognosis signatures were confirmed as an independent prognostic factor. The association between prognostic signatures and immune infiltration indicated that the 6 lncRNAs could reflect the immune status of the tumor. Collectively, the present study demonstrates that six-lncRNA signatures may be potential biomarkers to predict the prognosis of patients with HCC.

Immune-Related LncRNAs as Prognostic Factors for Pediatric Rhabdoid Tumor of the Kidney

Background

Immune-related long noncoding RNAs (IrlncRNAs) are recognized as important prognostic factors in a variety of cancers, but thus far, their prognostic value in pediatric rhabdoid tumor of the kidney (pRTK) has not been reported. Here, we clarified the associations between IrlncRNAs and overall survival (OS) of pRTK patients and constructed a model to predict their prognosis.

Methods

We accessed RNA sequencing data and corresponding clinical data of pRTK from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database. An expression profile of immune-related genes (Irgenes) and lncRNAs of pRTK was extracted from the RNA sequencing data. IrlncRNAs were defined by co-expression analysis of lncRNAs and Irgenes. The limma R package was used to identify differential expression IrlncRNAs. Univariate and multivariate Cox regression analyses were conducted to build a prognostic IrlncRNAs model. The performance of this prognostic model was validated by multimethods, like ROC curve analysis.

Results

A total of 1097 IrlncRNAs were defined. Univariate Cox regression analysis identified 7 IrlncRNAs (AC004791.2, AP003068.23, RP11-54O7.14, RP11-680F8.1, TBC1D3P1-DHX40P1, TUNAR, and XXbac-BPG308K3.5) and were significantly associated with OS. Multivariate regression analysis constructed the best prognostic model based on the expression of AC004791.2, AP003068.23, RP11-54O7.14, TBC1D3P1-DHX40P1, and TUNAR. According to the prognostic model, a risk score of each patient was calculated, and patients were divided into high-risk and low-risk groups accordingly. The survival time of low-risk patients was significantly better than high-risk patients (p < 0.001). Univariate (hazard ratio 1.098, 95% confidence interval 1.048-1.149, p value <0.001) and multivariate (hazard ratio 1.095, 95% confidence interval 1.043-1.150, p value <0.001) analyses confirmed that the prognostic model was reliable and independent in prediction of OS. Time-dependent ROC analysis showed that 1-year survival AUC of prognostic model, stage, age, and sex was 0.824, 0.673, 0.531, and 0.495, respectively, which suggested that the prognostic model was the best predictor of survival in pRTK patients.

Conclusions

The prognostic model based on 5 IrlncRNAs was robust and could better predict the survival of pRTK than other clinical factors. Additionally, the mechanism of regulation and action of prognosis-associated lncRNAs could provide new avenues for basic research to explore the mechanism of tumor initiation and development in order to prevent and treat pRTK.

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.

Genome Instability-Associated Long Non-Coding RNAs Reveal Biomarkers for Glioma Immunotherapy and Prognosis

Genome instability is a hallmark of tumors and is involved in proliferation, invasion, migration, and treatment resistance of many tumors. However, the relationship of genome instability with gliomas remains unclear. Here, we constructed genome instability-derived long non-coding RNA (lncRNA)-based gene signatures (GILncSig) using genome instability-related lncRNAs derived from somatic mutations. Multiple platforms were used to confirm that the GILncSig were closely related to patient prognosis and clinical characteristics. We found that GILncSig, the glioma microenvironment, and glioma cell DNA methylation-based stemness index (mDNAsi) interacted with each other to form a complex regulatory network. In summary, this study confirmed that GILncSig was an independent prognostic indicator for patients, distinguished high-risk and low-risk groups, and affected immune-cell infiltration and tumor-cell stemness indicators (mDNAsi) in the tumor microenvironment, resulting in tumor heterogeneity and immunotherapy resistance. GILncSig are expected to provide new molecular targets for the clinical treatment of patients with gliomas.

MAPKAPK5-AS1 drives the progression of hepatocellular carcinoma via regulating miR-429/ZEB1 axis

Background

Hepatocellular carcinoma (HCC) is a common malignancy. Long non-coding RNAs (lncRNAs) partake in the progression of HCC. However, the role of lncRNA MAPKAPK5-AS1 in the development of HCC has not been fully clarified.

Methods

RNA sequencing data and quantitative real-time polymerase chain reaction (qRT-PCR) were adopted to analyze MAPKAPK5-AS1, miR-429 and ZEB1 mRNA expressions in HCC tissues and cell lines. Western blot was used to detect ZEB1, E-cadherin and N-cadherin protein expressions. 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT), Transwell and flow cytometry assays were adopted to analyze the effects of MAPKAPK5-AS1 on cell proliferation, migration, invasion and apoptosis. Besides, luciferase reporter assay was used to detect the targeting relationship between miR-429 and MAPKAPK5-AS1 or ZEB1 3’UTR. The xenograft tumor mouse models were used to explore the effect of MAPKAPK5-AS1 on lung metastasis of HCC cells.

Results

MAPKAPK5-AS1 and ZEB1 expressions were up-regulated in HCC tissues, and miR-429 expression is down-regulated in HCC tissues. MAPKAPK5-AS1 knockdown could significantly impede HCC cell proliferation, migration, invasion and epithelial-mesenchymal transition (EMT), as well as promote cell apoptosis. MAPKAPK5-AS1 overexpression could enhance L02 cell proliferation, migration, invasion and EMT, and inhibit cell apoptosis. MiR-429 was validated to be the target of MAPKAPK5-AS1, and miR-429 inhibitors could partially offset the effects of knocking down MAPKAPK5-AS1 on HCC cells. MAPKAPK5-AS1 could positively regulate ZEB1 expression through repressing miR-429. Moreover, fewer lung metastatic nodules were observed in the lung tissues of nude mice when the MAPKAPK5-AS1 was knocked down in HCC cells.

Conclusion

MAPKAPK5-AS1 can adsorb miR-429 to promote ZEB1 expression to participate in the development of HCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12860-022-00420-x.

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.

Long non-coding RNA HCG18 promotes gastric cancer progression by regulating miRNA-146a-5p/tumor necrosis factor receptor-associated factor 6 axis

Although long non-coding RNAs (lncRNAs) have been demonstrated to be dysregulated in gastric cancer (GC), the function of lncRNA HCG18 (HCG18) in GC is elusive. Therefore, the study was designed to evaluate the underlying mechanism of HCG18 in GC. HCG18 and microRNA 146a-5p (miR-146a-5p) levels in GC were evaluated by RT-qPCR. The effects of miR-146a-5p and HCG18 on GC cell function were examined using Transwell assay, colony formation, and CCK-8 assays. Tumor necrosis factor receptor-associated factor 6 (TRAF6) and p65 expression levels were detected by Western blot. HCG18 and miR-146a-5p target genes were identified using luciferase reporter and bioinformatics assays. HCG18 expression was increased in GC. HCG18 overexpression significantly increased GC cell proliferation, invasion, and migration. Furthermore, HCG18 overexpression inhibited miR-146a-5p and upregulated TRAF6 and p65 expression. Finally, miR-146a-5p/TRAF6 was found to be involved in the role of HCG18 in GC progression in vivo. Altogether, HCG18 promotes GC progression via the miR-146a-5p/TRAF6 axis and could be a GC treatment target.

Exploration of Prognostic Immune-Related Genes and lncRNAs Biomarkers in Kidney Renal Clear Cell Carcinoma and Its Crosstalk with Acute Kidney Injury

Kidney renal clear cell carcinoma (KIRC) has a poor prognosis and a high death rate globally. Cancer prognosis is strongly linked to immune-related genes (IRGs), according to numerous research. We utilized KIRC RNA-seq data from the TCGA database to build a prognostic model incorporating seven immune-related (IR) lncRNAs, and we constructed the model using LASSO regression. Additionally, we calculated a risk score for each patient using a prognostic model that divided patients into high-risk and low-risk groups. The ESTIMATE and CIBERSORT methodologies were then used to analyze the differences in the tumor microenvironment of the two groups of patients. Finally, we predicted three small molecule drugs that may have potential therapeutic effects for high-risk patients. We combined the acute kidney injury dataset to obtain differential genes that may serve standard biological functions with two risk groups. Our study shows that the model we constructed for IR-lncRNAs has reliable predictive efficacy for patients with KIRC.

Constructing a Novel Signature Based on Immune-Related lncRNA to Improve Prognosis Prediction of Cervical Squamous Cell Carcinoma Patients

We downloaded gene expression data, clinical data, and somatic mutation data of cervical squamous cell carcinoma (CSCC) patients from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Predictive lncRNAs were screened using univariate analysis and least absolute shrinkage and selection operator (LASSO) regression, and risk scores were calculated for each patient according to the expression levels of lncRNAs and regression coefficients to establish a risk model that could be a novel signature. We assessed the correlation between immune infiltration status, chemotherapeutics sensitivity, immune checkpoint proteins (ICP), and the signature. Therefore, we selected 11 immune-related lncRNAs (WWC2,AS2, STXBP5.AS1, ERICH6.AS1, USP30.AS1, LINC02073, RBAKDN, IL21R.AS1, LINC02078, DLEU1, LINC00426, BOLA3.AS1) to construct the risk model. Patients who were in the high-risk group had a shorter survival time than those in the low-risk group (p < 0.001). Risk scores in the signature were negatively correlated with macrophage M1, macrophage M2, and T cell CD8 + ; what's more, T cell CD8 + was higher in the low-risk group. The expression levels of ICP such as PD-L1, PD-1, CTLA-4, TIGIT, LAG-3, and TIM-3 were substantially higher in the low-risk group. For chemotherapeutic agents, high-risk scores were associated with higher half-inhibitory concentrations (IC50) of cisplatin. These findings suggested that the risk model can be a novel signature for predicting CSCC patients' prognosis, and it also can be used to formulate clinical treatment plans for CSCC patients.

Bioinformatics Analysis of Prognostic Value of SPC24 in ccRCC and Pan-Cancer

Purpose

Clear cell renal cell carcinoma (ccRCC) is one of the most common diseases in the world, with high morbidity and mortality. Recent studies have revealed the important role of SPC24, a subunit of the Ndc80 complex, in the occurrence and development of carcinoma. However, the latent effect of SPC24 in the progress of ccRCC remains to be further explored. The intent of this research is to investigate whether SPC24 can be used as an index to predict the progression of ccRCC and to explore its relationship with the immune microenvironment and pan-cancer.

Materials and Methods

Based on data from public databases, we determined the expression level and clinical value of SPC24 in ccRCC and human pan-cancer. RT-qPCR analysis was carried out to detect the expression level of SPC24 in the OSRC/786O (human ccRCC cells) cell lines and HK2 (human normal kidney cells) cell line. The signal transduction pathways activated by different levels of SPC24 expression were explored by Gene Set Enrichment Analysis (GSEA), and the CIBERSORT algorithm was applied to analyze the relationship between infiltrating immune cells and SPC24 expression in ccRCC and pan-cancer tissues.

Results

SPC24 is overexpressed in ccRCC and several types of tumors, which is associated with poor prognosis. GSEA and CIBERSORT algorithms suggested that the high expression group of SPC24 enriched various pathways including immune-related pathways, and the several infiltrated immunocytes were related to the expression of SPC24.

Conclusion

Our study revealed that SPC24 is a prognostic factor in ccRCC related to immunomodulation and has generalized value in pan-cancer.

A Novel lncRNA Panel Related to Ferroptosis, Tumor Progression, and Microenvironment is a Robust Prognostic Indicator for Glioma Patients

Objective: To establish a lncRNA panel related to ferroptosis, tumor progression, and microenvironment for prognostic estimation in patients with glioma.

Methods: LncRNAs associated with tumor progression and microenvironment were screened via the weighted gene co-expression network analysis (WGCNA). Overlapped lncRNAs highlighted in WGCNA, related to ferroptosis, and incorporated in Chinese Glioma Genome Atlas (CGGA) were identified as hub lncRNAs. With expression profiles of the hub lncRNA, we conducted the least absolute shrinkage and selection operator (LASSO) regression and built a ferroptosis-related lncRNA signature to separate glioma patients with distinct survival outcomes. The lncRNA signature was validated in TCGA, the CGGA_693, and CGGA_325 cohorts using Kaplan-Meier survival analysis and ROC curves. The ferroptosis-related lncRNA panel was validated with 15 glioma samples using quantitative real-time PCR (qRT-PCR). Multivariate Cox regression was performed, and a nomogram was mapped and validated. Immune infiltration correlated to the signature was explored using TIMER and CIBERSORT algorithms.

Results: The present study identified 30 hub lncRNAs related to ferroptosis, tumor progression, and microenvironment. With the 30 hub lncRNAs, we developed a lncRNA signature with distinct stratification of survival chance in patients with glioma in two independent cohorts (HRs>1, p < 0.05). The lncRNA signature revealed a panel of 14 lncRNAs, i.e., APCDD1L-AS1, H19, LINC00205, LINC00346, LINC00475, LINC00484, LINC00601, LINC00664, LINC00886, LUCAT1, MIR155HG, NEAT1, PVT1, and SNHG18. These lncRNA expressions were validated in clinical specimens using qRT-PCR. Robust predictive accuracies of the signature were present across different datasets at multiple timepoints. With univariate and multivariate regressions, we demonstrated that the risk score based on the lncRNA signature is an independent prognostic indicator after clinical factors were adjusted. A nomogram was constructed with these prognostic factors, and it has demonstrated decent classification and accuracy. Additionally, the signature-based classification was observed to be correlated with multiple clinical characteristics and molecular subtypes. Further, extensive immune cells were upregulated in the high-risk group, such as CD8⁺ T cell, neutrophil, macrophage, and myeloid dendritic cell, indicating increased immune infiltrations.

Conclusion: We established a novel ferroptosis-related lncRNA signature that could effectively stratify the prognosis of glioma patients with adequate predictive performance.

Prognostic value and immune infiltration of novel signatures in colon cancer microenvironment

Background

Growing evidence has shown that the prognosis for colon cancer depends on changes in microenvironment. The purpose of this study was to elucidate the prognostic value of long noncoding RNAs (lncRNAs) related to immune microenvironment (IM) in colon cancer.

Methods

Single sample gene set enrichment analysis (ssGSEA) was used to identify the subtypes of colon cancer based on the immune genomes of 29 immune signatures. Cox regression analysis identified a lncRNA signatures associated with immune infiltration. The Tumor Immune Estimation Resource database was used to analyze immune cell content.

Results

Colon cancer samples were divided into three subtypes by unsupervised cluster analysis. Cox regression analysis identified an immune infiltration-related 5-lncRNA signature. This signature combined with clinical factors can effectively improve the predictive ability for the overall survival (OS) of colon cancer. At the same time, we found that the expression of H19 affects the content of B cells and macrophages in the microenvironment of colon cancer and affects the prognosis of colon cancer. Finally, we constructed the H19 regulatory network and further analyzed the possible mechanisms. We found that knocking down the expression of H19 can significantly inhibit the expression of CCND1 and VEGFA. At the same time, the immunohistochemical assay found that the expression of CCND1 and VEGFA protein was significantly positively correlated with the infiltration of M2 type macrophages.

Conclusion

The findings may help to formulate clinical strategies and understand the underlying mechanisms of H19 regulation. H19 may be a biomarker for targeted treatment of colon cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-02342-8.

Identification of Immune-Related lncRNA Prognostic Signature and Molecular Subtypes for Glioblastoma

Background

Glioblastoma multiforme (GBM) is extensively genetically and transcriptionally heterogeneous, which poses challenges for classification and management. Long noncoding RNAs (lncRNAs) play a critical role in the development and progression of GBM, especially in tumor-associated immune processes. Therefore, it is necessary to develop an immune-related lncRNAs (irlncRNAs) signature.

Methods

Univariate and multivariate Cox regression analyses were utilized to construct a prognostic model. GBM-specific CeRNA and PPI network was constructed to predict lncRNAs targets and evaluate the interactions of immune mRNAs translated proteins. GO and KEGG pathway analyses were used to show the biological functions and pathways of CeRNA network-related immunity genes. Consensus Cluster Plus analysis was used for GBM gene clustering. Then, we evaluated GBM subtype-specific prognostic values, clinical characteristics, genes and pathways, immune infiltration access single cell RNA-seq data, and chemotherapeutics efficacy. The hub genes were finally validated.

Results

A total of 17 prognostically related irlncRNAs were screened to build a prognostic model signature based on six key irlncRNAs. Based on GBM-specific CeRNAs and enrichment analysis, PLAU was predicted as a target of lncRNA-H19 and mainly enriched in the malignant related pathways. GBM subtype-A displayed the most favorable prognosis, high proportion of genes (IDH1, ATRX, and EGFR) mutation, chemoradiotherapy, and low risk and was characterized by low expression of four high-risk lncRNAs (H19, HOTAIRM1, AGAP2-AS1, and AC002456.1) and one mRNA KRT8. GSs with poor survival were mainly infiltrated by mesenchymal stem cells (MSCs) and astrocyte, and were more sensitive to gefitinib and roscovitine. Among GSs, three hub genes KRT8, NGFR, and TCEA3, were screened and validated to potentially play feasible oncogenic roles in GBM.

Conclusion

Construction of lncRNAs risk model and identification of GBM subtypes based on 17 irlncRNAs, which suggesting that irlncRNAs had the promising potential for clinical immunotherapy of GBM.

An immune-associated ten-long noncoding RNA signature for predicting overall survival in cervical cancer

BACKGROUND

Several immune-associated long non-coding RNA (lncRNA) signatures have been reported as prognostic models in different types of cancers; however, the immune-associated lncRNA signature for predicting overall survival (OS) in cervical cancer is unknown.

METHODS

The lncRNA expression profiles and clinical data of cervical cancer were acquired from The Cancer Genome Atlas (TCGA) dataset. Immune-associated genes were extracted from the Molecular Signatures Database (MSigDB), and the immune-associated lncRNAs were extracted for Cox regression analysis. Principal component analysis (PCA) was used to distinguish the high and low risk status of cervical cancer patients. Gene Set Enrichment Analysis (GSEA) was used for functional analyses.

RESULTS

Cox regression analyses and the least absolute shrinkage and selection operator (LASSO) Cox regression model were used to construct an immune-associated ten-lncRNA signature (containing AL021807.1, AL109976.1, LINC02446, MIR4458HG, AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) for predicting OS in cervical cancer. The signature segregated the cervical cancer patients into 2 groups (high-risk group and low-risk group). The Kaplan-Meier survival curves of AL021807.1, AL109976.1, LINC02446, and MIR4458HG were statistically significant (P<0.05) and the others (including AC004540.2, AC009065.8, AC083809.1, AC055822.1, AP000904.1, and FBXL19-AS1) were not statistically significant (P>0.05). The Kaplan-Meier survival curves of the signature were statistically significant (P=1.134e-10), and the 5-year survival rate was 0.444 in the high-risk group [95% confidence interval (CI): 0.334 to 0.590] and 0.884 in the low-risk group (95% CI: 0.807 to 0.969). The area under curve (AUC) of the receiver operating characteristic (ROC) curve of the signature was 0.833. The concordance index (C-index) of the signature was 0.788 (95% CI: 0.730 to 0.846, P=1.884778e-22). The PCA successfully distinguished the high-risk group and low-risk group based on the signature. The GSEA showed that the signature-related protein coding genes (PCGs) may participate in immunologic biological processes and pathways.

CONCLUSIONS

This study revealed that the immune-associated ten-lncRNA signature is an independent factor for cervical cancer prognosis prediction, providing a bright future for immunotherapy of cervical cancer patients.

Long non-coding RNA profile study identifies an immune-related lncRNA prognostic signature for prostate adenocarcinoma

Prostate adenocarcinoma (PRAD) is the highest incidence rate of male urogenital morbidity worldwide. Long non-coding RNAs (lncRNAs), as a significant class of gene expression regulators, play a critical role in immune regulation. The purpose of this study is to explore a new immune related lncRNA signature to exactly predict the prognosis of PRAD patients. In this study, we conducted a genome-wide comparative analysis of lncRNA expression profiles in 532 patients with PRAD from the Cancer Genome Atlas (TCGA) database. The immune-related lncRNAs were identified by Cox regression model, and then a new five immune-related lncRNAs signature (FRMD6-AS2, AC008770.3, AC109460.3, AC011899.2, and AC008063.1) were constructed, which could predict the prognosis of PRAD patients. Univariate and multivariate Cox regression analysis showed that the signature could be an independent prognostic indicator of overall survival (OS). Through further study of different clinic-pathological parameters, we found that PRAD samples can be divided into high-risk groups with shorter OS and low-risk groups with longer OS by the signature. Principal component analysis showed that five immune-related lncRNA signature could distinguish the high-risk group from low-risk group in view of the immune-related lncRNAs. The difference of immune status between the two groups was observed by gene set enrichment analysis and the ESTIMATE algorithm. Except FRMD6-AS2, the expression of the other 4 lncRNAs were remarkably up-regulated in tumor tissues. In conclusion, the identified five immune-related lncRNAs signature had important clinical significance in prognosis prediction, and can be used as potential immunotherapy targets for PRAD patients.

Bioinformatics analysis based on immune-autophagy-related lncRNAs combined with immune infiltration in bladder cancer

Background

To construct a prognostic model based on immune-autophagy-related long noncoding RNA (IArlncRNAs), mainly to predict the overall survival rate (OS) of bladder cancer patients and investigate its possible mechanisms.

Methods

Transcriptome and clinical data were obtained from The Cancer Genome Atlas (TCGA) database. We identified the IArlncRNA by co-expression analysis, differential expression analysis, and Venn analysis. Then, we identified the independent prognostic IArlncRNAs by univariate Cox regression, LASSO regression, and multivariate Cox regression analysis. Moreover, we constructed the prognostic model based on the independent prognostic IArlncRNAs and clinical features. The proportion of 22 immune cell subtypes was analyzed by the CIBERSORT algorithm. Besides, we identified the differential proportion of 22 immune cell subtypes between the high- and low-risk groups. In addition, we identified the correlation between immune-infiltrating cells (screened by univariate Cox regression and multivariate Cox regression analysis) and IArlncRNAs by Pearson correlation analysis. Finally, we estimated the half-maximal inhibitory concentration (IC₅₀) of chemotherapeutic drugs in patients with bladder cancer based on the pRRophetic algorithm.

Results

Four IArlncRNAs were identified as independent prognostic factors, including AL136084.3, AC006270.1, Z84484.1, and AL513218.1. The OS of patients in the high-risk group was significantly worse compared to the low-risk group. The nomogram showed an excellent predictive effect with the C-index of 0.64. The calibration chart showed a good actual vs. predicted probability. B cells naïve, T cells CD8, T cells CD4 memory resting, T cells follicular helper, macrophages M1, dendritic resting and activated cells had higher infiltrations in the low-risk group and lower infiltration of macrophages M2. The fraction of macrophages M2 was positively associated with AL136084.3. The fraction of T cells CD8 was positively associated with Z84484.1. The fraction of M + macrophages M0 was negatively associated with Z84484.1. Further, we identified the differential IC₅₀ of 24 chemotherapeutic drugs between the high- and low-risk groups.

Conclusions

The prognostic model based on 4 IArlncRNAs showed an excellent predictive effect. Furthermore, we reasonably speculated that IArlncRNAs are directly or indirectly involved in the immune regulation of the tumor microenvironment (TME), as well as autophagy.

Establishment of an Immune-Related Gene Signature for Risk Stratification for Patients with Glioma

Glioma is a frequently seen primary malignant intracranial tumor, characterized by poor prognosis. The study is aimed at constructing a prognostic model for risk stratification in patients suffering from glioma. Weighted gene coexpression network analysis (WGCNA), integrated transcriptome analysis, and combining immune-related genes (IRGs) were used to identify core differentially expressed IRGs (DE IRGs). Subsequently, univariate and multivariate Cox regression analyses were utilized to establish an immune-related risk score (IRRS) model for risk stratification for glioma patients. Furthermore, a nomogram was developed for predicting glioma patients' overall survival (OS). The turquoise module (cor = 0.67; P < 0.001) and its genes (n = 1092) were significantly pertinent to glioma progression. Ultimately, multivariate Cox regression analysis constructed an IRRS model based on VEGFA, SOCS3, SPP1, and TGFB2 core DE IRGs, with a C-index of 0.811 (95% CI: 0.786-0.836). Then, Kaplan-Meier (KM) survival curves revealed that patients presenting high risk had a dismal outcome (P < 0.0001). Also, this IRRS model was found to be an independent prognostic indicator of gliomas' survival prediction, with HR of 1.89 (95% CI: 1.252-2.85) and 2.17 (95% CI: 1.493-3.14) in the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) datasets, respectively. We established the IRRS prognostic model, capable of effectively stratifying glioma population, convenient for decision-making in clinical practice.

Predicting the immune landscape of invasive breast carcinoma based on the novel signature of immune-related lncRNA

Background

The composition of the population of immune‐related long non‐coding ribonucleic acid (irlncRNA) generates a signature, irrespective of expression level, with potential value in predicting the survival status of patients with invasive breast carcinoma.

Methods

The current study uses univariate analysis to identify differentially expressed irlncRNA (DEirlncRNA) pairs from RNA‐Seq data from The Cancer Genome Atlas (TCGA). 36 pairs of DEirlncRNA pairs were identified. Using various algorithms to construct a model, we have compared the area under the curve and calculated the 5‐year curve of Akaike information criterion (AIC) values, which allows determination of the threshold indicating the maximum value for differentiation. Through cut‐off point to establish the optimal model for distinguishing high‐risk or low‐risk groups among breast cancer patients. We assigned individual patients with invasive breast cancer to either high risk or low risk groups depending on the cut‐off point, re‐evaluated the tumor immune cell infiltration, the effectiveness of chemotherapy, immunosuppressive biomarkers, and immunotherapy.

Results

After re‐assessing patients according to the threshold, we demonstrated an effective means of distinguish the severity of the disease, and identified patients with different clinicopathological characteristics, specific tumor immune infiltration states, high sensitivity to chemotherapy,wellpredicted response to immunotherapy and thus a more favorable survival outcome.

Conclusions

The current study presents novel findings regarding the use of irlncRNA without the need to predict precise expression levels in the prognosis of breast cancer patients and to indicate their suitability for anti‐tumor immunotherapy.

The Immune Heterogeneity Between Pulmonary Adenocarcinoma and Squamous Cell Carcinoma: A Comprehensive Analysis Based on lncRNA Model

Adenocarcinoma (AD) and squamous cell carcinoma (SCC) are both classified as major forms of non-small cell lung cancer, but differences in clinical prognoses and molecular mechanisms are remarkable. Recent studies have supported the importance of understanding immune status in that it influences clinical outcomes of cancer, and immunotherapies based on the theory of “immune editing” have had notable clinical success. Our study aimed to identify specific long non-coding (lnc) RNAs that control key immune-related genes and to use them to construct risk models for AD and SCC. Risk scores were used to separate patients into high- and low-risk groups, and we validated the prognostic significance of both risk scores with our own cohorts. A Gene Set Enrichment Analysis suggested that the immune responses of patients in the AD high-risk group and the SCC low-risk group tended to be weakened. Evaluation of immune infiltration revealed that the degree of infiltration of dendritic cells is of particular importance in AD. In addition, prediction of responses to immune checkpoint inhibitor (ICI) treatments, based on the T Cell Immune Dysfunction and Exclusion and immunophenoscore models, indicated that deterioration of the immune microenvironment is due mainly to T cell exclusion in AD patients and T cell dysfunction in SCC patients and that high-risk patients with SCC might benefit from ICI treatment. The prediction of downstream targets via The Cancer Proteome Atlas and RNA-seq analyses of a transfected lung cancer cell line indicated that the lncRNA LINC00996 is a potential therapeutic target in AD.

Identification of an epithelial-mesenchymal transition related long non-coding RNA (LncRNA) signature in Glioma

Epithelial–mesenchymal transition (EMT)-related long non-coding RNAs (lncRNAs) may be exploited as potential therapeutic targets in gliomas. However, the prognostic value of EMT-related lncRNAs in gliomas is unclear. We obtained lncRNAs from The Cancer Genome Atlas and constructed EMT-related lncRNA co-expression networks to identify EMT-related lncRNAs. The Chinese Glioma Genome Atlas (CGGA) was used for validation. Gene set enrichment and principal component analyses were used for functional annotation. The EMT–lncRNA co-expression networks were constructed. A real-time quantitative polymerase chain reaction assay was performed to validate the bioinformatics results. A nine-EMT-related lncRNAs (HAR1A, LINC00641, LINC00900, MIR210HG, MIR22HG, PVT1, SLC25A21-AS1, SNAI3-AS1, and SNHG18) signature was identified in patients with glioma. Patients in the low-risk group had a longer overall survival (OS) than those in the high-risk group (P < 0.0001). Additionally, patients in the high-risk group showed no deletion of chromosomal arms 1p and/or 19q, isocitrate dehydrogenase wild type, and higher World Health Organization grade. Moreover, the signature was identified as an independent factor and was significantly associated with OS (P = 0.041, hazard ratio = 1.806). These findings were further validated using the CGGA dataset. The low- and high-risk groups showed different EMT statuses based on principal component analysis. To study the regulatory function of lncRNAs, a lncRNA-mediated ceRNA network was constructed, which showed that complex interactions of lncRNA–miRNA–mRNA may be a potential cause of EMT progression in gliomas. This study showed that the nine-EMT-related lncRNA signature has a prognostic value in gliomas.

Long Non-Coding RNAs in Diagnosis, Treatment, Prognosis, and Progression of Glioma: A State-of-the-Art Review

Glioma is the most common malignant central nervous system tumor with significant mortality and morbidity. Despite considerable advances, the exact molecular pathways involved in tumor progression are not fully elucidated, and patients commonly face a poor prognosis. Long non-coding RNAs (lncRNAs) have recently drawn extra attention for their potential roles in different types of cancer as well as non-malignant diseases. More than 200 lncRNAs have been reported to be associated with glioma. We aimed to assess the roles of the most investigated lncRNAs in different stages of tumor progression and the mediating molecular pathways in addition to their clinical applications. lncRNAs are involved in different stages of tumor formation, invasion, and progression, including regulating the cell cycle, apoptosis, autophagy, epithelial-to-mesenchymal transition, tumor stemness, angiogenesis, the integrity of the blood-tumor-brain barrier, tumor metabolism, and immunological responses. The well-known oncogenic lncRNAs, which are upregulated in glioma, are H19, HOTAIR, PVT1, UCA1, XIST, CRNDE, FOXD2-AS1, ANRIL, HOXA11-AS, TP73-AS1, and DANCR. On the other hand, MEG3, GAS5, CCASC2, and TUSC7 are tumor suppressor lncRNAs, which are downregulated. While most studies reported oncogenic effects for MALAT1, TUG1, and NEAT1, there are some controversies regarding these lncRNAs. Expression levels of lncRNAs can be associated with tumor grade, survival, treatment response (chemotherapy drugs or radiotherapy), and overall prognosis. Moreover, circulatory levels of lncRNAs, such as MALAT1, H19, HOTAIR, NEAT1, TUG1, GAS5, LINK-A, and TUSC7, can provide non-invasive diagnostic and prognostic tools. Modulation of expression of lncRNAs using antisense oligonucleotides can lead to novel therapeutics. Notably, a profound understanding of the underlying molecular pathways involved in the function of lncRNAs is required to develop novel therapeutic targets. More investigations with large sample sizes and increased focus on in-vivo models are required to expand our understanding of the potential roles and application of lncRNAs in glioma.

Long Noncoding RNA LINC00707 Accelerates Tumorigenesis and Progression of Bladder Cancer via Targeting miR-145/CDCA3 Regulatory Loop

INTRODUCTION

Growing studies reveal that long noncoding RNA is involved in oncogenesis and progression. Previous studies have demonstrated that long intergenic noncoding RNA 00707 (LINC00707) stimulated tumor progress in numerous neoplasm types; however, the function of LINC00707 in bladder cancer (BC) was not yet clear. Our researches aimed to determine whether LINC00707 was dysregulated in BC and further study its biological functions.

METHODS

LINC00707 levels in BC tissues and cells were measured using reverse transcription-PCR (RT-PCR), and the associations between the levels of LINC00707 and clinicopathological features and the months of survival were also examined. Then, Cell Counting Kit-8 assays, flow cytometry, colony formation assays, and Transwell assays were applied for the assessment of the impact of LINC00707 on the abilities of BC cells. The interaction between LINC00707 and miR-145 or cell division cycle associated 3 was determined by luciferase reporter system and RT-PCR. Protein expressions of Wnt/β-catenin signaling were examined using RT-PCR and Western blot.

RESULTS

We found that LINC00707 expressions were notably upregulated in BC samples and cells. Higher expressions of LINC00707 were associated with T stage, grade, and shorter overall survival in BC patients. LINC00707 was also an independent prognostic factor for BC. In vitro assays confirmed that silencing LINC00707 expressions suppressed cell proliferation, colony formation, and metastasis. Mechanistic studies elucidated that LINC00707 was directly targeted to miR-145/CDCA3. Western blot assays revealed that Wnt/β-catenin signaling was inactivated by LINC00707 knockdown.

CONCLUSION

Our work offers new insight into the function of LINC00707 in the tumorigenesis of BC.

The Functional Characterization of Epigenetically Related lncRNAs Involved in Dysregulated CeRNA-CeRNA Networks Across Eight Cancer Types

Numerous studies have demonstrated that lncRNAs could compete with other RNAs to bind miRNAs, as competing endogenous RNAs (ceRNAs), to regulate each other. On the other hand, ceRNAs were found to be recurrently dysregulated in cancer status. However, limited studies considered the upstream epigenetic regulatory factors that disrupted the normal competing mechanism. In the present study, we constructed the lncRNA-associated dysregulated ceRNA networks across eight cancer types. lncRNAs in the individual dysregulated network and pan-cancer core dysregulated ceRNA subnetwork were found to play more important roles than mRNAs. Integrating lncRNA methylation profiles, we identified 49 epigenetically related (ER) lncRNAs involved in the dysregulated ceRNA networks, including 18 epigenetically activated (EA) lncRNAs, 18 epigenetically silenced (ES) lncRNAs, and 13 rewired ER lncRNAs across eight cancer types. Furthermore, we evaluated the epigenetic regulating patterns of these lncRNAs and screened nine pan-cancer ER lncRNAs (six EA and three ES lncRNAs). The nine lncRNAs were found to regulate the cancer hallmarks by competing with mRNAs. Moreover, we found that integrating the expression and methylation profiles of the nine lncRNAs could predict cancer incidence in eight cancer types robustly and the cancer outcome of several cancer types. These results provide an improved understanding of methylation regulation to ceRNA and offer novel potential molecular therapeutic targets for the diagnosis and prognosis across different cancer types.

Knockdown of HCG18 Inhibits Cell Viability, Migration and Invasion in Pediatric Osteosarcoma by Targeting miR-188-5p/FOXC1 Axis

Understanding the underlying mechanisms of pediatric osteosarcoma (OS) migration and invasion is important for prognosis and treatment. We tried to measure the expression of long non-coding RNA HLA complex group 18 (HCG18) in OS and reveal its function in the malignant behaviors of OS cells. This study detected the expression of HCG18, miR-188-5p and forkhead box C1 (FOXC1) in OS tissues and cell lines by quantitative real-time PCR (qRT-PCR). The relevance between miR-188-5p and HCG18 or FOXC1 was affirmed by dual-luciferase reporter (DLR) assay. Cell viability was analyzed by MTT assay. Transwell assay was utilized to test cell invasion and migration. FOXC1 protein expression was detected by western blot. HCG18 expression was elevated in OS tissues, and enhanced HCG18 expression was related to metastasis. HCG18 silencing repressed the viability, migration and invasion of OS cells. Moreover, HCG18 interacted with miR-188-5p. MiR-188-5p up-regulation repressed cell viability, invasion and migration in OS cells. FOXC1, a known target of miR-188-5p, was negatively modulated by miR-188-5p. Furthermore, miR-188-5p inhibition or FOXC1 over-expression partially abolished the reduced of cell viability, invasion and migration mediated by HCG18 silencing in OS cell lines. This study revealed that HCG18 knockdown repressed the viability, invasion and migration of OS cells by targeting miR-188-5p and regulating FOXC1 expression. Thus, HCG18/ miR-188-5p/FOX may be a hopeful target for OS therapy.

TEAD4 is an Immune Regulating-Related Prognostic Biomarker for Bladder Cancer and Possesses Generalization Value in Pan-Cancer

Recent studies have revealed the significant role of TEA domain family member 4 (TEAD4) in the development and progression of cancer. However, the potential role of TEAD4 in the progression of bladder cancer (BC) remains to be explored. The aim of this study was to determine whether TEAD4 could serve as a pan-cancer predictor of the prognosis for BC. Based on data mined from public databases, expression levels and clinical value of TEAD4 were identified in BC and human pan-cancers. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis was performed to detect the TEAD4 expression levels in BC cell lines. Gene Set Enrichment Analysis (GSEA) was carried out for functional analysis in BC, and the relationship between infiltrating immune cells and TEAD4 expression was evaluated by the CIBERSORT algorithm in BC and pan-cancer data. TEAD4 was overexpressed and associated with poor prognosis in BC and several types of cancers. GSEA and CIBERSORT algorithm suggested that various pathways including immune-related pathways were enriched in TEAD4 high expression group and several immunocytes infiltrated were correlated with the expression of TEAD4. This study revealed TEAD4 is an immune regulating-related predictor of prognosis for BC and has generalization value in pan-cancer.

Long non-coding RNAs in brain tumors: roles and potential as therapeutic targets

Brain tumors are associated with adverse outcomes despite improvements in radiation therapy, chemotherapy, and photodynamic therapy. However, treatment approaches are evolving, and new biological phenomena are being explored to identify the appropriate treatment of brain tumors. Long non-coding RNAs (lncRNAs), a type of non-coding RNA longer than 200 nucleotides, regulate gene expression at the transcriptional, post-transcriptional, and epigenetic levels and are involved in a variety of biological functions. Recent studies on lncRNAs have revealed their aberrant expression in various cancers, with distinct expression patterns associated with their instrumental roles in cancer. Abnormal expression of lncRNAs has also been identified in brain tumors. Here, we review the potential roles of lncRNAs and their biological functions in the context of brain tumors. We also summarize the current understanding of the molecular mechanisms and signaling pathways related to lncRNAs that may guide clinical trials for brain tumor therapy.

Development and validation of a set of novel and robust 4-lncRNA-based nomogram predicting prostate cancer survival by bioinformatics analysis

Background and objective

Accumulating evidence shows that long noncoding RNAs (lncRNAs) possess great potential in the diagnosis and prognosis of prostate cancer (PCa). Therefore, this study aimed to construct an lncRNA-based signature to more accurately predict the prognosis of different PCa patients, so as to improve patient management and prognosis.

Methods

Through univariate and multivariate Cox regression analysis, this study constructed a 4 lncRNAs-based prognosis nomogram for the classification and prediction of survival risk in patients with PCa based on TCGA data. Then we used the data of TCGA and ICGC to verify the performance of our prediction model. The receiver operating characteristic curve was plotted for detecting and validating our prediction model sensitivity and specificity. In addition, Cox regression analysis was conducted to examine whether the signature’s prediction ability was independent of additional clinicopathological variables. Possible biological functions for those prognostic lncRNAs were predicted on those 4 protein-coding genes (PCGs) related to lncRNAs.

Results

Four lncRNAs (HOXB-AS3, YEATS2-AS1, LINC01679, PRRT3-AS1) were extracted after COX regression analysis for classifying patients into high and low-risk groups by different OS rates. As suggested by ROC analysis, our proposed model showed high sensitivity and specificity. Independent prognostic capability of the model from other clinicopathological factors was indicated through further analysis. Based on functional enrichment, those action sites for prognostic lncRNAs were mostly located in the extracellular matrix and cell membrane, and their functions are mainly associated with the adhesion, activation and transport of the components across the extracellular matrix or cell membrane.

Conclusion

Our current study successfully identifies a novel candidate, which can provide more convincing evidence for prognosis in addition to the traditional clinicopathological indicators to predict the PCa survival, and laying the foundation for offering potentially novel therapeutic treatment. Additionally, this study sheds more lights on the PCa-related molecular mechanisms.

Screening and Identification of an Immune-Associated lncRNA Prognostic Signature in Ovarian Carcinoma: Evidence from Bioinformatic Analysis

Backgrounds

The dysregulated long noncoding RNAs (lncRNAs) have been described to be crucial regulators in the progression of ovarian carcinoma. The infiltration status of immune cells is also related to the clinical outcomes in ovarian carcinoma. The present research is aimed at constructing an immune-associated lncRNA signature with potential prognostic value for ovarian carcinoma patients.

Methods

We obtained 379 ovarian carcinoma cases with available clinical data and transcriptome data from The Cancer Genome Atlas database to evaluate the infiltration status of immune cells, thereby generating high and low immune cell infiltration groups. According to the expression of the immune-associated lncRNA signature, the risk score of each case was calculated. The high- and low-risk groups were classified using the median risk score as threshold.

Results

A total of 169 immune-associated lncRNAs that differentially expressed in ovarian carcinoma were included. According to the Lasso regression analysis and Cox univariate and multivariate analyses, 5 immune-associated lncRNAs, including AC134312.1, AL133467.1, CHRM3-AS2, LINC01722, and LINC02207, were identified as a predictive signature with significant prognostic value in ovarian carcinoma. The following Kaplan-Meier analysis, ROC analysis, and Cox univariate and multivariate analyses further suggested that the predicted signature may be an independent prognosticator for patients with ovarian carcinoma. The following gene set enrichment analysis showed that this 5 immune-associated lncRNAs signature was significantly related to the hedgehog pathway, basal cell carcinoma, Wnt signaling pathway, cytokine receptor interaction, antigen processing and presentation, and T cell receptor pathway.

Conclusion

: This study suggested a predictive model with 5 immune-associated lncRNAs that has an independent prognostic value for ovarian carcinoma patients.

Small nucleolar RNA host gene 8: A rising star in the targets for cancer therapy

Long non-coding RNAs (lncRNAs) are a group of transcripts that have been considered essential participants in cancer pathogenesis and progression over the past few decades. Small nucleolar RNA host gene 8 (SNHG8) is a newly discovered lncRNA that belongs to the SNHG family, a group of transcripts that can be processed into small nucleolar RNAs and exert important biological functions. As an oncogenic factor, SNHG8 is upregulated in multiple cancer types. Herein, we summarize the biological role of SNHG8 in different cancer types and the underlying mechanisms related to the interaction between SNHG8 and microRNAs, mRNAs, and proteins. In addition, this study emphasizes the clinical value of SNHG8 in cancer, hoping to provide new insights into cancer diagnosis, prognosis, and treatment.

Analysis of the EGFR Amplification and CDKN2A Deletion Regulated Transcriptomic Signatures Reveals the Prognostic Significance of SPATS2L in Patients With Glioma

Purpose: This study was conducted in order to analyze the prognostic effects of epidermal growth factor receptor (EGFR) and CDKN2A alterations and determine the prognostic significance of EGFR and CDKN2A alterations on regulated genes in patients with glioblastoma (GBM) or lower grade glioma (LGG).

Methods: The alteration frequencies of EGFR and CDKN2A across 32 tumor types were derived from cBioPortal based on The Cancer Genome Atlas (TCGA) datasets. The Kaplan–Meier analysis was used to determine the prognostic significance of EGFR and CDKN2A alterations. EGFR and CDKN2A alterations on regulated expression signatures were identified from RNA-seq data in the TCGA GBM datasets. The prognostic significance of EGFR and CDKN2A alterations on regulated genes in patients with glioma was determined using the TCGA and the Chinese Glioma Genome Atlas (CGGA) datasets.

Results: Compared with the other 31 tumor types, EGFR amplification and CDKN2A deletion particularly occurred in patients with GBM. GBM patients with EGFR amplification or CDKN2A deletion demonstrated poor prognosis. Statistical analysis showed the coexistence of EGFR alteration and CDKN2A deletion in GBM patients. We identified 864 genes which were commonly regulated by EGFR amplification and CDKN2A deletion, and those genes were highly expressed in brain tissues and associated with the cell cycle, EBRR2, and MAPK signaling pathways. Spermatogenesis-associated serine-rich 2-like gene (SPATS2L) was upregulated in GBM patients with EGFR amplification or CDKN2A alteration. Higher expression levels of SPATS2L were associated with worse prognosis in patients with GBM in both TCGA and CGGA datasets. Moreover, the expression levels of SPATS2L were higher in patients with a mesenchymal subtype of GBM. Statistical analysis also showed that the coexistence of EGFR alteration and CDKN2A deletion was significant in patients with LGG. SPATS2L was upregulated in LGG patients with EGFR amplification or CDKN2A alteration. Furthermore, higher expression levels of SPATS2L were associated with worse prognosis in patients with LGG in both TCGA and CGGA datasets. The expression levels of SPATS2L were higher in patients with an astrocytoma subtype of LGG. Finally, the coexistence and unfavorable prognostic effects of EGFR amplification and CDKN2A alteration were validated using the Memorial Sloan Kettering Cancer Center (MSKCC) glioma datasets.

Conclusions: EGFR amplification and CDKN2A deletion of the regulated gene SPATS2L have significant prognostic effects in patients with GBM or LGG.

Immune-related miRNA signature identifies prognosis and immune landscape in head and neck squamous cell carcinomas

Background: Head and neck squamous cell carcinoma (HNSCC) is recognised as an immune active cancer, but little is known about the role of microRNAs (miRNAs) in it. In the present study, we aim to determine a prognostic and immune-related miRNAs signature (IRMS) in HNSCC.

Methods: Spearman correlation analysis was used to screen out prognostic immune-related miRNAs based on single-sample gene set enrichment analysis (ssGSEA). Least absolute shrinkage and selection operator (LASSO) Cox regression model was used to establish IRMS in HNSCC. Then, the influence of the IRMS on HNSCC was comprehensively analysed.

Results: We obtained 11 prognostic immune-related miRNAs based on ssGSEA. Then an IRMS integrated with six miRNAs was established through LASSO Cox regression analysis. The stratification survival analysis indicated that IRMS was independent from other characteristics and performed favourably in the overall survival (OS) prediction. The function annotation suggested that IRMS was highly associated with the immune-related response biological processes and pathways which are so important for tumorigenesis of HNSCC. Moreover, the nomogram demonstrated that our model was identified as an independent prognostic factor. In addition, we found that IRMS was significantly correlated with the immune infiltration and expression of critical immune checkpoints, indicating that the poor prognosis might be caused partly by immunosuppressive microenvironment.

Conclusion: We established a novel IRMS, which exhibited a potent prognostic value and could be representative of immune status in HNSCC.

Integrated analysis of immune-related long noncoding RNAs as diagnostic biomarkers in psoriasis

Background

Psoriasis is a chronic immune-mediated inflammatory dermatosis. Long noncoding RNAs (lncRNAs) play an important role in immune-related diseases. This study aimed to identify potential immune-related lncRNA biomarkers for psoriasis.

Methods

We screened differentially expressed immune-related lncRNAs biomarkers using GSE13355 (skin biopsy samples of 180 cases) from Gene Expression Omnibus (GEO). Moreover, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, and Gene Set Enrichment Analysis (GSEA) were performed to explore biological mechanisms in psoriasis. In addition, we performed LASSO logistic regression to identify potential diagnostic lncRNAs and further verify the diagnostic value and relationship with drug response using two validation sets: GSE30999 (skin biopsy samples of 170 cases) and GSE106992 (skin biopsy samples of 192 cases). Furthermore, we estimated the degree of infiltrated immune cells and investigated the correlation between infiltrated immune cells and diagnostic lncRNA biomarkers.

Results

A total of 394 differentially expressed genes (DEGs) were extracted from gene expression profile. GO and KEGG analysis of target genes found that immune-related lncRNAs were primarily associated with epidermis development, skin development, collagen-containing extracellular matrix, and glycosaminoglycan binding and mainly enriched in cytokine-cytokine receptor interaction and influenza A and chemokine signaling pathway. We found that LINC01137, LINC01215, MAPKAPK5-AS1, TPT1-AS1, CARMN, CCDC18-AS1, EPB41L4A-AS, and LINC01214 exhibited well diagnostic efficacy. The ROC and ROC CI were 0.944 (0.907–0.982), 0.953 (0.919–0.987), 0.822 (0.758–0.887), 0.854 (0.797–0.911), 0.957(0.929–0.985), 0.894 (0.846–0.942), and 0.964 (0.937–0.991) for LINC01137, LINC01215, MAPKAPK5-AS1, TPT1-AS1,CARMN, CCDC18-AS1, EPB41L4A-AS1, and LINC01214. LINC01137, LINC01215, and LINC01214 were correlated with drug response. LINC01137, CCDC18-AS1, and CARMN were positively correlated with activated memory CD4 T cell, activated myeloid dendritic cell (DC), neutrophils, macrophage M1, and T follicular helper (Tfh) cells, while negatively correlated with T regulatory cell (Treg). LINC01215, MAPKAPK5-AS1, TPT1-AS1, EPB41L4A-AS, and LINC01214 were negatively correlated with activated memory CD4 T cell, activated myeloid DC, neutrophils, macrophage M1, and Tfh, while positively correlated with Treg.

Conclusions

These findings indicated that these immune-related lncRNAs may be used as potential diagnostic and predictive biomarkers for psoriasis.

Immunogenomic Profiling Demonstrate AC003092.1 as an Immune-Related eRNA in Glioblastoma Multiforme

Enhancer RNAs, a type of long non-coding RNAs (lncRNAs), play a critical role in the occurrence and development of glioma. RNA-seq data from 161 glioblastoma multiforme (GBM) samples were acquired from The Cancer Genome Atlas database. Then, 70 eRNAs were identified as prognosis-related genes, which had significant relations with overall survival (log-rank test, p < 0.05). AC003092.1 was demonstrated as an immune-related eRNA by functional enrichment analysis. We divided samples into two groups based on AC003092.1 expression: AC003092.1 High (AC003092.1_H) and AC003092.1 Low (AC003092.1_L) and systematically analyzed the influence of AC003092.1 on the immune microenvironment by single-sample gene-set enrichment analysis and CIBERSORTx. We quantified AC003092.1 and TFPI2 levels in 11 high-grade gliomas, 5 low-grade gliomas, and 7 GBM cell lines. Our study indicates that AC003092.1 is related to glioma-immunosuppressive microenvironment, and these results offer innovative sights into GBM immune therapy.