A Six-lncRNA Signature for Immunophenotype Prediction of Glioblastoma Multiforme

A novel immune-related long noncoding RNA (lncRNA) pair model to predict the prognosis of triple-negative breast cancer

Background

Breast cancer (BC) is the most prevalent cancer type and is the principal cause of cancer-related death in women. Anti-programmed cell death protein 1/programmed cell death ligand 1 (PD-1/PD-L1) immunotherapy has shown promising effects in metastatic triple-negative breast cancer (TNBC), but the potential factors affecting its efficacy have not been elucidated. Immune-related long noncoding RNAs (irlncRNAs) have been reported to be involved in immune escape to influence the carcinogenic process through the PD-1/PD-L1 signaling pathway. Therefore, exploring the potential regulatory mechanism of irlncRNAs in PD-1/PD-L1 immunotherapy in TNBC is of great importance.

Methods

We retrieved transcriptome profiling data from The Cancer Genome Atlas (TCGA) and identified differentially expressed irlncRNA (DEirlncRNA) pairs. Least absolute shrinkage and selection operator (LASSO) regression analysis was performed to construct a risk assessment model.

Results

Receiver operating characteristic (ROC) curve analysis indicated that the risk model may serve as a potential prediction tool in TNBC patients. Clinical stage and risk score were proved to be independent prognostic predictors by univariate and multivariate Cox regression analyses. Subsequently, we investigated the correlation between the risk model and tumor-infiltrating immune cells and immune checkpoints. Finally, we identified USP30-AS1 through the StarBase and Multi Experiment Matrix (MEM) databases, predicted the potential target genes of USP30-AS1, and then discovered that these target genes were closely associated with immune responses.

Conclusions

Our study constructed a risk assessment model by irlncRNA pairs regardless of expression levels, which contributed to predicting the efficacy of immunotherapy in TNBC. Furthermore, the lncRNA USP30-AS1 in the model was positively correlated with the expression of PD-L1 and provided a potential therapeutic target for TNBC.

Noncoding RNA Profile in Reovirus Treated KRAS-Mutated Colorectal Cancer Patients

PURPOSE

To investigate the alterations in the expression of noncoding, micro, and small RNA expression during treatment with oncolytic reovirus in KRAS-mutated colorectal cancer.

METHODS

Oncolytic reovirus treatment was administered in phase 1 clinical trial (NCT01274624) for 5 days every 28 days, and blood samples were collected before the administration of the reovirus and 48 h, 8 days, and 15 days after its administration on day 1. Data from the blood samples were sorted using Transcriptome Analysis Software (TAC) 4.0, where a two-tailed t-test and a fold change filter were used to ascertain which sample signals had a statistically significant relative fold change of greater than 2 at multiple timepoints before or after oncolytic reovirus administration.

RESULTS

The long noncoding RNA's RP11-332M2.1 (-6.1 x), LINC01506 (-16.18 x), and LINC00534 (-1.94 x) were downregulated at 48 h after reovirus administration [p < 0.05]. ncRNA's EPB41L4A-AS1 (-6.34 x, 48 h; 11.99 x, day 8), JAK2 (2.2 x, 48 h; -2.23 x, day 8), ANXA4 (20.47 x, day 8; -7.54 x, day 15), and PCDH9 (-2.09, day 8; 1.82 x, day 15) were affected by the reovirus treatment and reflected the progress of the treatment [p < 0.05]. The small RNA SNORA26 (-1.59 x, day 8) was downregulated 48 h after the reovirus administration [p < 0.05]. The microRNA MIR-4461 (6.18 x, day 8; -3.76 x, day 15) was also affected by the reovirus administration [p < 0.05].

CONCLUSION

The administration of oncolytic reovirus to treat KRAS-mutated colorectal cancer is reflected in a noncoding RNA profile, and expression levels of the ncRNAs in that profile may thus be able to be used as a potential predictive marker for reovirus-treated colorectal cancer.

New algorithms based on autophagy-related lncRNAs pairs to predict the prognosis of skin cutaneous melanoma patients

Skin cutaneous melanoma (SKCM) is the most malignant skin tumor for it is enormously easy to develop invasion and metastasis. Autophagy is a process by which cellular material is degraded by lysosomes or vacuoles and recycled. Autophagy-related long non-coding RNAs (lncRNAs) have been thought to correlate with SKCM. This study aims to explore the prognostic significance of autophagy-related lncRNAs and establish a prognostic model of autophagy-related lncRNA pairs in SKCM. Firstly, the RNA-seq data and related clinical information were downloaded from the TCGA database. 446 qualified samples were enrolled. 222 autophagy-related genes were obtained from the HADb database. Pearson correlation analysis was conducted to identify autophagy-related lncRNAs (ARLs). After that, we obtained prognosis-related ARLs and autophagy-related lncRNA pairs (ARLPs). Using Lasso-Cox regression analysis, an autophagy-related lncRNA-pair prognostic signature was established. The accuracy of the signature were confirmed through a series of validations in terms of mutation profiles, immunity infiltration, and cellular pathways. And we used the random forest method to find USP30-AS1 as a key mediating factor in SKCM.

T2DB: A Web Database for Long Non-Coding RNA Genes in Type II Diabetes

Type II diabetes (T2D) is a growing health problem worldwide due to increased levels of obesity and can lead to other life-threatening diseases, such as cardiovascular and kidney diseases. As the number of individuals diagnosed with T2D rises, there is an urgent need to understand the pathogenesis of the disease in order to prevent further harm to the body caused by elevated blood glucose levels. Recent advances in long non-coding RNA (lncRNA) research may provide insights into the pathogenesis of T2D. Although lncRNAs can be readily detected in RNA sequencing (RNA-seq) data, most published datasets of T2D patients compared to healthy donors focus only on protein-coding genes, leaving lncRNAs to be undiscovered and understudied. To address this knowledge gap, we performed a secondary analysis of published RNA-seq data of T2D patients and of patients with related health complications to systematically analyze the expression changes of lncRNA genes in relation to the protein-coding genes. Since immune cells play important roles in T2D, we conducted loss-of-function experiments to provide functional data on the T2D-related lncRNA USP30-AS1, using an in vitro model of pro-inflammatory macrophage activation. To facilitate lncRNA research in T2D, we developed a web application, T2DB, to provide a one-stop-shop for expression profiling of protein-coding and lncRNA genes in T2D patients compared to healthy donors or subjects without T2D.

A Novel Aging-Related Prognostic lncRNA Signature Correlated with Immune Cell Infiltration and Response to Immunotherapy in Breast Cancer

Breast cancer (BC) is among the most universal malignant tumors in women worldwide. Aging is a complex phenomenon, caused by a variety of factors, that plays a significant role in tumor development. Consequently, it is crucial to screen for prognostic aging-related long non-coding RNAs (lncRNAs) in BC. The BC samples from the breast-invasive carcinoma cohort were downloaded from The Cancer Genome Atlas (TCGA) database. The differential expression of aging-related lncRNAs (DEarlncRNAs) was screened by Pearson correlation analysis. Univariate Cox regression, LASSO-Cox analysis, and multivariate Cox analysis were performed to construct an aging-related lncRNA signature. The signature was validated in the GSE20685 dataset from the Gene Expression Omnibus (GEO) database. Subsequently, a nomogram was constructed to predict survival in BC patients. The accuracy of prediction performance was assessed through the time-dependent receiver operating characteristic (ROC) curves, Kaplan-Meier analysis, principal component analyses, decision curve analysis, calibration curve, and concordance index. Finally, differences in tumor mutational burden, tumor-infiltrating immune cells, and patients' response to chemotherapy and immunotherapy between the high- and low-risk score groups were explored. Analysis of the TCGA cohort revealed a six aging-related lncRNA signature consisting of MCF2L-AS1, USP30-AS1, OTUD6B-AS1, MAPT-AS1, PRR34-AS1, and DLGAP1-AS1. The time-dependent ROC curve proved the optimal predictability for prognosis in BC patients with areas under curves (AUCs) of 0.753, 0.772, and 0.722 in 1, 3, and 5 years, respectively. Patients in the low-risk group had better overall survival and significantly lower total tumor mutational burden. Meanwhile, the high-risk group had a lower proportion of tumor-killing immune cells. The low-risk group could benefit more from immunotherapy and some chemotherapeutics than the high-risk group. The aging-related lncRNA signature can provide new perspectives and methods for early BC diagnosis and therapeutic targets, especially tumor immunotherapy.

A prognostic risk prediction model based on ferroptosis-related long non-coding RNAs in bladder cancer: A bulk RNA-seq research and scRNA-seq validation

BACKGROUND

To construct a prognostic risk model of bladder cancer (BC) from the perspective of long non-coding RNAs (lncRNAs) and ferroptosis, in order to guide clinical prognosis and identify potential therapeutic targets.

METHODS

In-hours BC samples were collected from 4 patients diagnosed with BC, who underwent radical cystectomy. Single cell transcriptome sequencing was performed and Seurat package were used for quality control and secondary analysis. LncRNAs expression profiles of BC samples were extracted from The Cancer Genome Atlas database. And sex, age, tumor, node, metastasis stage and other clinical data was downloaded at the same time. Ferroptosis-related lncRNAs were identified by co-expression analysis. We constructed a risk model by Cox regression and least absolute shrinkage and selection operator regression analyses. The predictive strength of the risk model for overall survival (OS) of patients with BC was evaluated by the log-rank test and Kaplan-Meier method. Finally, the enrichment analysis was performed and visualized.

RESULTS

We identified and included 15 prognostic ferroptosis-related lncRNAs (AL356740.1, FOXC2AS1, ZNF528AS1, LINC02535, PSMB8AS1, AL590428.1, AP000347.2, OCIAD1-AS1, AP001347.1, AC104986.2, AC018926.2, LINC00867, AC099518.4, USP30-AS1, and ARHGAP5-AS1), to build our ferroptosis-related lncRNAs risk model. Using this risk model, BC patients were divided into high and low-risk groups, and their respective survival lengths were calculated. The results showed that the OS of the low-risk group was significantly longer than that of the high-risk group. A nomogram was utilized to predict the survival rate of BC patients. As indicated in the nomogram, risk score was the most important indicator of OS in patients with BC. The ferroptosis-related lncRNAs risk model is an independent tool for prognostic risk assessment in patients with BC. Single cell transcriptome sequencing suggests that ferroptosis-related lncRNAs express specifically in BC tumor microenvironment. AL356740.1, LINC02535 and LINC00867 were mainly expressed in tumor cells.

CONCLUSION

The risk model based on the ferroptosis-related lncRNAs and the genomic clinico-pathological nomogram could be used to accurately predict the prognosis of patients with BC. The lncRNAs used to build this model might become potential therapeutic targets in the future.

Systematic characterization and biological functions of non-coding RNAs in glioblastoma

Glioblastoma multiforme (GBM) is the most malignant and aggressive type of glioma. Non-coding RNAs (ncRNAs) are RNAs that do not encode proteins but widely exist in eukaryotic cells. The common characteristics of these RNAs are that they can all be transcribed from the genome without being translated into proteins, thus performing biological functions, particularly microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs. Studies have found that ncRNAs are associated with the occurrence and development of GBM, and there is a complex regulatory network among ncRNAs, which can regulate cell proliferation, migration, apoptosis and differentiation, thus provide a basis for the development of highly specific diagnostic tools and therapeutic strategies in the future. The present review aimed to comprehensively describe the biogenesis, general features and functions of regulatory ncRNAs in GBM, and to interpret the potential biological functions of these ncRNAs in GBM as well as their impact on clinical diagnosis, treatment and prognosis and discusses the potential mechanisms of these RNA subtypes leading to cancer in order to contribute to the better design of personalized GBM therapies in the future.

Identification of biomarkers for hepatocellular carcinoma based on single cell sequencing and machine learning algorithms

Hepatocellular carcinoma (HCC) remains one of the most lethal cancers around the world. Precision oncology will be crucial for further improving the prognosis of HCC patients. Compared with traditional bulk RNA-seq, single-cell RNA sequencing (scRNA-seq) enables the transcriptomes of a great deal of individual cells assayed in an unbiased manner, showing the potential to deeply reveal tumor heterogeneity. In this study, based on the scRNA-seq results of primary neoplastic cells and paired normal liver cells from eight HCC patients, a new strategy of machine learning algorithms was applied to screen core biomarkers that distinguished HCC tumor tissues from the adjacent normal liver. Expression profiles of HCC cells and normal liver cells were first analyzed by maximum relevance minimum redundancy (mRMR) to get a top 50 signature gene feature. For further analysis, the incremental feature selection (IFS) method and leave-one-out cross validation (LOOCV) were conducted to build an optimal classification model and to extract 21 potentially essential biomarkers for HCC cells. Our results provided new insights into HCC pathogenesis that might be valuable for HCC diagnosis and therapy.

Establishment of a Cell Necroptosis Index to Predict Prognosis and Drug Sensitivity for Patients With Triple-Negative Breast Cancer

Background: Necroptosis has been an alternatively identified mechanism of programmed cancer cell death, which plays a significant role in cancer. However, research about necroptosis-related long noncoding RNAs (lncRNAs) in cancer are still few. Moreover, the potentially prognostic value of necroptosis-related lncRNAs and their correlation with the immune microenvironment remains unclear. The present study aimed to explore the potential prognostic value of necroptosis-related lncRNAs and their relationship to immune microenvironment in triple-negative breast cancer (TNBC).

Methods: The RNA expression matrix of patients with TNBC was obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases. Finally, 107 patients of GSE58812, 159 patients of TCGA, and 143 patients of GSE96058 were included. Necroptosis-related lncRNAs were screened by Cox regression and Pearson correlation analysis with necroptosis-related genes. By LASSO regression analysis, nine necroptosis-related lncRNAs were employed, and a cell necroptosis index (CNI) was established; then, we evaluated its prognostic value, clinical significance, pathways, immune infiltration, and chemotherapeutics efficacy.

Results: Based on the CNI value, the TNBC patients were divided into high- and low-CNI groups, and the patients with high CNI had worse prognosis, more lymph node metastasis, and larger tumor (p < 0.05). The receiver operating characteristic (ROC) analysis showed that the signature performed well. The result of the infiltration proportion of different immune cell infiltration further explained that TNBC patients with high CNI had low immunogenicity, leading to poor therapeutic outcomes. Moreover, we found significant differences of the IC50 values of various chemotherapeutic drugs in the two CNI groups, which might provide a reference to make a personalized chemotherapy for them.

Conclusion: The novel prognostic marker CNI could not only precisely predict the survival probability of patients with TNBC but also demonstrate a potential role in antitumor immunity and drug sensitivity.

lncRNA USP30-AS1 sponges miR-765 and modulates the progression of colon cancer

Background

The incidence and mortality of colon cancer is increasing recently. It is necessary to identify effective biomarkers for the progression and prognosis of colon cancer. To assess the potential of lncRNA USP30-AS1 (USP30-AS1) in serving as the biomarker of colon cancer and unearth the underlying mechanism.

Methods

There were 123 colon cancer patients enrolled. The expression of USP30-AS1 was evaluated with PCR in tissue and cell samples. The clinical significance of USP30-AS1 was assessed with a series of statistical methods, while the CCK8 and Transwell assay were conducted to estimate its biological effect on the colon cancer cellular processes. In mechanism, the interaction of USP30-AS1 with miR-765 was evaluated with the dual-luciferase reporter assay.

Results

In colon cancer tissues, the USP30-AS1 downregulation and the miR-765 upregulation were observed, and there was a negative correlation between the USP30-AS1 expression level and the miR-765 expression level. The downregulation of USP30-AS1 related to the malignant progression and served as an adverse prognostic indicator of colon cancer. The overexpression of USP30-AS1 dramatically suppressed colon cancer cellular processes, which was alleviated by miR-765.

Conclusions

USP30-AS1 predicts the malignancy and prognosis of colon cancer patients. USP30-AS1 suppressed the progression of colon cancer through modulating miR-765.

A prognostic model based on immune-related long noncoding RNAs for patients with epithelial ovarian cancer

BACKGROUND

Long noncoding RNAs (lncRNAs) are important regulators of gene expression and can affect a variety of physiological processes. Recent studies have shown that immune-related lncRNAs play an important role in the tumour immune microenvironment and may have potential application value in the treatment and prognosis prediction of tumour patients. Epithelial ovarian cancer (EOC) is characterized by a high incidence and poor prognosis. However, there are few studies on immune-related lncRNAs in EOC. In this study, we focused on immune-related lncRNAs associated with survival in EOC.

METHODS

We downloaded mRNA data for EOC patients from The Cancer Genome Atlas (TCGA) database and mRNA data for normal ovarian tissue from the Genotype-Tissue Expression (GTEx) database and identified differentially expressed genes through differential expression analysis. Immune-related lncRNAs were obtained through intersection and coexpression analysis of differential genes and immune-related genes from the Immunology Database and Analysis Portal (ImmPort). Samples in the TCGA EOC cohort were randomly divided into a training set, validation set and combination set. In the training set, Cox regression analysis and LASSO regression were performed to construct an immune-related lncRNA signature. Kaplan-Meier survival analysis, time-dependent ROC curve analysis, Cox regression analysis and principal component analysis were performed for verification in the training set, validation set and combination set. Further studies of pathways and immune cell infiltration were conducted through Gene Set Enrichment Analysis (GSEA) and the Timer data portal.

RESULTS

An immune-related lncRNA signature was identified in EOC, which was composed of six immune-related lncRNAs (KRT7-AS, USP30-AS1, AC011445.1, AP005205.2, DNM3OS and AC027348.1). The signature was used to divide patients into high-risk and low-risk groups. The overall survival of the high-risk group was lower than that of the low-risk group and was verified to be robust in both the validation set and the combination set. The signature was confirmed to be an independent prognostic biomarker. Principal component analysis showed the different distribution patterns of high-risk and low-risk groups. This signature may be related to immune cell infiltration (mainly macrophages) and differential expression of immune checkpoint-related molecules (PD-1, PDL1, etc.).

CONCLUSIONS

We identified and established a prognostic signature of immune-related lncRNAs in EOC, which will be of great value in predicting the prognosis of clinical patients and may provide a new perspective for immunological research and individualized treatment in EOC.

The Establishment and Experimental Verification of an lncRNA-Derived CD8+ T Cell Infiltration ceRNA Network in Colorectal Cancer

BACKGROUND

Long noncoding RNAs (LncRNA) lead a vital role in colorectal cancer (CRC) development. The infiltrating CD8+ T cell is the main target of immunotherapy. Our study aimed to figure out the potential mechanism of lncRNAs regulating the function of CD8+ T cells in CRC.

METHODS

We collected bulk RNA-seq, miRNA-seq, and single-cell RNA-seq (scRNA-seq) data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. The cibersort algorithm and correlation analysis were used to estimate the abundance of CD8+ T cells and screened out the most relevant lncRNAs. We used scRNA-seq data to identify the main cell lncRNA expressed. Furthermore, one competing endogenous RNA (ceRNA) network focusing on the potential mechanism of lncRNA-derived CD8+ T cell infiltration was constructed. We established a co-culture system to assess the immunosuppressive function of the lncRNA. And we evaluated the effects of the lncRNA on CD8+ T cell cytotoxicity by flow cytometry, qPCR, and clone formation assay.

RESULTS

Three CD8+ T cell infiltration-related lncRNAs were identified, and LINC00657 was expressed mainly in tumor cells, negatively associated with CD8+ T cell infiltration. Hsa-miRNA-1224-3p and hsa-miRNA-338-5p and SCD, ETS2, UBE2H, and YY1 were identified to construct the ceRNA network. Immunosuppression-related tumor marker CD155 was proved to be positively correlated with LINC00657 and mRNAs in the ceRNA network. In addition, we proved that LINC00657 could impair the cytotoxicity of CD8+ T cells, and its expression was positively associated with CD155 in vitro.

CONCLUSIONS

We successfully constructed an lncRNA-derived CD8+ T cell infiltration ceRNA network in CRC. LINC00657 may play a leading role in the CRC immune escape and could be a novel immunotherapy target.

Unraveling Immune-Related lncRNAs in Breast Cancer Molecular Subtypes

Breast cancer (BRCA) is the most leading cause of cancer worldwide. It is a heterogeneous disease with at least five molecular subtypes including luminal A, luminal B, basal-like, HER2-enriched, and normal-like. These five molecular subtypes are usually stratified according to their mRNA profile patterns; however, ncRNAs are increasingly being used for this purpose. Among the ncRNAs class, the long non-coding RNAs (lncRNAs) are molecules with more than 200 nucleotides with versatile regulatory roles; and high tissue-specific expression profiles. The heterogeneity of BRCA can also be reflected regarding tumor microenvironment immune cells composition, which can directly impact a patient's prognosis and therapy response. Using BRCA immunogenomics data from a previous study, we propose here a bioinformatics approach to include lncRNAs complexity in BRCA molecular and immune subtype. RNA-seq data from The Cancer Genome Atlas (TCGA) BRCA cohort was analyzed, and signal-to-noise ratio metrics were applied to create these subtype-specific signatures. Five immune-related signatures were generated with approximately ten specific lncRNAs, which were then functionally analyzed using GSEA enrichment and survival analysis. We highlighted here some lncRNAs in each subtype. LINC01871 is related to immune response activation and favorable overall survival in basal-like samples; EBLN3P is related to immune response suppression and progression in luminal B, MEG3, XXYLT1-AS2, and LINC02613 were related with immune response activation in luminal A, HER2-enriched and normal-like subtypes, respectively. In this way, we emphasize the need to know better the role of lncRNAs as regulators of immune response to provide new perspectives regarding diagnosis, prognosis and therapeutical targets in BRCA molecular subtypes.