A Radioresponse-Related lncRNA Biomarker Signature for Risk Classification and Prognosis Prediction in Non-Small-Cell Lung Cancer

Comprehensive multimodal and multiomic profiling reveals epigenetic and transcriptional reprogramming in lung tumors

Epigenomic mechanisms are critically involved in mediation of genetic and environmental factors that underlie cancer development. Histone modifications represent highly informative epigenomic marks that reveal activation and repression of gene activities and dysregulation of transcriptional control due to tumorigenesis. Here, we present a comprehensive epigenomic and transcriptomic mapping of 18 tumor and 20 non-neoplastic tissues from non-small cell lung adenocarcinoma patients. Our profiling covers 5 histone marks including activating (H3K4me3, H3K4me1, and H3K27ac) and repressive (H3K27me3 and H3K9me3) marks and the transcriptome using only 20 mg of tissue per sample, enabled by low-input omic technologies. Using advanced integrative bioinformatic analysis, we uncovered cancer-driving signaling cascade networks, changes in 3D genome modularity, and differential expression and functionalities of transcription factors and noncoding RNAs. Many of these identified genes and regulatory molecules showed no significant change in their expression or a single epigenomic modality, emphasizing the power of integrative multimodal and multiomic analysis using patient samples.

dbCRAF: a curated knowledgebase for regulation of radiation response in human cancer

Radiation therapy (RT) is one of the primary treatment modalities of cancer, with 40-60% of cancer patients benefiting from RT during their treatment course. The intrinsic radiosensitivity or acquired radioresistance of tumor cells would affect the response to RT and clinical outcomes in patients. Thus, mining the regulatory mechanisms in tumor radiosensitivity or radioresistance that have been verified by biological experiments and computational analysis methods will enhance the overall understanding of RT. Here, we describe a comprehensive database dbCRAF (http://dbCRAF.xialab.info/) to document and annotate the factors (1,677 genes, 49 proteins and 612 radiosensitizers) linked with radiation response, including radiosensitivity, radioresistance in cancer cells and prognosis in cancer patients receiving RT. On the one hand, dbCRAF enables researchers to directly access knowledge for regulation of radiation response in human cancer buried in the vast literature. On the other hand, dbCRAF provides four flexible modules to analyze and visualize the functional relationship between these factors and clinical outcome, KEGG pathway and target genes. In conclusion, dbCRAF serves as a valuable resource for elucidating the regulatory mechanisms of radiation response in human cancers as well as for the improvement of RT options.

CASC19: An Oncogenic Long Non-coding RNA in Different Cancers

A 324 bp lncRNA called CASC19 is found on chromosome 8q24.21. Recent research works have revealed that CASC19 is involved in the prognosis of tumors and related to the regulation of the radiation tolerance mechanisms during tumor radiotherapy (RT). This review sheds light on the changes and roles that CASC19 plays in many tumors and diseases, such as nasopharyngeal carcinoma (NPC), cervical cancer, colorectal cancer (CRC), non-small cell lung cancer (NSCLC), clear cell renal cell carcinoma (ccRCC), gastric cancer (GC), pancreatic cancer (PC), hepatocellular carcinoma (HCC), glioma, and osteoarthritis (OA). CASC19 provides a new strategy for targeted therapy, and the regulatory networks of CASC19 expression levels play a key role in the occurrence and development of tumors and diseases. In addition, the expression level of CASC19 has predictive roles in the prognosis of some tumors and diseases, which has major implications for clinical diagnoses and treatments. CASC19 is also unique in that it is a key gene affecting the efficacy of RT in many tumors, and its expression level plays a decisive role in improving the success rate of treatments. Further research is required to determine the precise process by which CASC19 causes changes in diseased cells in some tumors and diseases.

Long non-coding RNAs in non-small cell lung cancer: implications for preventing therapeutic resistance

Lung cancer has the highest mortality and morbidity rates among all cancers worldwide. Despite many complex treatment options, including radiotherapy, chemotherapy, targeted drugs, immunotherapy, and combinations of these treatments, efficacy is low in cases of resistance to therapy, metastasis, and advanced disease, contributing to low overall survival. There is a pressing need for the discovery of novel biomarkers and therapeutic targets for the early diagnosis of lung cancer and to determine the efficacy and outcomes of drug treatments. There is now substantial evidence for the diagnostic and prognostic value of long noncoding RNAs (lncRNAs). This review briefly discusses recent findings on the roles and mechanisms of action of lncRNAs in the responses to therapy in non-small cell lung cancer.

Role of MAGI2-AS3 in malignant and non-malignant disorders

MAGI2 Antisense RNA 3 (MAGI2-AS3) is a long non-coding RNA (lncRNA) transcribed from a locus on 7q21.11. This lncRNA has been described to be abnormally expressed in a variety of malignancies in correlation with many clinical characteristics. Moreover, it might participate in the pathogenesis of congenital diaphragmatic hernia, Alzheimer's disease and intervertebral disc degeneration. Mechanistically, MAGI2-AS3 can serve as a molecular sponge for miR-142-3p, miR-424-5p, miR-15b, miR-233, miR-452-5p, miR-629-5p, miR-25, miR-155, miR-23a-3p, miR-519c-3p, miR-374b-5p, miR-374a, miR-31-5p, miR-3163, miR-525-5p, miR-15-5p, miR-374a-5p, miR-374b-5p, miR-218-5p, miR-141-3p and miR-200a-3p to regulate expression of their mRNA targets. The current review summarizes the role of MAGI2-AS3 in different disorders to highlight its importance in their pathophysiology.

Systematic analysis of lncRNA gene characteristics based on PD-1 immune related pathway for the prediction of non-small cell lung cancer prognosis

BACKGROUND

Non-small cell lung cancer (NSCLC) is heterogeneous. Molecular subtyping based on the gene expression profiles is an effective technique for diagnosing and determining the prognosis of NSCLC patients.

METHODS

Here, we downloaded the NSCLC expression profiles from The Cancer Genome Atlas and the Gene Expression Omnibus databases. ConsensusClusterPlus was used to derive the molecular subtypes based on long-chain noncoding RNA (lncRNA) associated with the PD-1-related pathway. The LIMMA package and least absolute shrinkage and selection operator (LASSO)-Cox analysis were used to construct the prognostic risk model. The nomogram was constructed to predict the clinical outcomes, followed by decision curve analysis (DCA) to validate the reliability of this nomogram.

RESULTS

We discovered that PD-1 was strongly and positively linked to the T-cell receptor signaling pathway. Furthermore, we identified two NSCLC molecular subtypes yielding a significantly distinctive prognosis. Subsequently, we developed and validated the 13-lncRNA-based prognostic risk model in the four datasets with high AUC values. Patients with low-risk showed a better survival rate and were more sensitive to PD-1 treatment. Nomogram construction combined with DCA revealed that the risk score model could accurately predict the prognosis of NSCLC patients.

CONCLUSIONS

This study demonstrated that lncRNAs engaged in the T-cell receptor signaling pathway played a significant role in the onset and development of NSCLC, and that they could influence the sensitivity to PD-1 treatment. In addition, the 13 lncRNA model was effective in assisting clinical treatment decision-making and prognosis evaluation.

Overexpression of CASC19 contributes to tumor progression and predicts poor prognosis after radical resection in hepatocellular carcinoma

BACKGROUND

Long non-coding RNAs (lncRNAs) have been implicated as functional molecules in hepatocellular carcinoma (HCC) progression. The present research aimed to investigate the levels of LncRNA cancer susceptibility candidate gene 19 (CASC19) in HCC tissues and cell lines and to explore its potential role in the diagnosis and prognosis of HCC.

METHODS

HCC tissues and cell lines were collected to assess the levels of CASC19 by real-time quantitative reverse transcription PCR (RT-qPCR). The prognostic value of CASC19 was evaluated using the Kaplan-Meier method and Cox regression analysis. The functional role of CASC19 in regulating HCC cell proliferation, migration, and invasion was evaluated by Cell Counting Kit-8 (CCK-8) and Transwell analysis. The potential targeted miR-140-5p of CASC19 was confirmed by a dual-luciferase reporter assay.

RESULTS

High CASC19 expression positively correlated with tumor size, differentiation, and TNM stage in HCC patients (P < 0.05). Patients with high CASC19 expression have a poorer survival prognosis and are prone to relapse compared to those with low CASC19. miR-140-5p, a target miRNA for CASC19, negatively correlated with CASC19 levels in tumor tissues. Reduced CASC19 levels attenuated cell proliferation, migration, and invasion, but this attenuation was reversed by suppression of miR-140-5p.

CONCLUSION

Up-regulated CASC19 may serve as a biomarker for predicting poor prognosis in HCC patients. In vitro, overexpressed CASC19 promoted the progression of HCC, indicating that CASC19 may be a possible therapeutic target for the treatment of HCC.

Integrated single-cell and transcriptome sequencing analyses develops a metastasis-based risk score system for prognosis and immunotherapy response in uveal melanoma

Background: Uveal melanoma (UM) is the most frequent ocular neoplasm with a strong metastatic ability. The prognostic value of metastasis-associated genes (MAGs) of UM remains unclear. It is urgent to develop a prognostic score system according to the MAGs of UM. Methods: Unsupervised clustering was used to identify MAGs-based molecular subtypes. Cox methods were utilized to generate a prognostic score system. The prognostic ability of the score system was detected by plotting ROC and survival curves. The immune activity and underlying function were depicted by CIBERSORT GSEA algorithms. Results: Gene cluster analysis determined two MAGs-based subclusters in UM, which were remarkably different in clinical outcomes. A risk score system containing six MAGs (COL11A1, AREG, TIMP3, ADAM12, PRRX1 and GAS1) was set up. We employed ssGSEA to compare immune activity and immunocyte infiltration between the two risk groups. Notch, JAK/STAT and mTOR pathways were greatly enriched in the high-risk group. Furthermore, we observed that knockdown of AREG could inhibit UM proliferation and metastasis by in vitro assays. Conclusion: The MAGs-based subtype and score system in UM can enhance prognosis assessment, and the core system provides valuable reference for clinical decision-making.

A Connexin-Based Biomarker Model Applicable for Prognosis and Immune Landscape Assessment in Lung Adenocarcinoma

Purpose

Gap junction protein (Connexin) family is the basic unit of cellular connection, whose multiple members were recently demonstrated to be associated with tumor progression. However, the expression pattern and prognostic value of connexin in lung adenocarcinoma (LUAD) have not yet been elucidated.

Methods

Consensus cluster algorithm was first applied to determine a novel molecular subtype in LUAD based on connexin genes. The differentially expressed genes (DEGs) between two clusters were obtained to include in Cox regression analyses for the model construction. To examine the predictive capacity of the signature, survival curves and ROC plots were conducted. We implemented GSEA method to uncover the function effects enriched in the risk model. Moreover, the tumor immune microenvironment in LUAD was depicted by CIBERSORT and ssGSEA methods.

Results

The integrated LUAD cohort (TCGA-LUAD and GSE68465) were clustered into two subtypes (C1 = 217 and C2 = 296) based on 21 connexins and the clinical outcomes of LUAD cases in the two clusters showed remarkable discrepancy. Next, we collected 222 DEGs among two subclusters to build a prognostic model using stepwise Cox analyses. Our proposed model consisted of six genes that accurately forecast patient outcomes and differentiate patient risk. GSEA indicated that high-risk group was involved in tumor relevant pathways were activated in high-risk group, such as PI3K/AKT signaling, TGF-β pathway, and p53 pathway. Furthermore, LUAD cases with high-risk presented higher infiltration level of M2 macrophage and neutrophil, suggesting high-risk group were more likely to generate an immunosuppressive status.

Conclusion

Our data identified a novel connexin-based subcluster in LUAD and further created a risk signature which plays a central part in prognosis assessment and clinical potency.

LincRNAs and snoRNAs in Breast Cancer Cell Metastasis: The Unknown Players

Recent advances in research have led to earlier diagnosis and targeted therapies against breast cancer, which has resulted in reduced breast cancer-related mortality. However, the majority of breast cancer-related deaths are due to metastasis of cancer cells to other organs, a process that has not been fully elucidated. Among the factors and genes implicated in the metastatic process regulation, non-coding RNAs have emerged as crucial players. This review focuses on the role of long intergenic noncoding RNAs (lincRNAs) and small nucleolar RNAs (snoRNAs) in breast cancer cell metastasis. LincRNAs are transcribed between two protein-coding genes and are longer than 200 nucleotides, they do not code for a specific protein but function as regulatory molecules in processes such as cell proliferation, apoptosis, epithelial-to-mesenchymal transition, migration, and invasion while most of them are highly elevated in breast cancer tissues and seem to function as competing endogenous RNAs (ceRNAs) inhibiting relevant miRNAs that specifically target vital metastasis-related genes. Similarly, snoRNAs are 60-300 nucleotides long and are found in the nucleolus being responsible for the post-transcriptional modification of ribosomal and spliceosomal RNAs. Most snoRNAs are hosted inside intron sequences of protein-coding and non-protein-coding genes, and they also regulate metastasis-related genes affecting related cellular properties.

Biological Mechanisms to Reduce Radioresistance and Increase the Efficacy of Radiotherapy: State of the Art

Cancer treatment with ionizing radiation (IR) is a well-established and effective clinical method to fight different types of tumors and is a palliative treatment to cure metastatic stages. Approximately half of all cancer patients undergo radiotherapy (RT) according to clinical protocols that employ two types of ionizing radiation: sparsely IR (i.e., X-rays) and densely IR (i.e., protons). Most cancer cells irradiated with therapeutic doses exhibit radio-induced cytotoxicity in terms of cell proliferation arrest and cell death by apoptosis. Nevertheless, despite the more tailored advances in RT protocols in the last few years, several tumors show a relatively high percentage of RT failure and tumor relapse due to their radioresistance. To counteract this extremely complex phenomenon and improve clinical protocols, several factors associated with radioresistance, of both a molecular and cellular nature, must be considered. Tumor genetics/epigenetics, tumor microenvironment, tumor metabolism, and the presence of non-malignant cells (i.e., fibroblast-associated cancer cells, macrophage-associated cancer cells, tumor-infiltrating lymphocytes, endothelial cells, cancer stem cells) are the main factors important in determining the tumor response to IR. Here, we attempt to provide an overview of how such factors can be taken advantage of in clinical strategies targeting radioresistant tumors.

Exploration of shared gene signature with development of pre-eclampsia and cervical cancer

Background: The relationship between pre-eclampsia (PE) and cervical cancer (CC) has drawn more attention recently, while little is known about the shared pathogenesis of CC and PE. In the present research, we aimed to generate the shared gene network as well as the prognostic model to reveal the development of CC and PE.

Methods: The transcription data of CC and PE patients were obtained and enrolled into weighted gene co-expression network (WGCNA) analysis. Disease-specific modules in CC and PE were determined to discover the shared genes. The expression patterns of genes at protein level were examined by HPA database. Further, LASSO penalty regression and Cox analysis were applied to create a prognostic signature based on the shared genes, with survival curves and ROC plots employed to confirm the predictive capacity. To uncover the function roles and pathways involved in signature, gene set enrichment analysis (GSEA) was conducted. Finally, the immune infiltration status in CC was depicted using CIBERSORT algorithms.

Results: WGCNA determined three hub modules between CC and PE. A total of 117 shared genes were obtained for CC and PE and mainly enriched in cell proliferation, regulation of cell development and neuron differentiation. Then, we created a robust prognostic model based on the 10 shared genes by performing stepwise Cox analyses. Our proposed model presented a favorable ability in prognosis forecast and was correlated with the infiltration of immunocytes including B cells, macrophages and T cells. GSEA disclosed that high-risk group was involved in cancer-related pathways.

Conclusion: The present project identified the shared genes to uncover the pathogenesis of CC and PE and further proposed and validated a prognostic signature to accurately forecast the clinical outcomes of CC patients.

Long non-coding RNAs and exosomal lncRNAs: Potential functions in lung cancer progression, drug resistance and tumor microenvironment remodeling

Among the different kinds of tumors threatening human life, lung cancer is one that is commonly observed in both males and females. The aggressive behavior of lung cancer and interactions occurring in tumor microenvironment enhances the malignancy of this tumor. The lung tumor cells have demonstrated capacity in developing chemo- and radio-resistance. LncRNAs are a category of non-coding RNAs that do not encode proteins, but their aberrant expression is responsible for tumor development, especially lung cancer. In the present review, we focus on both lncRNAs and exosomal lncRNAs in lung cancer, and their ability in regulating proliferation and metastasis. Cell cycle progression and molecular mechanisms related to lung cancer metastasis such as EMT and MMPs are regulated by lncRNAs. LncRNAs interact with miRNAs, STAT, Wnt, EZH2, PTEN and PI3K/Akt signaling pathways to affect progression of lung cancer cells. LncRNAs demonstrate both tumor-suppressor and tumor-promoting functions in lung cancer. They can be considered as biomarkers in lung cancer and especially exosomal lncRNAs present in body fluids are potential tools for minimally invasive diagnosis. Furthermore, we discuss regulation of lncRNAs by anti-cancer drugs and genetic tools as well as the role of these factors in therapy response of lung cancer cells.

The Mechanism of Long Non-coding RNA in Cancer Radioresistance/Radiosensitivity: A Systematic Review

Background and purpose: Radioresistance remains a significant challenge in tumor therapy. This systematic review aims to demonstrate the role of long non-coding RNA (lncRNA) in cancer radioresistance/radiosensitivity.

Material and methods: The electronic databases Pubmed, Embase, and Google Scholar were searched from January 2000 to December 2021 to identify studies addressing the mechanisms of lncRNAs in tumor radioresistance/sensitivity, each of which required both in vivo and in vitro experiments.

Results: Among the 87 studies identified, lncRNAs were implicated in tumor radioresistance/sensitivity mainly in three paradigms. 1) lncRNAs act on microRNA (miRNA) by means of a sponge, and their downstream signals include some specific molecular biological processes (DNA repair and chromosome stabilization, mRNA or protein stabilization, cell cycle and proliferation, apoptosis-related pathways, autophagy-related pathways, epithelial-mesenchymal transition (EMT), cellular energy metabolism) and some signaling mediators (transcription factors, kinases, some important signal transduction pathways) that regulate various biological processes. 2) lncRNAs directly interact with proteins, affecting the cell cycle and autophagy to contribute to tumor radioresistance. 3) lncRNAs act like transcription factors to initiate downstream signaling pathways and participate in tumor radioresistance.

Conclusion: lncRNAs are important regulators involved in tumor radioresistance\sensitivity. Different lncRNAs may participate in the radioresistance with the same regulatory paradigm, and the same lncRNAs may also participate in the radioresistance in different ways. Future research should focus more on comprehensively characterizing the mechanisms of lncRNAs in tumor radioresistance to help us identify corresponding novel biomarkers and develop new lncRNA-based methods to improve radioresistance.

Comprehensive Analysis of Subtypes and Identification of Key lncRNAs Based on Glutamine Metabolism-Related Long Noncoding RNAs

Background

Long noncoding RNAs (lncRNAs) are becoming a critical class of metabolic regulate molecule in cancer. Glutamine is a regulator that contributes to each of the core metabolic tasks in proliferating tumor cells. Thus, we aimed to evaluate the association of lncRNAs with glutamine metabolism in lung adenocarcinoma (LUAD).

Methods

Using single-sample gene set enrichment analysis (ssGSEA), LUAD specimens were assigned scores based on glutamine metabolism-related genes, and the shared common glutamine metabolism-related lncRNAs in three different LUAD data cohorts were identified. ConsensusClusterPlus was used to perform unsupervised clustering analysis in patients with LUAD. Key glutamine metabolism-related lncRNAs were identified by first-order partial correlation analysis.

Results

A total of 11 shared glutamine metabolism-associated lncRNAs were identified in three LUAD data cohorts, and LUAD patients were classified into three glutamine metabolism subtypes based on the expressions of the related genes. C1 exhibited shorter overall survival (OS), poor genomic instability, and inadequate infiltration of immune cell types in the tumor microenvironment (TME) and was representative of the immunodeficiency phenotype. C2 represented the immunosuppressive phenotype while C3 represented the immune activation phenotype, exhibiting the highest sensitivity to immunotherapy. Nine of the 11 lncRNAs were localized to the nucleus. Finally, three key lncRNAs, significantly enriched in multiple metabolic pathways, were screened and found to be remarkably related to the OS of LUAD.

Conclusion

We identified three glutamine metabolism subtypes of LUAD, which reflected different OS, genomic, and TME features, and identified three key glutamine metabolism-associated lncRNAs may contribute to further study of lncRNAs in cancer metabolism.