• Cuproptosis
• Immunotherapy
• Lung adenocarcinoma
• Prognostic model
• lncRNA

• No. 2023A04J0622 Guangzhou Municipal Science and Technology Project
• No. 202201010053 Guangzhou Municipal Science and Technology Project
• No. 2020A1515011290 the Guangdong Province Natural Science Foundation

• ALOX15
• AQP4-AS1
• ferroptosis
• lung adenocarcinoma
• miRNA-4476

• Disulfidptosis
• Drug prediction
• Immunotherapy
• Lung adenocarcinoma
• Prognosis
• lncRNA signature

• Humans
• Prognosis
• RNA, Long Noncoding/genetics
• Immunotherapy
• Adenocarcinoma
• Lung
• Elastin
• Recombinant Fusion Proteins
• Silk

• Lung adenocarcinoma
• Prognosis
• Pyroptosis
• Tumor immune microenvironment
• lncRNA

• Humans
• Pyroptosis/genetics
• RNA, Long Noncoding/genetics
• Biomarkers
• Cell Death
• Adenocarcinoma/genetics

• Chemotherapy
• Genetic signature associated with genomic instability
• Immune characteristic
• Lung adenocarcinoma
• Tumor immune microenvironment

• Humans
• Carcinoma, Non-Small-Cell Lung
• Lung Neoplasms/drug therapy
• Lung Neoplasms/genetics
• Adenocarcinoma of Lung/drug therapy
• Adenocarcinoma of Lung/genetics
• B-Lymphocytes
• Genomic Instability
• Prognosis
• Tumor Microenvironment/genetics

• 82172663, 82104208, 81872066, 81773131, and 81972635 National Natural Science Foundation of China
• 2021HSC-CIP011 Innovative Program of Development Foundation of Hefei Centre for Physical Science and Technology
• YZJJ2023QN50, and YZJJ2022QN49 CASHIPS Director's Fund
• CASHIPS Director’s Fund

• APE1
• Chemoresistance
• Genomic Instability
• Homologous Recombination
• Tumorigenesis

• Male
• Animals
• Mice
• Humans
• Drug Resistance, Neoplasm/genetics
• In Situ Hybridization, Fluorescence
• Cell Line, Tumor
• Adenocarcinoma/drug therapy
• Adenocarcinoma/genetics
• Adenocarcinoma/metabolism
• Carcinogenesis/genetics
• Cell Transformation, Neoplastic/genetics
• Homologous Recombination
• Cell Cycle
• Genomic Instability
• Genomics
• Chromosomal Instability/genetics
• Deoxyribonucleases/genetics
• Evolution, Molecular

• I01 BX001584 BLRD VA
• P01 CA155258 NCI NIH HHS
• P50 CA100707 NCI NIH HHS

• NSCLC
• biomarkers
• drug resistance
• lncRNA
• therapeutic targets

• Humans
• Carcinoma, Non-Small-Cell Lung/drug therapy
• Carcinoma, Non-Small-Cell Lung/genetics
• Carcinoma, Non-Small-Cell Lung/pathology
• RNA, Long Noncoding/genetics
• Lung Neoplasms/drug therapy
• Lung Neoplasms/genetics
• Lung Neoplasms/pathology
• Gene Expression Regulation, Neoplastic
• Biomarkers, Tumor/genetics

• ScRNA-seq
• anoikis
• cancer prognosis
• immune microenvironment
• lung adenocarcinoma

• Natural Science Foundation of Shanghai

• cyclin-dependent kinase inhibitor 2A
• lung cancer
• machine learning
• random forest
• somatic mutation

• cellular senescence
• immunotherapy
• lncRNA
• lung adenocarcinoma
• prognosis
• tumor microenvironment

• the Medical Research Fund Project of Guangdong Province
• Medical and Health Science and Technology Project of Shantou City

Background: Non–small cell lung cancer (NSCLC) is highly malignant with driver somatic mutations and genomic instability. Long non-coding RNAs (lncRNAs) play a vital role in regulating these two aspects. However, the identification of somatic mutation-derived, genomic instability-related lncRNAs (GIRlncRNAs) and their clinical significance in NSCLC remains largely unexplored.

Methods: Clinical information, gene mutation, and lncRNA expression data were extracted from TCGA database. GIRlncRNAs were screened by a mutator hypothesis-derived computational frame. Co-expression, GO, and KEGG enrichment analyses were performed to investigate the biological functions. Cox and LASSO regression analyses were performed to create a prognostic risk model based on the GIRlncRNA signature (GIRlncSig). The prediction efficiency of the model was evaluated by using correlation analyses with mutation, driver gene, immune microenvironment contexture, and therapeutic response. The prognostic performance of the model was evaluated by external datasets. A nomogram was established and validated in the testing set and TCGA dataset.

Results: A total of 1446 GIRlncRNAs were selected from the screen, and the established GIRlncSig was used to classify patients into high- and low-risk groups. Enrichment analyses showed that GIRlncRNAs were mainly associated with nucleic acid metabolism and DNA damage repair pathways. Cox analyses further identified 19 GIRlncRNAs to construct a GIRlncSig-based risk score model. According to Cox regression and stratification analyses, 14 risk lncRNAs (AC023824.3, AC013287.1, AP000829.1, LINC01611, AC097451.1, AC025419.1, AC079949.2, LINC01600, AC004862.1, AC021594.1, MYRF-AS1, LINC02434, LINC02412, and LINC00337) and five protective lncRNAs (LINC01067, AC012645.1, AL512604.3, AC008278.2, and AC089998.1) were considered powerful predictors. Analyses of the model showed that these GIRlncRNAs were correlated with somatic mutation pattern, immune microenvironment infiltration, immunotherapeutic response, drug sensitivity, and survival of NSCLC patients. The GIRlncSig risk score model demonstrated good predictive performance (AUCs of ROC for 10-year survival was 0.69) and prognostic value in different NSCLC datasets. The nomogram comprising GIRlncSig and tumor stage exhibited improved robustness and feasibility for predicting NSCLC prognosis.

Conclusion: The newly identified GIRlncRNAs are powerful biomarkers for clinical outcome and prognosis of NSCLC. Our study highlights that the GIRlncSig-based score model may be a useful tool for risk stratification and management of NSCLC patients, which deserves further evaluation in future prospective studies.

• National Natural Science Foundation of China

Background: Globally, pancreatic adenocarcinoma (PAAD) is a common and highly devastating gastrointestinal malignancy that seriously threatens human health. Pyroptosis refers to an emerging form of programmed cell death that has been discovered in recent years, and studies have demonstrated that long non-coding RNA (lncRNA) may act as a moderator in the pyroptosis process of cancer cells. However, relevant explorations about lncRNAs and pyroptosis are still insufficient in PAAD. Therefore, our research is designed to make a comprehensive analysis of the potential values of pyroptosis-related lncRNAs in PAAD.

Methods: By integrating the RNA-sequencing, somatic mutation, and copy number variation (CNV) datasets, as well as the clinicopathological features, we established and validated a risk signature based on pyroptosis-related lncRNAs, and comprehensively analyzed its clinical significance and the potential connection with the tumor immune microenvironment (TIME).

Consequences: The genetic variation landscape displayed that the somatic mutations were rare while CNV changes were general and mainly concentrated on copy number amplification of these 52 pyroptosis-related genes. Subsequently, a risk signature consisting of 10 lncRNAs (TRAF3IP2-AS1, LINC00519, LINC01133, LINC02251, AC005332.6, AL590787.1, AC090114.2, TRPC7-AS1, MIR223HG, and MIR3142HG) was constructed and patients were divided into different subgroups according to the median risk score; patients with high-risk scores presented worse outcomes compared to those with low-risk scores in the training, testing, and entire cohorts. Furthermore, patients at low-risk scores possessed a higher infiltration abundance of immune cells compared with high-risk patients, which was consistent with the expression levels of lncRNAs between the high/low-risk groups. Drug sensitivity analysis showed that low-risk scores were related to anti-cancer agents like AICAR and Axitinib, whereas high-risk scores were connected with certain drugs such as AUY922. These results demonstrated that our risk signature could be used for prognosis prediction; additionally, it was also related to the TIME that might act as a potential indicator to instruct immunotherapeutic strategies.

Conclusion: This work explored the significance of the risk model constructed by pyroptosis-related lncRNAs in prognosis prediction and its internal link with the immune microenvironment of PAAD. The results are expected to assist in the diagnosis, prognostic assessment, and management of patients with PAAD.

• National Natural Science Foundation of China

Adenosine-to-inosine RNA editing (ATIRE) is characterized as non-mutational epigenetic reprogramming hallmark of cancer, while little is known about its predictive role in cancer survival.

To explore survival-related ATIRE events in lung squamous cell carcinoma (LUSC), ATIRE profile, gene expression data, and corresponding clinical information of LUSC patients were downloaded from the TCGA database. Patients were randomly divided into a training (n = 134) and validation cohort (n = 94). Cox proportional hazards regression followed by least absolute shrinkage and selection operator algorithm were performed to identify survival-related ATIRE sites and to generate ATIRE risk score. Then a nomogram was constructed to predict overall survival (OS) of LUSC patients. The correlation of ATIRE level and host gene expression and ATIREs’ effect on transcriptome expression were analyzed.

Seven ATIRE sites that were TMEM120B chr12:122215052A > I, HMOX2 chr16:4533713A > I, CALCOCO2 chr17:46941503A > I, LONP2 chr16:48388244A > I, ZNF440 chr19:11945758A > I, CLCC1 chr1:109474650A > I, and CHMP3 chr2:86754288A > I were identified to generate the risk score, of which high levers were significantly associated with worse OS and progression-free survival in both the training and validation sets. High risk-score was also associated with advanced T stages and worse clinical stages. The nomogram performed well in predicting OS probability of LUSC. Moreover, the editing of ATIRE sites exerted a significant association with expression of host genes and affected several cancer-related pathways.

This is the first comprehensive study to analyze the role of ATIRE events in predicting LUSC survival. The AITRE-based model might serve as a novel tool for LUSC survival prediction.

The online version contains supplementary material available at 10.1186/s12885-022-09773-0.

• A-to-I RNA editing
• Lung squamous cell carcinoma
• Nomogram
• Overall survival

• Ferroptosis
• LUAD
• TCGA
• lncRNA
• lncRNA PACERR
• survival

LINC01133 is a long intergenic non-coding RNA that regulates malignancy in several cancers, including those of the digestive, female reproductive, respiratory, and urinary system. LINC01133 is an extensively studied lncRNA that is highly conserved, and its relatively stable expression is essential for its robust biological function. Its expression is highly tissue-specific with a distinct subcellular localization. It functions as an oncogene or a tumor suppressor gene in different cancers via multiple mechanisms, such as those that involve competing with endogenous RNA and binding to RNA-binding proteins or DNA. Moreover, the secretion and transportation of LINC01133 by extracellular vesicles in the tumor micro-environment is regulated by other cells in the tumor micro-environment. To date, two mechanisms, an increase in copy number and regulation of transcription elements, have been found to regulate LINC01133 expression. Clinically, LINC01133 is an ideal marker for cancer prognosis and a potential therapeutic target in cancer treatment regimes. In this review, we aimed to summarize the aforementioned information as well as posit future directions for LINC01133 research.

As an important non‐apoptotic cell death method, oncosis has been reported to be closely associated with tumors in recent years. However, few research reported the relationship between oncosis and lung cancer.

In this study, we established an oncosis‐based algorithm comprised of cluster grouping and a risk assessment model to predict the survival outcomes and related tumor immunity of patients with lung adenocarcinomas (LUAD). We selected 11 oncosis‐related lncRNAs associated with the prognosis (CARD8‐AS1, LINC00941, LINC01137, LINC01116, AC010980.2, LINC00324, AL365203.2, AL606489.1, AC004687.1, HLA‐DQB1‐AS1, and AL590226.1) to divide the LUAD patients into different clusters and different risk groups. Compared with patients in clsuter1, patients in cluster2 had a survival advantage and had a relatively more active tumor immunity. Subsequently, we constructed a risk assessment model to distinguish between patients into different risk groups, in which low‐risk patients tend to have a better prognosis. GO enrichment analysis revealed that the risk assessment model was closely related to immune activities. In addition, low‐risk patients tended to have a higher content of immune cells and stromal cells in tumor microenvironment, higher expression of PD‐1, CTLA‐4, HAVCR2, and were more sensitive to immune checkpoint inhibitors (ICIs), including PD‐1/CTLA‐4 inhibitors. The risk score had a significantly positive correlation with tumor mutation burden (TMB). The survival curve of the novel oncosis‐based algorithm suggested that low‐risk patients in cluster2 have the most obvious survival advantage.

The novel oncosis‐based algorithm investigated the prognosis and the related tumor immunity of patients with LUAD, which could provide theoretical support for customized individual treatment for LUAD patients.

• cluster analysis
• lung adenocarcinoma
• oncosis-related lncRNAs
• prognostic signature
• tumor immunity

• chemosensitivity
• genomic instability
• long non-coding RNA
• pancreatic adenocarcinoma
• prognosis
• signature

Genomic instability is one of the representative features of cancer evolution. Recent research has revealed that long noncoding RNAs (lncRNAs) play a critical role in maintaining genomic instability. Our work proposed a gene signature (GILncSig) based on genomic instability-derived lncRNAs to probe the possibility of lncRNA signatures as an index of genomic instability, providing a potential new approach to identify genomic instability-related cancer biomarkers.

Lung adenocarcinoma (LUAD) gene expression data from an RNA-seq FPKM dataset, somatic mutation information and relevant clinical materials were downloaded from The Cancer Genome Atlas (TCGA). A prognostic model consisting of genomic instability-related lncRNAs was constructed, termed GILncSig, to calculate the risk score. We validated GILncSig using data from the Gene Expression Omnibus (GEO) database. In this study, we used R software for data analysis.

Through univariate and multivariate Cox regression analyses, five genomic instability-associated lncRNAs (LINC01671, LINC01116, LINC01214, lncRNA PTCSC3, and LINC02555) were identified. We constructed a lncRNA signature (GILncSig) related to genomic instability. LUAD patients were classified into two risk groups by GILncSig. The results showed that the survival rate of LUAD patients in the low-risk group was higher than that of those in the high-risk group. Then, we verified GILncSig in the GEO database. GILncSig was associated with the genomic mutation rate of LUAD. We also used GILncSig to divide TP53 mutant-type patients and TP53 wild-type patients into two groups and performed prognostic analysis. The results suggested that compared with TP53 mutation status, GILncSig may have better prognostic significance.

By combining the lncRNA expression profiles associated with somatic mutations and the corresponding clinical characteristics of LUAD, a lncRNA signature (GILncSig) related to genomic instability was established.

• GILncSig
• TCGA
• TP53
• genome instability
• lung adenocarcinoma (LUAD)

• gene signature
• immune checkpoint
• lactate metabolism
• lncRNA
• lung adenocarcinoma

• Adenocarcinoma/genetics
• Biomarkers, Tumor/genetics
• Biomarkers, Tumor/metabolism
• CD8-Positive T-Lymphocytes
• Humans
• Lactic Acid
• Lung/metabolism
• Prognosis
• RNA, Long Noncoding/genetics

• Postdoctoral Research Foundation of China

• Endometrial cancer
• Genomic instability
• Prognosis
• The cancer genome atlas

Background: Lower-grade gliomas (LGGs) are a heterogeneous set of gliomas. One of the primary sources of glioma heterogeneity is genomic instability, a novel characteristic of cancer. It has been reported that long noncoding RNAs (lncRNAs) play an essential role in regulating genomic stability. However, the potential relationship between genomic instability and lncRNA in LGGs and its prognostic value is unclear.

Methods: In this study, the LGG samples from The Cancer Genome Atlas (TCGA) were divided into two clusters by integrating the lncRNA expression profile and somatic mutation data using hierarchical clustering. Then, with the differentially expressed lncRNAs between these two clusters, we identified genomic instability-related lncRNAs (GInLncRNAs) in the LGG samples and analyzed their potential function and pathway by co-expression network. Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were conducted to establish a GInLncRNA prognostic signature (GInLncSig), which was assessed by internal and external verification, correlation analysis with somatic mutation, independent prognostic analysis, clinical stratification analysis, and model comparisons. We also established a nomogram to predict the prognosis more accurately. Finally, we performed multi-omics-based analyses to explore the relationship between risk scores and multi-omics data, including immune characteristics, N⁶-methyladenosine (m⁶A), stemness index, drug sensitivity, and gene set enrichment analysis (GSEA).

Results: We identified 52 GInLncRNAs and screened five from them to construct the GInLncSig model (CRNDE, AC025171.5, AL390755.1, AL049749.1, and TGFB2-AS1), which could independently and accurately predict the outcome of patients with LGG. The GInLncSig model was significantly associated with somatic mutation and outperformed other published signatures. GSEA revealed that metabolic pathways, immune pathways, and cancer pathways were enriched in the high-risk group. Multi-omics-based analyses revealed that T-cell functions, m⁶A statuses, and stemness characteristics were significantly disparate between two risk subgroups, and immune checkpoints such as PD-L1, PDCD1LG2, and HAVCR2 were significantly highly expressed in the high-risk group. The expression of GInLncSig prognostic genes dramatically correlated with the sensitivity of tumor cells to chemotherapy drugs.

Conclusion: A novel signature composed of five GInLncRNAs can be utilized to predict prognosis and impact the immune status, m⁶A status, and stemness characteristics in LGG. Furthermore, these lncRNAs may be potential and alternative therapeutic targets.

• genome instability
• long noncoding RNA
• lower-grade glioma
• multi-omics analysis
• signature

• biomarkers
• clinical prognosis
• copy number variation
• non-coding RNAs
• precision medicine

• LUAD
• ceRNA network
• ferroptosis
• lncRNAs
• prognostic signature

• genome instability-related lncRNAs (GInLncRNAs)
• genomic instability
• lncRNA
• prognostic risk model
• renal cell carcinoma

Osteosarcoma (OSA) is characterized by its relatively high morbidity in children and adolescents. Patients usually have advanced disease at the time of diagnosis, resulting in poor outcomes. This study focused on building a circular RNA-based ceRNA network to develop a reliable model for OSA risk prediction.

We used the Gene Expression Omnibus (GEO) datasets to explore the expression patterns of circRNA, miRNA, and mRNA in OSA. The prognostic value of circRNA host genes was assessed with data from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) database using Kaplan–Meier survival analysis. We established a circRNA-related ceRNA network and annotated its biological functions. Next, we developed a prognostic risk signature based on mRNAs extracted from the ceRNA network. We also developed a prognostic model and constructed a nomogram to enhance the prediction of OSA prognosis.

We identified 166 DEcircRNAs, 233 DEmiRNAs, and 1317 DEmRNAs and used them to create a circRNA-related ceRNA network. We then established a prognostic risk model consisting of four genes (MLLT11, TNFRSF11B, SLC7A7, and PARVA). Moreover, we found that inhibition of MLLT11 and SLC7A7 blocked OSA cell proliferation and migration in in vitro experiments.

Our study identifies crucial prognostic genes and provides a circRNA-related ceRNA network for OSA, which will contribute to the elucidation of the molecular mechanisms underlying the oncogenesis and development of OSA.

• TARGET
• ceRNA network
• nomogram
• osteosarcoma
• prognosis

Genomic instability (GI) is among the top ten characteristics of malignancy. Long non-coding RNAs (lncRNAs) are promising cancer biomarkers that are reportedly involved in GI. So far, the clinical value of GI-related lncRNAs (GIlncs) in papillary thyroid cancer (PTC) has not been clarified.

Integrative analysis of lncRNA expression and somatic mutation profiles was performed to identify GIlncs. Analysis of differentially expressed lncRNAs in the group with high- and low- cumulative number of somatic mutations revealed significant GIlncs in PTC. Univariate and multivariate Cox proportional hazard regression analyses were performed to identify hub-GIlncs.

A computational model based on four lncRNAs (FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1) was identified as a quantitative index using an in-silicon discovery cohort. GILS score was significantly associated with poor prognosis, as validated in the TCGA dataset and further tested in our local RNA-Seq cohort. Moreover, a combination of clinical characteristics and the composite GILS-clinical prognostic nomogram demonstrates satisfactory discrimination and calibration. Furthermore, the GILS score and FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 were also associated with driver mutations and multiple clinical-pathological variables, respectively. Moreover, RNA-Seq confirmed the expression patterns of FOXD2-AS1, LINC01614, AC073257.2, and AC005082.1 in PTC and normal thyroid tissues. Biological experiments demonstrated that downregulated or overexpressed LINC01614 affect PTC cell proliferation, migration, and invasion in vitro. Activation of the stromal and immune cell infiltration was also observed in the high LINC01614 group in the PTC microenvironment.

In summary, we identified a signature for clinical outcome prediction in PTC comprising four lncRNAs associated with GI. A better understanding of the GI providing an alternative evaluation of the progression risk of PTC. Our study also demonstrated LINC01614 as a novel oncogenic lncRNA and verified its phenotype in PTC.

• genomic instability (GI)
• long non-coding RNAs
• mutator phenotype
• papillary thyroid carcinoma
• prognosis

Melanoma is a common tumor characterized by a high mortality rate in its late stage. After metastasis, current treatment methods are relatively ineffective. Many studies have shown that long noncoding RNA (lncRNA) may participate in gene mutation and genomic instability in cancer.

We downloaded transcriptome data, mutation data, and clinical follow-up data of melanoma patients from The Cancer Genome Atlas. We divided samples into groups according to the number of somatic cell mutations and then performed a differential analysis to screen out the differentially expressed genes. We then divided samples into genomic unstable and genomic stable groups. We compared lncRNA expression profiles in these groups and constructed a protein-coding genes network coexpressed with selected lncRNA to analyze the pathways enriched by these genes. Two machine learning methods, least absolute shrinkage and selector operation (LASSO) and support vector machine-recursive feature elimination (SVM-RFE), were applied to conduct the lncRNA-related prognostic model. Afterward, we performed survival analysis, risk correlation analysis, independent prognostic analysis, and clinical subgroup model validation. Finally, through wound healing assay and transwell assay, the function of AATBC was verified by A375 cell lines.

We screened 61 prognostic-related lncRNAs and constructed an lncRNA-mRNA coexpression network based on these lncRNAs. Seven lncRNAs were selected as common characteristic factors based on the two machine learning methods. The model formula was as follows: risk score = 0.085∗AATBC + 0.190∗ AC026689.1−0.117∗AC083799.1 + 0.036∗ AC091544.6−0.039∗ LINC01287−0.291∗ SPRY4.AS1 + 0.056∗ ZNF667.AS1. The seven lncRNAs in this formula are key candidates. Cell experiments have verified that knocking down AATBC in A375 cell lines can reduce the proliferation and invasion ability of melanoma cells.

The lncRNA we identified provides a new way to study lncRNA's role in the genomic instability of melanoma. Our findings may provide essential candidate biomarkers for the diagnosis and treatment of melanoma.