The landscape of immune checkpoint-related long non-coding RNAs core regulatory circuitry reveals implications for immunoregulation and immunotherapy responses.

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) by cooperating with immunity genes in tumor immunization. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here we present an integrated framework that leverages network-based analyses and Bayesian network inference to identify the regulated relationships including lncRNA, ICP and immunity genes as ICP-related LncRNAs mediated Core Regulatory Circuitry Triplets (ICP-LncCRCTs) that can make robust predictions. Hub ICP-related lncRNAs such as MIR155HG and ADAMTS9-AS2 were highlighted to play central roles in immune regulation. Specific ICP-related lncRNAs could distinguish cancer subtypes. Moreover, the ICP-related lncRNAs are likely to significantly correlated with immune cell infiltration, MHC, CYT. Some ICP-LncCRCTs such as CXCL10-MIR155HG-ICOS could better predict one-, three- and five-year prognosis compared to single molecule in melanoma. We also validated that some ICP-LncCRCTs could effectively predict ICI-response using three kinds of machine learning algorithms follow five independent datasets. Specially, combining ICP-LncCRCTs with the tumor mutation burden (TMB) improves the prediction of ICI-treated melanoma patients. Altogether, this study will improve our grasp of lncRNA functions and accelerating discovery of lncRNA-based biomarkers in ICI treatment.

DNA methylation heterogeneity attributable to a complex tumor immune microenvironment prompts prognostic risk in glioma.

Gliomas are malignant tumours of the human nervous system with different World Health Organization (WHO) classifications, glioblastoma (GBM) with higher grade and are more malignant than lower-grade glioma (LGG). To dissect how the DNA methylation heterogeneity in gliomas is influenced by the complex cellular composition of the tumour immune microenvironment, we first compared the DNA methylation profiles of purified human immune cells and bulk glioma tissue, stratifying three tumour immune microenvironmental subtypes for GBM and LGG samples from The Cancer Genome Atlas (TCGA). We found that more intermediate methylation sites were enriched in glioma tumour tissues, and used the Proportion of sites with Intermediate Methylation (PIM) to compare intertumoral DNA methylation heterogeneity. A larger PIM score reflected stronger DNA methylation heterogeneity. Enhanced DNA methylation heterogeneity was associated with stronger immune cell infiltration, better survival rates, and slower tumour progression in glioma patients. We then created a Cell-type-associated DNA Methylation Heterogeneity Contribution (CMHC) score to explore the impact of different immune cell types on heterogeneous CpG site (CpGct) in glioma tissues. We identified eight prognosis-related CpGct to construct a risk score: the Cell-type-associated DNA Methylation Heterogeneity Risk (CMHR) score. CMHR was positively correlated with cytotoxic T-lymphocyte infiltration (CTL), and showed better predictive performance for IDH status (AUC = 0.96) and glioma histological phenotype (AUC = 0.81). Furthermore, DNA methylation alterations of eight CpGct might be related to drug treatments of gliomas. In conclusion, we indicated that DNA methylation heterogeneity is associated with a complex tumour immune microenvironment, glioma phenotype, and patient's prognosis.

A comprehensive database of exosome molecular biomarkers and disease-gene associations.

Exosomes play a crucial role in intercellular communication and can be used as biomarkers for diagnostic and therapeutic clinical applications. However, systematic studies in cancer-associated exosomal nucleic acids remain a big challenge. Here, we developed ExMdb, a comprehensive database of exosomal nucleic acid biomarkers and disease-gene associations curated from published literature and high-throughput datasets. We performed a comprehensive curation of exosome properties including 4,586 experimentally supported gene-disease associations, 13,768 diagnostic and therapeutic biomarkers, and 312,049 nucleic acid subcellular locations. To characterize expression variation of exosomal molecules and identify causal factors of complex diseases, we have also collected 164 high-throughput datasets, including bulk and single-cell RNA sequencing (scRNA-seq) data. Based on these datasets, we performed various bioinformatics and statistical analyses to support our conclusions and advance our knowledge of exosome biology. Collectively, our dataset will serve as an essential resource for investigating the regulatory mechanisms of complex diseases and improving the development of diagnostic and therapeutic biomarkers.

Identification of somatic mutation-driven enhancers and their clinical utility in breast cancer.

Somatic mutations contribute to cancer development by altering the activity of enhancers. In the study, a total of 135 mutation-driven enhancers, which displayed significant chromatin accessibility changes, were identified as candidate risk factors for breast cancer (BRCA). Furthermore, we identified four mutation-driven enhancers as independent prognostic factors for BRCA subtypes. In Her2 subtype, enhancer G > C mutation was associated with poorer prognosis through influencing its potential target genes FBXW9, TRIR, and WDR83. We identified aminoglutethimide and quinpirole as candidate drugs targeting the mutated enhancer. In normal subtype, enhancer G > A mutation was associated with poorer prognosis through influencing its target genes ALOX15B, LINC00324, and MPDU1. We identified eight candidate drugs such as erastin, colforsin, and STOCK1N-35874 targeting the mutated enhancer. Our findings suggest that somatic mutations contribute to breast cancer subtype progression by altering enhancer activity, which could be potential candidates for cancer therapy.

Identification of cuproptosis-related diagnostic biomarkers in idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a progressive and fatal lung disease with clinical and pathological heterogeneity. Recent studies have identified cuproptosis as a novel cell death mechanism. However, the role of cuproptosis-related genes in the pathogenesis of IPF is still unclear. Two IPF datasets of the Gene Expression Omnibus database were studied. Mann-Whitney U test, correlation analysis, functional enrichment analyses, single-sample gene set enrichment analysis, CIBERSORT, unsupervised clustering, weighted gene co-expression network analysis, and receiver operating characteristic curve analysis were used to conduct our research. The dysregulated cuproptosis-related genes and immune responses were identified between IPF patients and controls. Two cuproptosis-related molecular clusters were established in IPF, the high immune score group (C1) and the low immune score group (C2). Significant heterogeneity in immunity between clusters was revealed by functional analyses results. The module genes with the strongest correlation to the 2 clusters were identified by weighted gene co-expression network analysis results. Seven hub genes were found using the Cytoscape software. Ultimately, 2 validated diagnostic biomarkers of IPF, CDKN2A and NEDD4, were obtained. Subsequently, the results were validated in GSE47460. Our investigation illustrates that CDKN2A and NEDD4 may be valid biomarkers that were useful for IPF diagnosis and copper-related clustering.

Metabolic-Related Gene Prognostic Index for Predicting Prognosis, Immunotherapy Response, and Candidate Drugs in Ovarian Cancer.

Ovarian cancer (OC) is a highly heterogeneous disease, with patients at different tumor staging having different survival times. Metabolic reprogramming is one of the key hallmarks of cancer; however, the significance of metabolism-related genes in the prognosis and therapy outcomes of OC is unclear. In this study, we used weighted gene coexpression network analysis and differential expression analysis to screen for metabolism-related genes associated with tumor staging. We constructed the metabolism-related gene prognostic index (MRGPI), which demonstrated a stable prognostic value across multiple clinical trial end points and multiple validation cohorts. The MRGPI population had its distinct molecular features, mutational characteristics, and immune phenotypes. In addition, we investigated the response to immunotherapy in MRGPI subgroups and found that patients with low MRGPI were prone to benefit from anti-PD-1 checkpoint blockade therapy and exhibited a delayed treatment effect. Meanwhile, we identified four candidate therapeutic drugs (ABT-737, crizotinib, panobinostat, and regorafenib) for patients with high MRGPI, and we evaluated the pharmacokinetics and safety of the candidate drugs. In summary, the MRGPI was a robust clinical feature that could predict patient prognosis, immunotherapy response, and candidate drugs, facilitating clinical decision making and therapeutic strategy of OC.

Proteomic characterization of epithelial ovarian cancer delineates molecular signatures and therapeutic targets in distinct histological subtypes.

Clear cell carcinoma (CCC), endometrioid carcinoma (EC), and serous carcinoma (SC) are the major histological subtypes of epithelial ovarian cancer (EOC), whose differences in carcinogenesis are still unclear. Here, we undertake comprehensive proteomic profiling of 80 CCC, 79 EC, 80 SC, and 30 control samples. Our analysis reveals the prognostic or diagnostic value of dysregulated proteins and phosphorylation sites in important pathways. Moreover, protein co-expression network not only provides comprehensive view of biological features of each histological subtype, but also indicates potential prognostic biomarkers and progression landmarks. Notably, EOC have strong inter-tumor heterogeneity, with significantly different clinical characteristics, proteomic patterns and signaling pathway disorders in CCC, EC, and SC. Finally, we infer MPP7 protein as potential therapeutic target for SC, whose biological functions are confirmed in SC cells. Our proteomic cohort provides valuable resources for understanding molecular mechanisms and developing treatment strategies of distinct histological subtypes.

Tumor biology, immune infiltration and liver function define seven hepatocellular carcinoma subtypes linked to distinct drivers, survival and drug response.

BACKGROUND & AIMS: Tumor heterogeneity is jointly determined by the components of the tumor ecosystem (TES) including tumor cells, immune cells, stromal cells, and non-cellular components. We aimed to identify subtypes using TES-related genes and determine subtype specific drivers and treatments for hepatocellular carcinoma (HCC). METHODS: We collected 68 genesets depicting tumor biology, immune infiltration, and liver function, totaling 2831 genes, and collected mRNA profiles and clinical data for over 6000 tumors from 65 datasets in the GEO, TCGA, ICGC, and several other databases. We designed a three-step clustering pipeline to identify subtypes. The microenvironment, genomic alteration, and drug response differences were systematically compared among subtypes. RESULTS: Seven subtypes (TES-1/2/3/4/5/6/7) were revealed in 159 tumors from the CHCC-HBV cohort. We constructed a single sample classifier using paired genes (sscpgsTES). TES subtypes were significantly associated with multiple clinical variables including etiology, and survival in 14 of 17 cohorts and the meta-cohort. TES-1 had the poorest prognosis and highest proliferation level. Both TES-2 and TES-7 were immune-enriched, however, TES-2 had a significantly worse prognosis, and hypoxic and immunosuppressive microenvironment. TES-4 had activated Wnt pathway, driven by CTNNB1 mutation. Good prognosis TES-6 exhibited the best differentiation. TES-5 and TES-3 were considered as novel subclasses by comparing with ten previous subtyping systems. TES-5 tumors had high AFP but good overall survival, and ∼45% of them harbored AXIN1 mutation. TES-3 was immune and stromal desert, may be driven by high copy number alteration burden, and had the poorest response to immune checkpoint inhibitor. TES-1 and TES-2 had significantly lower response to transarterial chemoembolization, but they showed significantly higher sensitivity to compound YM-155. CONCLUSIONS: Tumor ecosystem subtypes expand existing HCC subtyping systems, have distinct drivers, prognosis, and treatment vulnerabilities.

Identifying immune checkpoint-related lncRNA biomarkers for immunotherapy response and prognosis in cancers.

Long non-coding RNAs (lncRNAs) could modulate expression of immune checkpoints (ICPs) in tumor-immune. However, precise functions in immunity and potential for predicting ICP inhibitors (ICI) response have been described for only a few lncRNAs. Here, a multiple-step pipeline was developed to identify cancer- and immune-context ICP and lncRNA cooperative regulation pairs (ICPaLncCRPs) across cancers. Immune-related ICPs and lncRNAs were extracted follow immune cell lines and immunologic constant of rejection groups. ICPaLncCRP networks were constructed, which likely to modulate tumor-immune by specific patterns. Common and specific hub ICPaLncs such as MIR155HG, TRG-AS1 and PCED1B-AS1 maybe play central roles in prognosis and circulating. Moreover, these hub ICPaLncs were significantly correlated with immune cell infiltration based on bulk and single-cell RNA sequencing data. Some ICPaLncCRPs such as IDO1-MIR155HG could predict three- and five-year prognosis of melanoma in two independent datasets. We also validated that some ICPaLncCRPs could effectively predict ICI-response follow six independent datasets. Collectively, this study will enhance our understanding of lncRNA functions and accelerate discovery of lncRNA-based biomarkers in ICI treatment.

Reconstructing the immunosenescence core pathway reveals global characteristics in pan-cancer.

Immunosenescence has been demonstrated to play an important role in tumor progression. However, there is lacking comprehensive analyses of immunosenescence-related pathways. Meanwhile, the sex disparities of immunosenescence in cancer are still poorly understood. In this study, we analyzed the multi-omics data of 12,836 tumor samples, including genomics, transcriptomics, epigenomics, proteomics, and metabolomics. We systematically identified immunosenescence pathways that were disordered across cancer types. The mutations and copy number variations of immunosenescence pathways were found to be more active in pan-cancer. We reconstructed the immunosenescence core pathways (ISC-pathways) to improve the ability of prognostic stratification in 33 cancer types. We also found the head and neck squamous carcinoma (HNSC) contained abundant sex-specific immunosenescence features and showed sex differences in survival. We found that OSI-027 was a potential sex-specific drug in HNSC tumors, which tended to be more effective in male HNSC by targeting the MTOR gene in the PI3K-Akt signaling pathway. In conclusion, our study provided a systematic understanding of immunosenescence pathways and revealed the global characteristics of immunosenescence in pan-cancer. We highlighted MTOR gene could be a powerful immunosenescence biomarker of HNSC that helps to develop sex-specific immunosenescence drugs.

CRS: a circadian rhythm score model for predicting prognosis and treatment response in cancer patients.

BACKGROUND: Circadian rhythm regulates complex physiological activities in organisms. A strong link between circadian dysfunction and cancer has been identified. However, the factors of dysregulation and functional significance of circadian rhythm genes in cancer have received little attention. METHODS: In 18 cancer types from The Cancer Genome Atlas (TCGA), the differential expression and genetic variation of 48 circadian rhythm genes (CRGs) were examined. The circadian rhythm score (CRS) model was created using the ssGSEA method, and patients were divided into high and low groups based on the CRS. The Kaplan-Meier curve was created to assess the patient survival rate. Cibersort and estimate methods were used to identify the infiltration characteristics of immune cells between different CRS subgroups. Gene Expression Omnibus (GEO) dataset is used as verification queue and model stability evaluation queue. The CRS model's ability to predict chemotherapy and immunotherapy was assessed. Wilcoxon rank-sum test was used to compare the differences of CRS among different patients. We use CRS to identify potential "clock-drugs" by the connective map method. RESULTS: Transcriptomic and genomic analyses of 48 CRGs revealed that most core clock genes are up-regulated, while clock control genes are down-regulated. Furthermore, we show that copy number variation may affect CRGs aberrations. Based on CRS, patients can be classified into two groups with significant differences in survival and immune cell infiltration. Further studies showed that patients with low CRS were more sensitive to chemotherapy and immunotherapy. Additionally, we identified 10 compounds (e.g. flubendazole, MLN-4924, ingenol) that are positively associated with CRS, and have the potential to modulate circadian rhythms. CONCLUSIONS: CRS can be utilized as a clinical indicator to predict patient prognosis and responsiveness to therapy, and identify potential "clock-drugs".

Pan-cancer transcriptomic analysis identified six classes of immunosenescence genes revealed molecular links between aging, immune system and cancer.

Aging is a complex process that significantly impacts the immune system. The aging-related decline of the immune system, termed immunosenescence, can lead to disease development, including cancer. The perturbation of immunosenescence genes may characterize the associations between cancer and aging. However, the systematical characterization of immunosenescence genes in pan-cancer remains largely unexplored. In this study, we comprehensively investigated the expression of immunosenescence genes and their roles in 26 types of cancer. We developed an integrated computational pipeline to identify and characterize immunosenescence genes in cancer based on the expression profiles of immune genes and clinical information of patients. We identified 2218 immunosenescence genes that were significantly dysregulated in a wide variety of cancers. These immunosenescence genes were divided into six categories based on their relationships with aging. Besides, we assessed the importance of immunosenescence genes in clinical prognosis and identified 1327 genes serving as prognostic markers in cancers. BTN3A1, BTN3A2, CTSD, CYTIP, HIF1AN, and RASGRP1 were associated with ICB immunotherapy response and served as prognostic factors after ICB immunotherapy in melanoma. Collectively, our results furthered the understanding of the relationship between immunosenescence and cancer and provided insights into immunotherapy for patients.

Immune Pathways with Aging Characteristics Improve Immunotherapy Benefits and Drug Prediction in Human Cancer.

(1) Background: Perturbation of immune-related pathways can make substantial contributions to cancer. However, whether and how the aging process affects immune-related pathways during tumorigenesis remains largely unexplored. (2) Methods: Here, we comprehensively investigated the immune-related genes and pathways among 25 cancer types using genomic and transcriptomic data. (3) Results: We identified several pathways that showed aging-related characteristics in various cancers, further validated by conventional aging-related gene sets. Genomic analysis revealed high mutation burdens in cytokines and cytokines receptors pathways, which were strongly correlated with aging in diverse cancers. Moreover, immune-related pathways were found to be favorable prognostic factors in melanoma. Furthermore, the expression level of these pathways had close associations with patient response to immune checkpoint blockade therapy in melanoma and non-small cell lung cancer. Applying a net-work-based method, we predicted immune- and aging-related genes in pan-cancer and utilized these genes for potential immunotherapy drug discovery. Mapping drug target data to our top-ranked genes identified potential drug targets, FYN, JUN, and SRC. (4) Conclusions: Taken together, our systematic study helped interpret the associations among immune-related pathways, aging, and cancer and could serve as a resource for promoting clinical treatment.

Radiogenomic analysis of prediction HER2 status in breast cancer by linking ultrasound radiomic feature module with biological functions.

BACKGROUND: Human epidermal growth factor receptor 2 (HER2) overexpressed associated with poor prognosis in breast cancer and HER2 has been defined as a therapeutic target for breast cancer treatment. We aimed to explore the molecular biological information in ultrasound radiomic features (URFs) of HER2-positive breast cancer using radiogenomic analysis. Moreover, a radiomics model was developed to predict the status of HER2 in breast cancer. METHODS: This retrospective study included 489 patients who were diagnosed with breast cancer. URFs were extracted from a radiomics analysis set using PyRadiomics. The correlations between differential URFs and HER2-related genes were calculated using Pearson correlation analysis. Functional enrichment of the identified URFs-correlated HER2 positive-specific genes was performed. Lastly, the radiomics model was developed based on the URF-module mined from auxiliary differential URFs to assess the HER2 status of breast cancer. RESULTS: Eight differential URFs (p < 0.05) were identified among the 86 URFs extracted by Pyradiomics. 25 genes that were found to be the most closely associated with URFs. Then, the relevant biological functions of each differential URF were obtained through functional enrichment analysis. Among them, Zone Entropy is related to immune cell activity, which regulate the generation of calcification in breast cancer. The radiomics model based on the Logistic classifier and URF-module showed good discriminative ability (AUC = 0.80, 95% CI). CONCLUSION: We searched for the URFs of HER2-positive breast cancer, and explored the underlying genes and biological functions of these URFs. Furthermore, the radiomics model based on the Logistic classifier and URF-module relatively accurately predicted the HER2 status in breast cancer.

LncTarD 2.0: an updated comprehensive database for experimentally-supported functional lncRNA-target regulations in human diseases.

An updated LncTarD 2.0 database provides a comprehensive resource on key lncRNA-target regulations, their influenced functions and lncRNA-mediated regulatory mechanisms in human diseases. LncTarD 2.0 is freely available at (http://bio-bigdata.hrbmu.edu.cn/LncTarD or https://lnctard.bio-database.com/). LncTarD 2.0 was updated with several new features, including (i) an increased number of disease-associated lncRNA entries, where the current release provides 8360 key lncRNA-target regulations, with 419 disease subtypes and 1355 lncRNAs; (ii) predicted 3312 out of 8360 lncRNA-target regulations as potential diagnostic or therapeutic biomarkers in circulating tumor cells (CTCs); (iii) addition of 536 new, experimentally supported lncRNA-target regulations that modulate properties of cancer stem cells; (iv) addition of an experimentally supported clinical application section of 2894 lncRNA-target regulations for potential clinical application. Importantly, LncTarD 2.0 provides RNA-seq/microarray and single-cell web tools for customizable analysis and visualization of lncRNA-target regulations in diseases. RNA-seq/microarray web tool was used to mining lncRNA-target regulations in both disease tissue samples and CTCs blood samples. The single-cell web tools provide single-cell lncRNA-target annotation from the perspectives of pan-cancer analysis and cancer-specific analysis at the single-cell level. LncTarD 2.0 will be a useful resource and mining tool for the investigation of the functions and mechanisms of lncRNA deregulation in human disease.

CellTracer: a comprehensive database to dissect the causative multilevel interplay contributing to cell development trajectories.

During the complex process of tumour development, the unique destiny of cells is driven by the fine-tuning of multilevel features such as gene expression, network regulation and pathway activation. The dynamic formation of the tumour microenvironment influences the therapeutic response and clinical outcome. Thus, characterizing the developmental landscape and identifying driver features at multiple levels will help us understand the pathological development of disease in individual cell populations and further contribute to precision medicine. Here, we describe a database, CellTracer (http://bio-bigdata.hrbmu.edu.cn/CellTracer), which aims to dissect the causative multilevel interplay contributing to cell development trajectories. CellTracer consists of the gene expression profiles of 1 941 552 cells from 222 single-cell datasets and provides the development trajectories of different cell populations exhibiting diverse behaviours. By using CellTracer, users can explore the significant alterations in molecular events and causative multilevel crosstalk among genes, biological contexts, cell characteristics and clinical treatments along distinct cell development trajectories. CellTracer also provides 12 flexible tools to retrieve and analyse gene expression, cell cluster distribution, cell development trajectories, cell-state variations and their relationship under different conditions. Collectively, CellTracer will provide comprehensive insights for investigating the causative multilevel interplay contributing to cell development trajectories and serve as a foundational resource for biomarker discovery and therapeutic exploration within the tumour microenvironment.

RNA2Immune: A Database of Experimentally Supported Data Linking Non-coding RNA Regulation to The Immune System.

Non-coding RNAs (ncRNAs), such as microRNAs (miRNAs), long non-coding RNAs (lncRNAs), and circular RNAs (circRNAs), have emerged as important regulators of the immune system and are involved in the control of immune cell biology, disease pathogenesis, as well as vaccine responses. A repository of ncRNA-immune associations will facilitate our understanding of ncRNA-dependent mechanisms in the immune system and advance the development of therapeutics and prevention for immune disorders. Here, we describe a comprehensive database, RNA2Immune, which aims to provide a high-quality resource of experimentally supported database linking ncRNA regulatory mechanisms to immune cell function, immune disease, cancer immunology, and vaccines. The current version of RNA2Immune documents 50,433 immune-ncRNA associations in 42 host species, including (1) 6690 ncRNA associations with immune functions involving 31 immune cell types; (2) 38,672 ncRNA associations with 348 immune diseases; (3) 4833 ncRNA associations with cancer immunology; and (4) 238 ncRNA associations with vaccine responses involving 26 vaccine types targeting 22 diseases. RNA2Immune provides a user-friendly interface for browsing, searching, and downloading ncRNA-immune system associations. Collectively, RNA2Immune provides important information about how ncRNAs influence immune cell function, how dysregulation of these ncRNAs leads to pathological consequences (immune diseases and cancers), and how ncRNAs affect immune responses to vaccines. RNA2Immune is available at http://bio-bigdata.hrbmu.edu.cn/rna2immune/home.jsp.

Construction of a ferroptosis-related signature based on seven lncRNAs for prognosis and immune landscape in clear cell renal cell carcinoma.

BACKGROUND: Recent studies have demonstrated that long non-coding RNAs (lncRNAs) are involved in regulating tumor cell ferroptosis. However, prognostic signatures based on ferroptosis-related lncRNAs (FRLs) and their relationship to the immune microenvironment have not been comprehensively explored in clear cell renal cell carcinoma (ccRCC). METHODS: In the present study, the expression profiles of ccRCC were acquired from The Cancer Genome Atlas (TCGA) database; 459 patient specimens and 69 adjacent normal tissues were randomly separated into training or validation cohorts at a 7:3 ratio. We identified 7 FRLs that constitute a prognostic signature according to the differential analysis, correlation analysis, univariate regression, and least absolute shrinkage and selection operator (LASSO) Cox analysis. To identify the independence of risk score as a prognostic factor, univariate and multivariate regression analyses were also performed. Furthermore, CIBERSORT was conducted to analyze the immune infiltration of patients in the high-risk and low-risk groups. Subsequently, the differential expression of immune checkpoint and m6A genes was analyzed in the two risk groups. RESULTS: A 7-FRLs prognostic signature of ccRCC was developed to distinguish patients into high-risk and low-risk groups with significant survival differences. This signature has great prognostic performance, with the area under the curve (AUC) for 1, 3, and 5 years of 0.713, 0.700, 0.726 in the training set and 0.727, 0.667, and 0.736 in the testing set, respectively. Moreover, this signature was significantly associated with immune infiltration. Correlation analysis showed that risk score was positively correlated with regulatory T cells (Tregs), activated CD4 memory T cells, CD8 T cells and follicular helper T cells, whereas it was inversely correlated with monocytes and M2 macrophages. In addition, the expression of fourteen immune checkpoint genes and nine m6A-related genes varied significantly between the two risk groups. CONCLUSION: We established a novel FRLs-based prognostic signature for patients with ccRCC, containing seven lncRNAs with precise predictive performance. The FRLs prognostic signature may play a significant role in antitumor immunity and provide a promising idea for individualized targeted therapy for patients with ccRCC.

The dynamic dysregulated network identifies stage-specific markers during lung adenocarcinoma malignant progression and metastasis.

Brain metastasis occurs in approximately 30% of patients with lung adenocarcinoma (LUAD) and is closely associated with poor prognosis, recurrence, and death. However, dynamic gene regulation and molecular mechanism driving LUAD progression remain poorly understood. In this study, we performed a comprehensive single-cell transcriptome analysis using data from normal, early stage, advanced stage, and brain metastasis LUAD. Our single-cell-level analysis reveals the cellular composition heterogeneity at different stages during LUAD progression. We identified stage-specific risk genes that could contribute to LUAD progression and metastasis by reprogramming immune-related and metabolic-related functions. We constructed an early advanced metastatic dysregulated network and revealed the dynamic changes in gene regulations during LUAD progression. We identified 6 early advanced (HLA-DRB1, HLA-DQB1, SFTPB, SFTPC, PLA2G1B, and FOLR1), 8 advanced metastasis (RPS15, RPS11, RPL13A, RPS24, HLA-DRB5, LYPLA1, KCNJ15, and PSMA3), and 2 common risk genes in different stages (SFTPD and HLA-DRA) as prognostic markers in LUAD. Particularly, decreased expression of HLA-DRA, HLA-DRB1, HLA-DQB1, and HLA-DRB5 refer poor prognosis in LUAD by controlling antigen processing and presentation and T cell activation. Increased expression of PSMA3 and LYPLA1 refer poor prognosis by reprogramming fatty acid metabolism and RNA catabolic process. Our findings will help further understanding the pathobiology of brain metastases in LUAD.

Identifying enhancer-driven subtype-specific prognostic markers in breast cancer based on multi-omics data.

Breast cancer is a cancer of high complexity and heterogeneity, with differences in prognosis and survival among patients of different subtypes. Copy number variations (CNVs) within enhancers are crucial drivers of tumorigenesis by influencing expression of their targets. In this study, we performed an integrative approach to identify CNA-driven enhancers and their effect on expression of target genes in four breast cancer subtypes by integrating expression data, copy number data and H3K27ac data. We identified 672, 555, 531, 361 CNA-driven enhancer-gene pairs and 280, 189, 113 and 98 CNA-driven enhancer-lncRNA pairs in the Basal-like, Her2, LumA and LumB subtypes, respectively. We then reconstructed a CNV-driven enhancer-lncRNA-mRNA regulatory network in each subtype. Functional analysis showed CNA-driven enhancers play an important role in the progression of breast cancer subtypes by influencing P53 signaling pathway, PPAR signaling pathway, systemic lupus erythematosus and MAPK signaling pathway in the Basal-like, Her2, LumA and LumB subtypes, respectively. We characterized the potentially prognostic value of target genes of CNV-driven enhancer and lncRNA-mRNA pairs in the subtype-specific network. We identified MUM1 and AC016876.1 as prognostic biomarkers in LumA and Basal-like subtypes, respectively. Higher expression of MUM1 with an amplified enhancer exhibited poorer prognosis in LumA patients. Lower expression of AC016876.1 with a deleted enhancer exhibited poorer survival outcomes of Basal-like patients. We also identified enhancer-related lncRNA-mRNA pairs as prognostic biomarkers, including AC012313.2-MUM1 in the LumA, AC026471.4-PLK5 in the LumB, AC027307.2-OAZ1 in the Basal-like and AC022431.1-HCN2 in the Her2 subtypes. Finally, our results highlighted target genes of CNA-driven enhancers and enhancer-related lncRNA-mRNA pairs could act as prognostic markers and potential therapeutic targets in breast cancer subtypes.