Identifying tumour microenvironment-related signature that correlates with prognosis and immunotherapy response in breast cancer.

Tumor microenvironment (TME) plays important roles in prognosis and immune evasion. However, the relationship between TME-related genes and clinical prognosis, immune cell infiltration, and immunotherapy response in breast cancer (BRCA) remains unclear. This study described the TME pattern to construct a TME-related prognosis signature, including risk factors PXDNL, LINC02038 and protective factors SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108, as an independent prognostic factor for BRCA. We found that the prognosis signature was negatively correlated with the survival time of BRCA patients, infiltration of immune cells and the expression of immune checkpoints, while positively correlated with tumor mutation burden and adverse treatment effects of immunotherapy. Upregulation of PXDNL and LINC02038 and downregulation of SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108 in high-risk score group synergistically contribute to immunosuppressive microenvironment which characterized by immunosuppressive neutrophils, impaired cytotoxic T lymphocytes migration and natural killer cell cytotoxicity. In summary, we identified a TME-related prognostic signature in BRCA, which was connected with immune cell infiltration, immune checkpoints, immunotherapy response and could be developed for immunotherapy targets.

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.

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.

Comprehensive characterization of clonality of driver genes revealing their clinical relevance in colorectal cancer.

BACKGROUND: Genomic studies of colorectal cancer have revealed the complex genomic heterogeneity of the tumor. The acquisition and selection of genomic alterations may be critical to understanding the initiation and progression of this disease. METHODS: In this study, we have systematically characterized the clonal architecture of 97 driver genes in 536 colorectal cancer patients from TCGA. RESULTS: A high proportion of clonal mutations in 93 driver genes were observed. 40 genes showed significant associations between their clonality and multiple clinicopathologic factors. Kaplan-Meier analysis suggested that the mutation clonality of ANK1, CASP8, SMAD2, and ARID1A had a significant impact on the CRC patients' outcomes. Multivariable analysis revealed that subclonal ANK1 mutations, clonal CASP8 mutations, and clonal SMAD2 mutations independently predicted for shorter overall survival after adjusting for clinicopathological factors. The poor outcome of the subclonal ANK1 mutation may be caused by upregulation of IL4I1, IDO1, IFNG and MAPK12 which showed potential roles in tumor immune evasion through accumulation of immunosuppressive cells such as regulatory T cells and myeloid derived suppressor cells. CONCLUSION: These results suggested that the clonality of driver genes could act as prognostic markers and potential therapeutic targets in human colorectal cancer.

Functional regulations between genetic alteration-driven genes and drug target genes acting as prognostic biomarkers in breast cancer.

Differences in genetic molecular features including mutation, copy number alterations and DNA methylation, can explain interindividual variability in response to anti-cancer drugs in cancer patients. However, identifying genetic alteration-driven genes and characterizing their functional mechanisms in different cancer types are still major challenges for cancer studies. Here, we systematically identified functional regulations between genetic alteration-driven genes and drug target genes and their potential prognostic roles in breast cancer. We identified two mutation and copy number-driven gene pairs (PARP1-ACSL1 and PARP1-SRD5A3), three DNA methylation-driven gene pairs (PRLR-CDKN1C, PRLR-PODXL2 and PRLR-SRD5A3), six gene pairs between mutation-driven genes and drug target genes (SLC19A1-SLC47A2, SLC19A1-SRD5A3, AKR1C3-SLC19A1, ABCB1-SRD5A3, NR3C2-SRD5A3 and AKR1C3-SRD5A3), and four copy number-driven gene pairs (ADIPOR2-SRD5A3, CASP12-SRD5A3, SLC39A11-SRD5A3 and GALNT2-SRD5A3) that all served as prognostic biomarkers of breast cancer. In particular, RARP1 was found to be upregulated by simultaneous copy number amplification and gene mutation. Copy number deletion and downregulated expression of ACSL1 and upregulation of SRD5A3 both were observed in breast cancers. Moreover, copy number deletion of ACSL1 was associated with increased resistance to PARP inhibitors. PARP1-ACSL1 pair significantly correlated with poor overall survival in breast cancer owing to the suppression of the MAPK, mTOR and NF-kB signaling pathways, which induces apoptosis, autophagy and prevents inflammatory processes. Loss of SRD5A3 expression was also associated with increased sensitivity to PARP inhibitors. The PARP1-SRD5A3 pair significantly correlated with poor overall survival in breast cancer through regulating androgen receptors to induce cell proliferation. These results demonstrate that genetic alteration-driven gene pairs might serve as potential biomarkers for the prognosis of breast cancer and facilitate the identification of combination therapeutic targets for breast cancers.