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.

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.

Cells function within intricate three-dimensional (3D) architectures to form tissues and organs. Constructing 3D tissue structure is critical for understanding cellular states, biological processes, and intercellular interactions. Herein, we describe a comprehensive 3D spatial omics resource, So3D (http://bio-bigdata.hrbmu.edu.cn/So3D or https://so3d.bio-database.com/), which aims to construct 3D tissue architecture and dissect biological processes occurring in 3D space from multiple perspectives. We systematically collected 72 sets of 3D spatial transcriptome datasets, involving 1 132 902 spots across 882 slices from four species, and matched reference single-cell RNA-sequencing data, covering 763 893 cells from 28 datasets. So3D also provides multiple flexible analysis modules for retrieving and analyzing 3D tissue, such as inference of 3D spatial domains and gene expression patterns across 3D tissue regions, 2D spatial slices, and single-cell datasets; mapping of cell type distribution and cell–cell communication networks to explore intercellular crosstalk in 3D space; and functional annotation of biological pathways and signaling networks within 3D tissue contexts. Collectively, So3D provides comprehensive insights for investigating biologically meaningful 3D spatial domains, mapping local gene expression landscapes, functional state, and communication networks in tissue, and may also serve as an efficient and reliable tool for understanding the tissue microenvironment and discovering biomarkers.

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.

Breast cancer has become the most common cancer that leads to women’s death. Breast cancer is a complex, highly heterogeneous disease classified into various subtypes based on histological features, which determines the therapeutic options. System identification of effective drugs for each subtype remains challenging. In this work, we present a computational network biology approach to screen precision drugs for different breast cancer subtypes by considering the impact intensity of candidate drugs on the pathway crosstalk mediated by miRNAs. Firstly, we constructed and analyzed the subtype-specific risk pathway crosstalk networks mediated by miRNAs. Then, we evaluated 36 Food and Drug Administration (FDA)-approved anticancer drugs by quantifying their effects on these subtype-specific pathway crosstalk networks and combining with survival analysis. Finally, some first-line treatments of breast cancer, such as Paclitaxel and Vincristine, were optimized for each subtype. In particular, we performed precision screening of subtype-specific therapeutic drugs and also confirmed some novel drugs suitable for breast cancer treatment. For example, Sorafenib was applicable for the basal subtype treatment, Irinotecan was optimum for Her2 subtype treatment, Vemurafenib was suitable for the LumA subtype treatment, and Vorinostat could apply to LumB subtype treatment. In addition, the mechanism of these optimal therapeutic drugs in each subtype of breast cancer was further dissected. In summary, our study offers an effective way to screen precision drugs for various breast cancer subtype treatments. We also dissected the mechanism of optimal therapeutic drugs, which may provide novel insight into the precise treatment of cancer and promote researches on the mechanisms of action of drugs.

The Coronavirus disease 2019 (COVID-19) has rapidly become the worst pandemic since the 1918 influenza pandemic. Studies have shown that severe COVID-19 patients have immune dysfunction. To characterize the dysregulated immune response to SARS-CoV-2 infection, we performed a comprehensive analysis of scRNA-seq and scV(D)J-seq in peripheral blood mononuclear cells from mild, moderate, and severe patients. We observed that as the severity of the disease increased, several CD8 + T cell subsets and Treg cells continued to decrease, while CD4 + T subsets, natural killer cells and plasma cells continued to increase. Several aberrantly expressed biomarkers associated with SARS-CoV-2 severity were identified. For example, RPS26 was down-regulated, while the ZFP36, IL-32, and IgM genes were up-regulated with increasing disease severity. Functional analysis showed multiple immune-related pathways, such as interleukin-2 and interleukin-10 production pathways, were dysregulated. As the disease severity increased, intercellular interactions fluctuated. Particularly, naive CD8 + T cells regulated memory and activated CD8 + T cells, and the weakening in Treg cells’ regulation of other immune cells was especially obvious. The expression of the MIF signaling pathway, mediated by CD74 + CXCR4, was higher throughout SARS-CoV-2 infection and the intensity of immune cell-cell interactions mediated by TGF-β was enhanced from mild to severe. Subsequently, scV(D)J-seq analysis showed a decreasing trend in the number of clonotypes, repertoire diversity and clonotypes overlap of monoclonal B cell receptor (BCR) and T cell receptor (TCR) as the SARS-CoV-2 progresses. The CDR3 sequence length in COVID-19-specific clonotypes showed a bias towards being longer as the severity of COVID-19 increases. Our findings may provide new clues for understanding COVID-19 immunopathogenesis and help identify optimal biomarkers for new therapeutic strategies.