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Identification of the CD8 + T-cell Related Signature for Predicting the Prognosis of Gastric Cancer Based on Integrated Analysis of Bulk and Single-cell RNA Sequencing Data.
The infiltration of CD8 + T cells in the tumor microenvironment is associated with better survival and immunotherapy response. However, their roles in gastric cancer have not been explored so far. In here, the profiles of GC gene expression were collected from The Cancer Genome Atlas database. Single-cell transcriptomic data originated from GSE134520. Cell clustering, annotation, and CD8 + T-cell differential genes were from the TISCH database. We determined 896 CD8 + T-cell differential genes by scRNA-seq analysis. After integrating immune-related genes, 174 overlapping genes were obtained and a novel risk model was subsequently built. The performance of CD8 + T-cell-associated gene signature was assessed in the training and external validation sets. The gene signature showed independent risk factors of overall survival for GC. A quantitative nomogram was built to enhance the clinical efficacy of this signature. Furthermore, low-risk individuals showed higher mutation status, higher immune checkpoint expression, low Tumour Immune Dysfunction and Exclusion (TIDE) scores, and higher IPS-PD-1 combined IPS-CTLA4 scores, indicating a greater response to immunotherapy. In addition, analysis of IMvigor210 immunotherapy cohort demonstrated that low-risk individuals had a favorable response to prognosis and immunotherapy. In conclusion, we generated a CD8 + T-cell-related signature that can serve as a promising tool for personalized prognosis prediction and guiding decisions regarding immunotherapy in GC patients.
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Elevated Tumor-Associated Androgen Receptor Activity Correlates with Poor Immune Infiltration and Immunotherapy Response across Cancer Types.
UNLABELLED: The role of androgen receptor (AR) signaling in modulating antitumor immune responses has received increasing attention in recent years; however, its broader impact across diverse cancer types and between sexes remains largely unexplored. In this study, we investigated how AR activity correlates with tumor-infiltrating leukocytes, patient prognosis, and immunotherapy response across cancers and sexes. We inferred AR activity using a network-based approach across bulk RNA sequencing [RNA-seq; The Cancer Genome Atlas (TCGA)], single-cell RNA-seq (prostate cancer meta-atlas), and immunotherapy cohorts. Pathway analysis and Cox regression assessed mechanisms and survival. Immune infiltration and signatures were evaluated via TIMER and single-sample gene set enrichment analysis. Key findings were validated using digital spatial profiling and IHC. Our pan-cancer analysis of 33 TCGA cancer types revealed broad variability in AR activity, with highest observed in prostate adenocarcinoma. Genes significantly correlated with AR activity showed negative associations and were enriched in immune activation pathways. Notably, AR activity inversely correlated with leukocyte abundance and IFNγ pathway activity across tumors and sexes-unlike estrogen or progesterone receptors. Longitudinal biopsy analysis in metastatic prostate cancer showed that AR inhibition enhanced immune cell and IFNγ signatures. Single-cell analysis confirmed that tumor-intrinsic AR activity inversely correlates with immune infiltration in prostate cancer. Furthermore, low AR activity is significantly associated with favorable immunotherapy responses in hormone-independent cohorts. Spatial proteomics showed a negative correlation between AR and CD45 protein in sarcoma and ovarian cancers. These findings suggest AR activity as a pan-cancer predictive biomarker of immunotherapy response and support that AR blockade in immunotherapy-refractory tumors represents a promising treatment strategy, regardless of tumor type or patient sex. SIGNIFICANCE: Tumor-associated AR activity negatively correlates with immune infiltration and immunotherapy response across cancers, independent of sex, suggesting that combining AR inhibitors with checkpoint blockade may benefit patients with immunotherapy-refractory tumors.
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Using single cell sequencing to investigate tumor microenvironment differences in left and right colon cancer and prognosis-related core genes.
BACKGROUND: Colon cancer is a common intestinal malignancy and previous studies reported many differences between left (LCC) and right colon cancer (RCC). Traditional research methods are unable to compare differences within tumors at the single-cell level. METHODS: We downloaded single-cell RNA sequencing (scRNA seq) data from GEO database. After quality control we conducted cell clustering, annotation and pseudotime analysis to identify the differentially expressed genes (DEGs) in RCC compared to LCC. Gene set enrichment analysis (GSEA) was undertaken to explore the pathways these DEGs involved. Based on the expression of these DEGs, we divided patients from TCGA (The Cancer Genome Atlas) database. The differences of tumor microenvironment and immune infiltration in different clusters were also analyzed. WGCNA was performed to select the survival-related genes. Survival-related DEGs were selected and univariate Cox regression analysis was conducted to further identify independent prognostic genes. A nomogram model was established for risk prediction and its accuracy was verified by calibration curve and immunohistochemistry (IHC) analysis. RESULTS: We found significant differences of tumor microenvironment and immune infiltration among three patient clusters. The patients with high infiltration of memory B cells were associated with poorer survival. We eventually obtained seven prognosis-related genes: S100P, LGALS4, TIMP1, DNASE1L3, BGN, TPM2 and LY6E, based on which a risk model was constructed, which was an accurate independent predictor for CRC patients. IHC stanning confirmed that the expression levels of these prognostic genes in tumor versus adjacent non-tumor tissues were consistent with our risk model. CONCLUSIONS: Our prognosis-related risk model based on DEGs between RCC and LCC may provide better prediction of clinical outcomes for patients with colon cancer.
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scSTAR reveals hidden heterogeneity with a real-virtual cell pair structure across conditions in single-cell RNA sequencing data.
Cell-state transition can reveal additional information from single-cell ribonucleic acid (RNA)-sequencing data in time-resolved biological phenomena. However, most of the current methods are based on the time derivative of the gene expression state, which restricts them to the short-term evolution of cell states. Here, we present single-cell State Transition Across-samples of RNA-seq data (scSTAR), which overcomes this limitation by constructing a paired-cell projection between biological conditions with an arbitrary time span by maximizing the covariance between two feature spaces using partial least square and minimum squared error methods. In mouse ageing data, the response to stress in CD4+ memory T cell subtypes was found to be associated with ageing. A novel Treg subtype characterized by mTORC activation was identified to be associated with antitumour immune suppression, which was confirmed by immunofluorescence microscopy and survival analysis in 11 cancers from The Cancer Genome Atlas Program. On melanoma data, scSTAR improved immunotherapy-response prediction accuracy from 0.8 to 0.96.
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UniPath: a uniform approach for pathway and gene-set based analysis of heterogeneity in single-cell epigenome and transcriptome profiles.
Recent advances in single-cell open-chromatin and transcriptome profiling have created a challenge of exploring novel applications with a meaningful transformation of read-counts, which often have high variability in noise and drop-out among cells. Here, we introduce UniPath, for representing single-cells using pathway and gene-set enrichment scores by a transformation of their open-chromatin or gene-expression profiles. The robust statistical approach of UniPath provides high accuracy, consistency and scalability in estimating gene-set enrichment scores for every cell. Its framework provides an easy solution for handling variability in drop-out rate, which can sometimes create artefact due to systematic patterns. UniPath provides an alternative approach of dimension reduction of single-cell open-chromatin profiles. UniPath's approach of predicting temporal-order of single-cells using their pathway enrichment scores enables suppression of covariates to achieve correct order of cells. Analysis of mouse cell atlas using our approach yielded surprising, albeit biologically-meaningful co-clustering of cell-types from distant organs. By enabling an unconventional method of exploiting pathway co-occurrence to compare two groups of cells, our approach also proves to be useful in inferring context-specific regulations in cancer cells. Available at https://reggenlab.github.io/UniPathWeb/.
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FIND-seq: high-throughput nucleic acid cytometry for rare single-cell transcriptomics.
Rare cells have an important role in development and disease, and methods for isolating and studying cell subsets are therefore an essential part of biology research. Such methods traditionally rely on labeled antibodies targeted to cell surface proteins, but large public databases and sophisticated computational approaches increasingly define cell subsets on the basis of genomic, epigenomic and transcriptomic sequencing data. Methods for isolating cells on the basis of nucleic acid sequences powerfully complement these approaches by providing experimental access to cell subsets discovered in cell atlases, as well as those that cannot be otherwise isolated, including cells infected with pathogens, with specific DNA mutations or with unique transcriptional or splicing signatures. We recently developed a nucleic acid cytometry platform called 'focused interrogation of cells by nucleic acid detection and sequencing' (FIND-seq), capable of isolating rare cells on the basis of RNA or DNA markers, followed by bulk or single-cell transcriptomic analysis. This platform has previously been used to characterize the splicing-dependent activation of the transcription factor XBP1 in astrocytes and HIV persistence in memory CD4 T cells from people on long-term antiretroviral therapy. Here, we outline the molecular and microfluidic steps involved in performing FIND-seq, including protocol updates that allow detection and whole transcriptome sequencing of rare HIV-infected cells that harbor genetically intact virus genomes. FIND-seq requires knowledge of microfluidics, optics and molecular biology. We expect that FIND-seq, and this comprehensive protocol, will enable mechanistic studies of rare HIV+ cells, as well as other cell subsets that were previously difficult to recover and sequence.
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AIRE illuminates the feature of medullary thymic epithelial cells in thymic carcinoma.
Despite the clear distinction between cortical (cTECs) and medullary thymic epithelial cells (mTECs) in physiology, the cell of origin of thymic carcinomas (TCs) and other thymic epithelial tumors remained enigmatic. We addressed this issue by focusing on AIRE, an mTEC-specific transcriptional regulator that is required for immunological self-tolerance. We found that a large proportion of TCs expressed AIRE with typical nuclear dot morphology by immunohistochemistry. AIRE expression in TCs was supported by the RNA-seq data in the TCGA-THYM database. Furthermore, our bioinformatics approach to the recent single-cell RNA-seq data on human thymi has revealed that TCs hold molecular characteristics of multiple mTEC subpopulations. In contrast, TCs lacked the gene signatures for cTECs. We propose that TCs are tumors derived from mTECs.
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Identification and Verification of Key Genes in Colorectal Cancer Liver Metastases Through Analysis of Single-Cell Sequencing Data and TCGA Data.
BACKGROUND: Colorectal cancer (CRC) is highly prevalent worldwide, with more patients experiencing colorectal cancer liver metastases (CRLM). This study aimed to identify key genes in CRLM through single-cell sequencing data reanalysis and experimental validation. METHODS: The study analyzed single-cell RNA-sequencing (scRNA-seq) data from the Gene Expression Omnibus (GEO) database. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used for gene functional enrichment analysis. The Cancer Genome Atlas (TCGA) data enabled bulk-RNA expression and survival prognosis analysis. Real-time polymerase chain reaction (qPCR) detected mRNA expression, whereas Western blot determined protein levels. Cell function experiments assessed SPARC's impact on CRC cell behavior. RESULTS: Cluster analysis showed 23 classes among 17 CRLM samples, representing six cell types. A GO and KEGG analysis identified interleukin-1 beta (IL1B), CD2 molecule (CD2), and C-X-C motif chemokine ligand 8 (CXCL8) as significant prognostic factors in CRC. Secreted protein acidic and cysteine rich (SPARC) was one of the top differentially expressed genes (DEGs) in tissue stem cells, confirmed in primary and metastatic lesions. Metastatic lesions showed higher expression of SPARC and CRC stem cell marker leucine-rich repeat containing G protein-coupled receptor 5 (LGR5), which was significantly correlated positively with LGR5 expression. Knockdown of SPARC reduced CRC cell sphere- and colony-formation, invasion, and migration abilities. Overexpression of SPARC significantly increased the malignancy of CRC cells. CONCLUSIONS: Several key genes were identified in the process of CRLM. In CRLM samples and those corresponding to CRC stem cells, SPARC was significantly upregulated. In the therapy of CRLM, SPARC might be a potential target.
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Single-cell transcriptome profiles of Drosophila fruitless-expressing neurons from both sexes.
Drosophila melanogaster reproductive behaviors are orchestrated by fruitless neurons. We performed single-cell RNA-sequencing on pupal neurons that produce sex-specifically spliced fru transcripts, the fru P1-expressing neurons. Uniform Manifold Approximation and Projection (UMAP) with clustering generates an atlas containing 113 clusters. While the male and female neurons overlap in UMAP space, more than half the clusters have sex differences in neuron number, and nearly all clusters display sex-differential expression. Based on an examination of enriched marker genes, we annotate clusters as circadian clock neurons, mushroom body Kenyon cell neurons, neurotransmitter- and/or neuropeptide-producing, and those that express doublesex. Marker gene analyses also show that genes that encode members of the immunoglobulin superfamily of cell adhesion molecules, transcription factors, neuropeptides, neuropeptide receptors, and Wnts have unique patterns of enriched expression across the clusters. In vivo spatial gene expression links to the clusters are examined. A functional analysis of fru P1 circadian neurons shows they have dimorphic roles in activity and period length. Given that most clusters are comprised of male and female neurons indicates that the sexes have fru P1 neurons with common gene expression programs. Sex-specific expression is overlaid on this program, to build the potential for vastly different sex-specific behaviors.
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Construction of a new prognostic model for colorectal cancer based on bulk RNA-seq combined with The Cancer Genome Atlas data.
BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of cancer-related deaths, and improving the prognosis of CRC patients is an urgent concern. The aim of this study was to explore new immunotherapy targets to improve survival in CRC patients. METHODS: We analyzed CRC-related single-cell data GSE201348 from the Gene Expression Omnibus (GEO) database, and identified differentially expressed genes (DEGs). Subsequently, we performed differential analysis on the rectum adenocarcinoma (READ) and colon adenocarcinoma (COAD) transcriptome sequencing data [The Cancer Genome Atlas (TCGA)-CRC queue] and clinical data downloaded from TCGA database. Subgroup analysis was performed using CIBERSORTx and cluster analysis. Finally, biomarkers were identified by one-way cox regression as well as least absolute shrinkage and selection operator (LASSO) analysis. RESULTS: In this study, we analyzed CRC-related single-cell data GSE201348, and identified 5,210 DEGs. Subsequently, we performed differential analysis on the TCGA-CRC queue database, and obtained 4,408 DEGs. Then, we categorized the cancer samples in the sequencing data into three groups (k1, k2, and k3), with significant differences observed between the k1 and k2 groups via survival analysis. Further differential analysis on the samples in the k1 and k2 groups identified 1,899 DEGs. A total of 77 DEGs were selected among those DEGs obtained from three differential analyses. Through subsequent Cox univariate analysis and LASSO analysis, seven biomarkers (RETNLB, CLCA4, UGT2A3, SULT1B1, CCL24, BMP5, and ATOH1) were identified and selected to establish a risk score (RS). CONCLUSIONS: To sum up, this study demonstrates the potential of the seven-gene prognostic risk model as instrumental variables for predicting the prognosis of CRC.