Identification and validation of a seven-gene prognostic marker in colon cancer based on single-cell transcriptome analysis

Colon cancer (CC) is one of the most commonly diagnosed tumours worldwide. Single‐cell RNA sequencing (scRNA‐seq) can accurately reflect the heterogeneity within and between tumour cells and identify important genes associated with cancer development and growth. In this study, scRNA‐seq was used to identify reliable prognostic biomarkers in CC. ScRNA‐seq data of CC before and after 5‐fluorouracil treatment were first downloaded from the Gene Expression Omnibus database. The data were pre‐processed, and dimensionality reduction was performed using principal component analysis and t‐distributed stochastic neighbour embedding algorithms. Additionally, the transcriptome data, somatic variant data, and clinical reports of patients with CC were obtained from The Cancer Genome Atlas database. Seven key genes were identified using Cox regression analysis and the least absolute shrinkage and selection operator method to establish signatures associated with CC prognoses. The identified signatures were validated on independent datasets, and somatic mutations and potential oncogenic pathways were further explored. Based on these features, gene signatures, and other clinical variables, a more effective predictive model nomogram for patients with CC was constructed, and a decision curve analysis was performed to assess the utility of the nomogram. A prognostic signature consisting of seven prognostic‐related genes, including CAV2, EREG, NGFRAP1, WBSCR22, SPINT2, CCDC28A, and BCL10, was constructed and validated. The proficiency and credibility of the signature were verified in both internal and external datasets, and the results showed that the seven‐gene signature could effectively predict the prognosis of patients with CC under various clinical conditions. A nomogram was then constructed based on features such as the RiskScore, patients' age, neoplasm stage, and tumor (T), nodes (N), and metastases (M) classification, and the nomogram had good clinical utility. Higher RiskScores were associated with a higher tumour mutational burden, which was confirmed to be a prognostic risk factor. Gene set enrichment analysis showed that high‐score groups were enriched in ‘cytoplasmic DNA sensing’, ‘Extracellular matrix receptor interactions’, and ‘focal adhesion’, and low‐score groups were enriched in ‘natural killer cell‐mediated cytotoxicity’, and ‘T‐cell receptor signalling pathways’, among other pathways. A robust seven‐gene marker for CC was identified based on scRNA‐seq data and was validated in multiple independent cohort studies. These findings provide a new potential marker to predict the prognosis of patients with CC.

Single-cell transcriptome analysis revealed the heterogeneity and microenvironment of gastrointestinal stromal tumors

Gastrointestinal stromal tumor (GIST) is the most common mesenchymal tumor of the human gastrointestinal tract. In this study, we performed single-cell RNA sequencing (RNA-seq) on intra- and peri-tumor tissues from GIST patients with the aim of discovering the heterogeneity of tumor cells in GIST and their interactions with other cells. We found four predominating cell types in GIST tumor tissue, including T cells, macrophages, tumor cells, and NK cells. Tumor cells could be clustered into two groups: one was highly proliferating and associated with high risk of metastasis, the other seemed "resting" and associated with low risk. Their clinical relevance and prognostic values were confirmed by RNA-seq of 65 GIST samples. T cells were the largest cell type in our single-cell data. Two groups of CD8⁺ effector memory (EM) cells were in the highest clonal expansion and performed the highest cytotoxicity but were also the most exhausted among all T cells. A group of macrophages were found polarized to possess both M1 and M2 signatures, and increased along with tumor progression. Cell-to-cell interaction analysis revealed that adipose endothelial cells had high interactions with tumor cells to facilitate their progression. Macrophages were at the center of the tumor microenvironment, recruiting immune cells to the tumor site and having most interactions with both tumor and nontumor cells. In conclusion, we obtained an overview of the GIST microenvironment and revealed the heterogeneity of each cell type and their relevance to risk classifications, which provided a novel theoretical basis for learning and curing GISTs.

Development and validation of a metastasis-associated prognostic signature based on single-cell RNA-seq in clear cell renal cell carcinoma

Single-cell RNA sequencing (scRNA-seq) was recently adopted for deciphering intratumoral heterogeneity across cell sub-populations, including clear cell renal cell carcinoma (ccRCC). Here, we characterized the single-cell expression profiling of 121 cell samples and found 44 metastasis-associated marker genes. Accordingly, we trained and validated 17 pivotal metastasis-associated genes (MAGs) in 626 patients incorporating internal and external cohorts to evaluate the model for predicting overall survival (OS) and progression-free survival (PFS). Correlation analysis revealed that the MAGs correlated significantly with several risk clinical characteristics. Moreover, we conducted Cox regression analysis integrating these independent clinical variables into a MAGs nomogram with superior accuracy in predicting progression events. We further revealed the differential landscape of somatic tumor mutation burden (TMB) between two nomogram-score groups and observed that TMB was also a prognostic biomarker; patients with high MAGs-nomogram scores suffered from a higher TMB, potentially leading to worse prognosis. Last, higher MAGs-nomogram scores correlated with the upregulation of oxidative phosphorylation, the Wnt signaling pathway, and MAPK signaling crosstalk in ccRCC. Overall, we constructed the robust MAGs through scRNA-seq and validated the model in a large patient population, which was valuable for prognostic stratification and providing potential targets against metastatic ccRCC.

CTCF promotes epithelial ovarian cancer metastasis by broadly controlling the expression of metastasis-associated genes

CCCTC-binding factor (CTCF) functions as both an oncogenic and a tumor suppressor, depending on the cancer type, through epigenetic regulation. Epigenetic regulation plays a key role in cancer metastasis. Our objective was to investigate whether CTCF plays a crucial role in epithelial ovarian cancer metastasis. First, we found that CTCF expression was increased in ovarian cancer tissues compared to non-tumor tissues. Increased expression of CTCF predicts poor prognosis of ovarian cancer patients. In addition, CTCF knockdown significantly inhibited the metastasis of ovarian cancer cells, although it had no effect on cell proliferation and tumor growth. More importantly, CTCF expression was higher in metastatic lesions compared to primary tumors from the same ovarian cancer patients. We also demonstrated that CTCF affects a number of metastasis-associated genes, including CTBP1, SERPINE1 and SRC. Additionally, our ChIP-seq results revealed that these genes have multiple CTCF-binding sites, findings that were further confirmed by ChIP-PCR. Our results suggest that CTCF could be a novel drug target to treat ovarian cancer by interfering with cancer cell metastasis.