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Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing

ABSTRACT: The highly intra-tumoral heterogeneity and complex cell origination of PCa greatly limited the significance of traditional bulk RNA sequencing in finding better biomarker for disease stratification. In the context of these sequencing strategies, significant gene expression differences in specific cell population might be normalized or even shaded by genes in those less “important” but large in amount cells. Tissue specimens based single-cell RNA sequencing holds great promise for identification of novel stratification biomarkers. However, the preparation of enough viable cells for sequencing, in particular for prostate cancer (PCa), is very challenging when using this technique. In the current study, we, for the first time, employed single-cell RNA sequencing of PCa cells isolated from cancerous prostate tissues of two patients and identified fifteen cell groups including three luminal clusters with different expression profiles. One of these luminal clusters had the highest copy number variation level and marker genes mainly enriched in PCa-related metabolic process, thereafter, was considered as the malignant luminal cells in PCa. Further analysis indicates that the fifth subgroup of these cells highly expressed genes significantly correlated with PCa initiation and early development. In addition, we found this subgroup highly expressed PCa prognosis biomarkers such as AMACR and PCA3. These findings suggest that this cell subgroup might be a critical cell population for PCa diagnosis and stratification. Moreover, by further deciphering the gene expression of this subgroup, we identified another marker gene, HPN, with a 0.930 area under the curve (AUC) score distinguishing normal tissue from PCa lesion using the RNA-seq data from The Cancer Genome Atlas (TCGA). This finding was further validated by immunostaining of HPN in PCa tissue array. The expression of HPN in PCa with Gleason score > 6 is significantly higher than those in Gleason score = 6. Taken together, we provide a valuable resource for interpreting the tumor heterogeneity in PCa, and a novel candidate marker for PCa management.

ORGANISM(S): Homo sapiens

PROVIDER: GSE157703 | GEO | 2020/10/20

REPOSITORIES: GEO

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Project description:Accurate estimation of recurrence risk is needed for optimal treatment of clinically localized prostate cancer (PCa) patients. We combined classical clinicopathological predictors of biochemical recurrence (BCR) and molecular markers into a new risk score for predicting BCR after radical prostatectomy (RP). A retrospective study which included 122 PCa patients who attended our department between 2000 and 2007 was conducted. Total RNA was isolated from formalin-fixed paraffin embedded PCa tissue. Gene expression patterns were analyzed in 21 selected samples from 7 localized, 6 locally advanced, and 8 metastatic PCa patients using Illumina microarrays. Expression levels of 41 genes were validated by quantitative PCR in an independent retrospective series of 101 patients with ≥ 10 years follow-up who underwent RP for clinically localized PCa. Univariate and multivariate logistic regression analysis were used to identify individual predictors of BCR. A risk score (RS) for predicting BCR including clinicopathological and gene expression data was developed and Kaplan-Meier curves were generated. Interaction networks between the genes of the model were built by GeneMANIA Cytoscape plugin. In a median follow-up of 120 months (range 3-190), 37 patients developed BCR (36.6%). Expression levels of 7,930 transcripts differed between clinically localized and locally advanced-metastatic PCa groups (FDR<0.1). A set of 41 genes were validated by quantitative PCR. Regression analysis showed that expression of ASF1B and MCL1 as well as Gleason score, extracapsular extension, seminal vesicle invasion and positive margins were independent prognostic factors of BCR. A RS generated using these gene expression and clinicopathological variables was able to discriminate between two groups of patients with a significantly different probability of BCR (HR 6.24; CI 3.23-12.4, p<0.01), improving the individual discriminative performance of each of these variables on their own. Direct interactions between the two genes of the model were not found. In conclusion, identification of new gene expression patterns associated with a high probability of BCR in clinically localized PCa patients treated with RP, and their combination with clinicopathological variables in a robust, easy-to-use and reliable classifier may contribute to improve PCa risk stratification and, consequently, to tailor treatment and surveillance strategies in these patients.

Project description:Background: Global DNA methylation alterations are hallmarks of cancer. The tumor-suppressive TET enzymes, which are involved in DNA demethylation, are decreased in prostate cancer (PCa); in particular, TET2 is specifically targeted by androgen-dependent mechanisms of repression in PCa and may play a central role in carcinogenesis. Thus, identification of key genes targeted by TET2 dysregulation may provide further insight into cancer biology. Results: Using a CRISPR/Cas9-derived TET2-knockout prostate cell line, and through whole-transcriptome and whole-methylome sequencing, we identified seven candidate genes – ASB2, ETNK2, MEIS2, NRG1, NTN1, NUDT10, and SRPX – exhibiting reduced expression and increased promoter methylation, a pattern characteristic of tumor suppressors. Decreased expression of these genes significantly discriminates between recurrent and non-recurrent prostate tumors from the Cancer Genome Atlas (TCGA) cohort (n=423), and ASB2, NUDT10, and SRPX were significantly correlated with lower recurrence-free survival in patients by Kaplan-Meier analysis. ASB2, MEIS2, and SRPX also showed significantly lower expression in high-risk Gleason score 8 tumors as compared to low or intermediate risk tumors, suggesting that these genes may be particularly useful as indicators of PCa progression. Furthermore, methylation array probes in the TCGA dataset, which were proximal to the highly conserved, differentially methylated sites identified in our TET2-knockout cells, were able to significantly distinguish between matched prostate tumor and normal prostate tissues (n=50 pairs). Except ASB2, all genes exhibited significantly increased methylation at these probes, and methylation status of at least one probe for each of these genes showed association with measures of PCa progression such as recurrence, stage, or Gleason score. Since ASB2 did not have any probes within the TET2-knockout differentially methylated region, we validated ASB2 methylation in an independent series of matched tumor-normal samples (n=19) by methylation-specific qPCR, which revealed concordant and significant increases in promoter methylation within the TET2-knockout site. Conclusions: Our study identifies seven genes governed by TET2 loss in PCa which exhibit association between their methylation and expression status and measures of PCa progression. As differential methylation profiles and TET2 expression are associated with advanced PCa, further investigation of these specialized TET2 targets may provide important insights into patterns of carcinogenic gene dysregulation.

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Identification of a distinct luminal subgroup diagnosing and stratifying early stage prostate cancer by tissue-based single-cell RNA sequencing

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