Combining single‐cell sequencing to identify key immune genes and construct the prognostic evaluation model for colon cancer patients

Regulatory T cells (Tregs) play a critical role in the maintenance of immune tolerance and tumor evasion. However, the relative low proportion of these cells in peripheral blood and tissues has hindered many studies. We sought to establish a rapamycin-based in vitro Treg expansion procedure in patients diagnosed with colorectal cancer and perform single-cell sequencing to explore the characteristics of Treg cells. CD25+ cells enriched from peripheral blood mononuclear cells (PBMC) of colorectal tumor patients were cultured in X-VIVO15 medium, supplemented with 5% human AB serum, L-glutamine, rapamycin, interleukin-2 (IL-2), and Dynabeads human Treg expander for 21 days to expand Tregs. Treg cells with satisfactory phenotype and function were successfully expanded from CD4+CD25+ cells in patients with colorectal cancer. The median expansion fold was 75 (range, 20–105-fold), and >90.0% of the harvest cells were CD4+CD25+CD127dim/− cells. The ratio of CD4+CD25+Foxp3+ cells exceeded 60%. Functional assays showed that iTregs significantly inhibited CD8+T cell proliferation in vitro. Single-cell sequencing showed that the transcriptome of pTreg (CD4+CD25+CD127dim/− cells isolated from PBMC of colorectal cancer patients) and iTreg (CD4+CD25+CD127dim/− cells expanded in vitro according to the above regimen) cells were interlaced. pTregs exhibited enhanced suppressive function, whereas iTregs exhibited increased proliferative capacity. TCR repertoire analysis indicated minimal overlap between pTregs and iTregs. Pseudo-time trajectory analysis of Tregs revealed that pTregs were a continuum composed of three main branches: activated/effector, resting and proliferative Tregs. In contrast, in vitro expanded iTregs were a mixture of proliferating and activated/effector cells. The expression of trafficking receptors was also different in pTregs and iTregs. Various chemokine receptors were upregulated in pTregs. Activated effector pTregs overexpressed the chemokine receptor CCR10, which was not expressed in iTregs. The chemokine CCL28 was overexpressed in colorectal cancer and associated with poor prognosis. CCR10 interacted with CCL28 to mediate the recruitment of Treg into tumors and accelerated tumor progression. Depletion of CCR10+Treg cells from tumor microenvironment (TME) could be used as an effective treatment strategy for colorectal cancer patients. Our data distinguished the transcriptomic characteristics of different subsets of Treg cells and revealed the context-dependent functions of different populations of Treg cells, which was crucial to the development of alternative therapeutic strategies for Treg cells in autoimmune disease and cancer.

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Abstract 1389: Single-cell sequencing to identify genetic variant accelerating shedding of CTCs into peripheral blood in gastric cancer patients

10.1158/1538-7445.am2019-1389 ◽

2019 ◽

Author(s):

Yilin Li ◽

Xiaotian Zhang ◽

Jifang Gong ◽

Daisy Dandan Wang ◽

Mingxia Qi ◽

...

Keyword(s):

Gastric Cancer ◽

Single Cell ◽

Cancer Patients ◽

Peripheral Blood ◽

Genetic Variant ◽

Single Cell Sequencing ◽

Gastric Cancer Patients

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Colon Cancer Classification and Prognosis Prediction Based on Genomics Multi-features

10.21203/rs.3.rs-837390/v1 ◽

2021 ◽

Author(s):

Jiasheng Xu ◽

Kaili Liao ◽

Chengfeng Wu ◽

Qijun Yang ◽

Hongping Wan ◽

...

Keyword(s):

Colon Cancer ◽

Cancer Patients ◽

Immune Cell ◽

Evaluation Model ◽

Lasso Regression ◽

Data Set ◽

Prognosis Prediction ◽

External Data ◽

Core Genes ◽

Prognosis Evaluation

Abstract Background: To classify colon cancer and predict the prognosis of patients with multiple characteristics of the genome.Methods: We used the mRNA expression profile data and mutation maf files of colon cancer patients in the TCGA database to calculate the TMB value of patients. Combined with CNV, MSI, and corresponding clinical information, the patients were clustered by the "K-means" method to identify different molecular subtypes of colon cancer. Comparing the differences of prognosis, and immune cell infiltration, and other indicators among patients in each subgroup, we used COX and lasso regression analysis to screen out the prognosis difference genes among subgroups and construct the prognosis prediction model. We used the external data set to verify the model, and carried out the hierarchical analysis of the model to compare the immune infiltration of patients in the high and low-risk groups. And detected the expression differences of core genes in tumor tissues of patients with different clinical stages by qPCR and immunohistochemistry.Results: We successfully calculated the TMB value and divided the patients into three subgroups. The prognosis of the second subgroup was significantly different from the other two groups. The mmunoinfiltration analysis showed that the expression of NK.cells.resting increased in cluster1 and cluster 3, and the expression of T.cells.CD4.memory.resting increased in cluster3. By analyzing the differences among subgroups, we screened out eight core genes related to prognoses, such as HYAL1, SPINK4, EREG, and successfully constructed a patient prognosis evaluation model. The test results of the external data set shows that the model can accurately predict the prognosis of patients; Compared with risk factors such as TNM stage and age, the risk score of the model has higher evaluation efficiency. The experimental results confirmed that the differential expression of eight core genes was basically consistent with the model evaluation results.Conclusion: Colon cancer patients were further divided into three subtypes by using genomic multi-features, and eight-core genes related to prognosis were screened out and the prognosis evaluation model was successfully constructed. With external data and experiments, it verified that the model had good evaluation efficiency.

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