Integrative Analysis Revealed Stemness Features and a Novel Stemness-Related Classification in Colorectal Cancer Patients

Ye, Meng-Ling; Li, Siqi; Yin, Yuqing; Li, Kezhi; Li, Jilin; Hu, Bang-li

doi:10.3389/fcell.2022.817509

Cited by 2 publications

(1 citation statement)

References 37 publications

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“… 8 One‐class logistic regression (OCLR) is a machine learning algorithm applied in this study to determine the mRNA expression of pluripotent stem cells and their differentiated progenies for calculating the single‐cell mRNA expression stemness index (mRNAsi), with an aim to reveal the tumor stemness‐related heterogeneity. 9 Ye et al 10 determined the mRNAsi scores in CRC and introduced a stemness‐related classification to predict patient outcomes based on The Cancer Genome Atlas (TCGA) database. On the basis of existing researches, this study is expected to find new stemness‐related prognostic genes of CRC.…”

Section: Introductionmentioning

confidence: 99%

Role of stemness‐related genes TIMP1, PGF, and SNAI1 in the prognosis of colorectal cancer through single‐cell RNA‐seq

Shen

et al. 2023

Cancer Medicine

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Background Colorectal cancer (CRC) is a fatal malignant tumor with poor prognosis. Cancer stem cells (CSCs) can cause metastasis, recurrence and drug resistance in CRC. This research aimed to analyze stemness‐related prognostic genes of CRC based on single‐cell RNA‐sequencing (scRNA‐seq) data. Methods DESeq2 was applied to analyze the differentially expressed genes (DEGs). The mRNA stemness index (mRNAsi) was calculated by one‐class logistic regression (OCLR). The stemness‐related cells were analyzed based on scRNA‐seq dataset GSE166555. Monocle 2 algorithm was used for stemness‐related cells pseudotime trajectory analysis. The stemness‐related prognostic genes were analyzed by clusterProfiler and survival package. The stemness of CRC cells was detected by spheroid formation assay, and the expression of stemness‐related prognostic genes was verified by qRT‐PCR and Western blot. Results 7916 DEGs between the CRC and normal tissues were obtained. The mRNAsi of the CRC tissues was shown to be significantly higher than that of the normal tissues. 7 and 8 cell types were annotated respectively in the normal and CRC tissues through analysis of the scRNA‐seq data. Cell–cell interactions (CCIs) in the tumor tissues were revealed to be significantly enhanced than that in the normal tissues. By calculating the ‘stemness score’, CSCs, epithelial cells (EPCs) and cancer‐associated fibroblasts (CAFs) were defined as stemness‐related cells. Through pseudotime trajectory analysis, 2111 genes were identified as state 2‐specific genes. Then, 41 genes were obtained by taking intersection of the up‐regulated genes with state 2‐specific genes and marker genes of CSCs, EPCs and CAFs. The univariate COX regression analysis revealed 5 stemness‐related prognostic genes (TIMP1, PGF, FSTL3, SNAI1 and FOXC1). Kaplan–Meier curve analysis indicated that the higher the expression of 5 genes, the lower the survival rate. In vitro cell experiment confirmed that the expression of TIMP1, PGF and SNAI1 was consistent with that revealed by bioinformatics analysis. Conclusions TIMP1, PGF and SNAI1 were identified as stemness‐related prognostic genes of CRC, and possibly potential therapeutic targets for CRC.

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Section: Introductionmentioning

confidence: 99%

Role of stemness‐related genes TIMP1, PGF, and SNAI1 in the prognosis of colorectal cancer through single‐cell RNA‐seq

Shen

et al. 2023

Cancer Medicine

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Characterization of stemness features and construction of a stemness subtype classifier to predict survival and treatment responses in lung squamous cell carcinoma

et al. 2023

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Background Cancer stemness has been proven to affect tumorigenesis, metastasis, and drug resistance in various cancers, including lung squamous cell carcinoma (LUSC). We intended to develop a clinically applicable stemness subtype classifier that could assist physicians in predicting patient prognosis and treatment response. Methods This study collected RNA-seq data from TCGA and GEO databases to calculate transcriptional stemness indices (mRNAsi) using the one-class logistic regression machine learning algorithm. Unsupervised consensus clustering was conducted to identify a stemness-based classification. Immune infiltration analysis (ESTIMATE and ssGSEA algorithms) methods were used to investigate the immune infiltration status of different subtypes. Tumor Immune Dysfunction and Exclusion (TIDE) and Immunophenotype Score (IPS) were used to evaluate the immunotherapy response. The pRRophetic algorithm was used to estimate the efficiency of chemotherapeutic and targeted agents. Two machine learning algorithms (LASSO and RF) and multivariate logistic regression analysis were performed to construct a novel stemness-related classifier. Results We observed that patients in the high-mRNAsi group had a better prognosis than those in the low-mRNAsi group. Next, we identified 190 stemness-related differentially expressed genes (DEGs) that could categorize LUSC patients into two stemness subtypes. Patients in the stemness subtype B group with higher mRNAsi scores exhibited better overall survival (OS) than those in the stemness subtype A group. Immunotherapy prediction demonstrated that stemness subtype A has a better response to immune checkpoint inhibitors (ICIs). Furthermore, the drug response prediction indicated that stemness subtype A had a better response to chemotherapy but was more resistant to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs). Finally, we constructed a nine-gene-based classifier to predict patients’ stemness subtype and validated it in independent GEO validation sets. The expression levels of these genes were also validated in clinical tumor specimens. Conclusion The stemness-related classifier could serve as a potential prognostic and treatment predictor and assist physicians in selecting effective treatment strategies for patients with LUSC in clinical practice.

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Integrative Analysis Revealed Stemness Features and a Novel Stemness-Related Classification in Colorectal Cancer Patients

Cited by 2 publications

References 37 publications

Role of stemness‐related genes TIMP1, PGF, and SNAI1 in the prognosis of colorectal cancer through single‐cell RNA‐seq

Role of stemness‐related genes TIMP1, PGF, and SNAI1 in the prognosis of colorectal cancer through single‐cell RNA‐seq

Characterization of stemness features and construction of a stemness subtype classifier to predict survival and treatment responses in lung squamous cell carcinoma

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