Single-cell and WGCNA uncover a prognostic model and potential oncogenes in colorectal cancer.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:36117173", "kind": "science-record", "revision": 3, "content_hash": "sha256:6253b5e8a43fdcec5535f9988c4fbca3f81e62f30eca81d7d68fa46ce011d845", "created_at": "2026-11-01T18:15:22Z", "updated_at": "2026-11-01T21:28:57Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-cell and WGCNA uncover a prognostic model and potential oncogenes in colorectal cancer.", "doi": "10.1186/s12575-022-00175-x", "pmid": "36117173", "authors": ["Ziyang Di", "Sicheng Zhou", "Gaoran Xu", "Lian Ren", "Chengxin Li", "Zheyu Ding", "Kaixin Huang", "Leilei Liang", "Yihang Yuan"], "journal": "Biological procedures online", "year": 2022, "abstract": "BACKGROUND: Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. Single-cell transcriptome sequencing (scRNA-seq) can provide accurate gene expression data for individual cells. In this study, a new prognostic model was constructed by scRNA-seq and bulk transcriptome sequencing (bulk RNA-seq) data of CRC samples to develop a new understanding of CRC.\nMETHODS: CRC scRNA-seq data were downloaded from the GSE161277 database, and CRC bulk RNA-seq data were downloaded from the TCGA and GSE17537 databases. The cells were clustered by the FindNeighbors and FindClusters functions in scRNA-seq data. CIBERSORTx was applied to detect the abundance of cell clusters in the bulk RNA-seq expression matrix. WGCNA was performed with the expression profiles to construct the gene coexpression networks of TCGA-CRC. Next, we used a tenfold cross test to construct the model and a nomogram to assess the independence of the model for clinical application. Finally, we examined the expression of the unreported model genes by qPCR and immunohistochemistry. A clone formation assay and orthotopic colorectal tumour model were applied to detect the regulatory roles of unreported model genes.\nRESULTS: A total of 43,851 cells were included after quality control, and 20 cell clusters were classified by the FindCluster () function. We found that the abundances of C1, C2, C4, C5, C15, C16 and C19 were high and the abundances of C7, C10, C11, C13, C14 and C17 were low in CRC tumour tissues. Meanwhile, the results of survival analysis showed that high abundances of C4, C11 and C13 and low abundances of C5 and C14 were associated with better survival. The WGCNA results showed that the red module was most related to the tumour and the C14 cluster, which contains 615 genes. Lasso Cox regression analysis revealed 8 genes (PBXIP1, MPMZ, SCARA3, INA, ILK, MPP2, L1CAM and FLNA), which were chosen to construct a risk model. In the model, the risk score features had the greatest impact on survival prediction, indicating that the 8-gene risk model can better predict prognosis. qPCR and immunohistochemistry analysis showed that the expression levels of MPZ, SCARA3, MPP2 and PBXIP1 were high in CRC tissues. The functional experiment results indicated that MPZ, SCARA3, MPP2 and PBXIP1 could promote the colony formation ability of CRC cells in vitro and tumorigenicity in vivo.\nCONCLUSIONS: We constructed a risk model to predict the prognosis of CRC patients based on scRNA-seq and bulk RNA-seq data, which could be used for clinical application. We also identified 4 previously unreported model genes (MPZ, SCARA3, MPP2 and PBXIP1) as novel oncogenes in CRC. These results suggest that this model could potentially be used to evaluate the prognostic risk and provide potential therapeutic targets for CRC patients.", "claims": [{"id": "claim-method", "statement": "Then, Lasso regression was used to solve the multicollinearity problem during regression analysis and reduce the number of genes in the risk model.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36117173", "locator": "sections[21] \"Construction and evaluation of a prognostic risk model\" paragraph 2", "quote": "Then, Lasso regression was used to solve the multicollinearity problem during regression analysis and reduce the number of genes in the risk model."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets used or analysed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:36117173", "locator": "sections[33] \"notes\" paragraph 1", "quote": "The datasets used or analysed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "An 8-gene risk model constructed from CRC scRNA-seq and bulk RNA-seq data predicted overall survival, with TCGA 1-, 3- and 5-year AUCs of 0.72, 0.70 and 0.65, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:36117173", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Lasso Cox regression analysis revealed 8 genes (PBXIP1, MPMZ, SCARA3, INA, ILK, MPP2, L1CAM and FLNA), which were chosen to construct a risk model."}, {"asset": "source:pmid:36117173", "locator": "sections[21] \"Construction and evaluation of a prognostic risk model\" paragraph 4", "quote": "The AUCs of the risk model for predicting 1-, 3- and 5-year survival were 0.72, 0.70 and 0.65, respectively (Fig. 7C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "An 8-gene risk model constructed from CRC scRNA-seq and bulk RNA-seq data predicted overall survival, with TCGA 1-, 3- and 5-year AUCs of 0.72, 0.70 and 0.65, respectively.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:36117173"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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Single-cell transcriptome sequencing (scRNA-seq) can provide accurate gene expression data f", "y, a new prognostic model was constructed by scRNA-seq and bulk transcriptome sequencing (bulk RNA-", "lop a new understanding of CRC. METHODS: CRC scRNA-seq data were downloaded from the GSE161277 data", "FindNeighbors and FindClusters functions in scRNA-seq data. CIBERSORTx was applied to detect the a"], "order_basis": "paper_text_order:188", "id": "assay:1"}, {"role": "data_generation", "definition_id": "rna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["-seq and bulk transcriptome sequencing (bulk RNA-seq) data of CRC samples to develop a new unders", "t the abundance of cell clusters in the bulk RNA-seq expression matrix. WGCNA was performed with", "of CRC patients based on scRNA-seq and bulk RNA-seq data, which could be used for clinical appli", "ells. Abundance analysis of clusters in bulk RNA-seq samples To assess the correlation between ce"], "order_basis": "paper_text_order:370", "id": "assay:2"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["expression of the unreported model genes by qPCR and immunohistochemistry. A clone formation", "ene risk model can better predict prognosis. qPCR and immunohistochemistry analysis showed tha", "P1) in clinical samples from CRC patients by qPCR (Fig. 9A-D) and IHC (Fig. 9E-H) analysis and", "e unreported model genes. The results of A-D qPCR and E–H IHC analysis showed that the express"], "order_basis": "paper_text_order:1101", "id": "assay:3"}, {"role": "data_generation", "definition_id": "ihc", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["on of the unreported model genes by qPCR and immunohistochemistry. A clone formation assay and orthotopic colo", "model can better predict prognosis. qPCR and immunohistochemistry analysis showed that the expression levels o", "es from CRC patients by qPCR (Fig. 9A-D) and IHC (Fig. 9E-H) analysis and found that the expr", "model genes. The results of A-D qPCR and E–H IHC analysis showed that the expression of MPZ,"], "order_basis": "paper_text_order:1109", "id": "assay:4"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["ity of CRC cells in vitro and tumorigenicity in vivo. CONCLUSIONS: We constructed a risk model to", "ells in vitro (Fig. 10 A) and tumorigenicity in vivo (Fig. 10 B-C), while overexpression of MPZ,", "cells in vitro (Fig. S7B) and tumorigenicity in vivo (Fig. S7C). IHC analysis of xenografted tumo", "W620 cells A in vitro and B-C tumorigenicity in vivo. D IHC analysis of xenografted tumour tissue"], "order_basis": "paper_text_order:2444", "id": "assay:5"}, {"role": "data_generation", "definition_id": "transfection", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["e cell lines in SW620 cells, and SW620 cells transfected with sh-NC were considered the control. West", "Western blots were used to validate the cell transfection efficiency and the results are presented in", "in the CRC cell line SW480, and SW480 cells transfected with vector were considered the control. The"], "order_basis": "paper_text_order:12067", "id": "assay:6"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["cted with sh-NC were considered the control. Western blots were used to validate the cell transfection"], "order_basis": "paper_text_order:12119", "id": "assay:7"}, {"role": "analysis", "definition_id": "survival_analysis", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["cox_ph"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["Then, Lasso regression was used to solve the multicollinearity problem during regression analysis and reduce the number of genes in the risk model."], "order_basis": "consumes_earlier_output", "id": "assay:8"}]}