Comparative analyses of the prognosis, tumor immune microenvironment, and drug treatment response between left-sided and right-sided colon cancer by integrating scRNA-seq and bulk RNA-seq data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37480572", "kind": "science-record", "revision": 4, "content_hash": "sha256:8abb7c2a085f09454e772f0e5a9f9be314dfe73924c131da5b8e5330be4d1179", "created_at": "2026-11-01T19:02:15Z", "updated_at": "2026-11-01T22:09:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Comparative analyses of the prognosis, tumor immune microenvironment, and drug treatment response between left-sided and right-sided colon cancer by integrating scRNA-seq and bulk RNA-seq data.", "doi": "10.18632/aging.204894", "pmid": "37480572", "authors": ["Lichao Cao", "Shenrui Zhang", "Danni Yao", "Ying Ba", "Qi Weng", "Jin Yang", "Hezi Zhang", "Yanan Ren"], "journal": "Aging", "year": 2023, "abstract": "BACKGROUND: In this study, we compared the prognosis, tumor immune microenvironment (TIM), and drug treatment response between left-sided (LCC) and right-sided (RCC) colon cancer to predict outcomes in patients with LCC and RCC.\nMETHODS: Based on identified differentially expressed genes and using single-cell RNA sequencing data, we constructed and validated a prognostic model for LCC and RCC patients in the TCGA-COAD cohort and GSE103479 cohort. Moreover, we compared the differences of TIM characteristics and drug treatment response between LCC and RCC patients.\nRESULTS: We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance. The RCC patients with higher risk scores were significantly associated with greater metastasis (P = 2.6×10-5), N stage (P = 0.012), advanced pathological stage (P = 1.4×10-4), and more stable microsatellite status (P = 0.007) but not T stage (P = 0.200). For LCC patients, the risk scores were not significantly associated with tumor stage and microsatellite status (P > 0.05). Additionally, immune infiltration by CD8 and regulatory T cells and M0, M1, and M2 macrophages differed significantly between LCC and RCC patients (P < 0.05). APC and TP53 mutations were significantly more common in LCC patients (P < 0.05). In contrast, KRAS, SYNE1, and MUC16 mutations were significantly more common in RCC patients (P < 0.05). In addition, tumor mutation burden values were significantly higher in RCC patients than in LCC patients (P = 5.9×10-8). Moreover, the expression of immune checkpoint targets was significantly higher in RCC patients than in LCC patients (P < 0.05), indicating that RCC patients maybe more sensitive to immunotherapy. However, LCC and RCC patients did not differ significantly in their sensitivity to eight selected chemicals or target drugs (P > 0.05). The average half-maximal inhibitory concentrations for camptothecin, teniposide, vinorelbine, and mitoxantrone were significantly lower in low-risk than in high-risk RCC patients (P < 0.05), indicating that the lower risk score of RCC patients, the more sensitive they were to these four drugs.\nCONCLUSIONS: We investigated the differences in prognosis, TIM, and drug treatment response between LCC and RCC patients, which may contribute to accurate colon cancer prognosis and treatment of colon cancer.", "claims": [{"id": "claim-method", "statement": "Kaplan–Meier survival and univariate Cox regression analyses were performed on the location-related DEGs in the LCC and RCC expression profile data with a cutoff criterion of P < 0.05. The genes with maximum prognostic value were selected for least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we established separate risk score models predicting LCC and RCC patient prognosis using a multivariate Cox regression analysis.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37480572", "locator": "sections[5] \"Prognostic LCC and RCC signature identification and validation\" paragraph 2", "quote": "Kaplan–Meier survival and univariate Cox regression analyses were performed on the location-related DEGs in the LCC and RCC expression profile data with a cutoff criterion of P < 0.05. The genes with maximum prognostic value were selected for least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we established separate risk score models predicting LCC and RCC patient prognosis using a multivariate Cox regression analysis."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The TCGA-COAD cohort was available in the UCSC Xena (https://xenabrowser.net/datapages/). The scRNA-seq datasets GSE200997, GSE14473 and bulk RNA-seq data GSE103479 were download from NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37480572", "locator": "sections[10] \"Data availability statement\" paragraph 2", "quote": "The TCGA-COAD cohort was available in the UCSC Xena (https://xenabrowser.net/datapages/). The scRNA-seq datasets GSE200997, GSE14473 and bulk RNA-seq data GSE103479 were download from NCBI-GEO database (https://www.ncbi.nlm.nih.gov/geo/)."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37480572", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We constructed and validated a five-gene prognostic model for LCC patients and a four-gene prognostic model for RCC patients, and both showed excellent performance.", "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:37480572"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE103479"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE103479"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37480572", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.18632/aging.204894", "sha256": "sha256:1d0711d09db6bcb75048f230892baa4a2c32fa9e40a3637df9c0db2766028c7f", "bytes": 45756, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.18632/aging.204894", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10415577", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE103479", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE103479", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE103479", "version": null, "note": "The TCGA-COAD cohort was available in the UCSC Xena (https://xenabrowser.net/datapages/). 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In contrast, Kirsten rat sarcoma virus proto-oncogene GTPase (KRAS),"], "order_basis": "paper_text_order:14831", "id": "assay:3"}, {"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": ["Kaplan–Meier survival and univariate Cox regression analyses were performed on the location-related DEGs in the LCC and RCC expression profile data with a cutoff criterion of P < 0.05. The genes with maximum prognostic value were selected for least absolute shrinkage and selection operator (LASSO) regression analysis. Next, we established separate risk score models predicting LCC and RCC patient prognosis using a multivariate Cox regression analysis."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}