Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35847888", "kind": "science-record", "revision": 5, "content_hash": "sha256:a1fec1802114da5faf9e18ad1b6e6122ffe2ddff730eb1c435c4b6218af01ea2", "created_at": "2026-11-01T18:14:56Z", "updated_at": "2026-11-01T22:49:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Integrative Proteo-Genomic Analysis for Recurrent Survival Prognosis in Colon Adenocarcinoma.", "doi": "10.3389/fonc.2022.871568", "pmid": "35847888", "authors": ["FeiYan Ai", "Wenhao Wang", "Shaojun Liu", "Decai Zhang", "Zhenyu Yang", "Fen Liu"], "journal": "Frontiers in oncology", "year": 2022, "abstract": "Background: The survival prognosis is the hallmark of cancer progression. Here, we aimed to develop a recurrence-related gene signature to predict the prognosis of colon adenocarcinoma (COAD).\nMethods: The proteomic data from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) and genomic data from the cancer genomic maps [The Cancer Genome Atlas (TCGA)] dataset were analyzed to identify co-differentially expressed genes (cDEGs) between recurrence samples and non-recurrence samples in COAD using limma package. Functional enrichment analysis, including Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was conducted. Univariate and multivariate Cox regressions were applied to identify the independent prognostic feature cDEGs and establish the signature whose performance was evaluated by Kaplan-Meier curve, receiver operating characteristic (ROC), Harrell's concordance index (C-index), and calibration curve. The area under the receiver operating characteristic (ROC) curve (AUROC) and a nomogram were calculated to assess the predictive accuracy. GSE17538 and GSE39582 were used for external validation. Quantitative real-time PCR and Western blot analysis were carried out to validate our findings.\nResults: We identified 86 cDEGs in recurrence samples compared with non-recurrence samples. These genes were primarily enriched in the regulation of carbon metabolic process, fructose and mannose metabolism, and extracellular exosome. Then, an eight-gene-based signature (CA12, HBB, NCF1, KBTBD11, MMAA, DMBT1, AHNAK2, and FBLN2) was developed to separate patients into high- and low-risk groups. Patients in the low-risk group had significantly better prognosis than those in the high-risk group. Four prognostic clinical features, including pathological M, N, T, and RS model status, were screened for building the nomogram survival model. The PCR and Western blot analysis results suggested that CA12 and AHNAK2 were significantly upregulated, while MMAA and DMBT1 were downregulated in the tumor sample compared with adjacent tissues, and in non-recurrent samples compared with non-recurrent samples in COAD.\nConclusion: These identified recurrence-related gene signatures might provide an effective prognostic predictor and promising therapeutic targets for COAD patients.", "claims": [{"id": "claim-method", "statement": "The limma package of R3.6.1 Version 3.34.7 (17) was used to identify the differentially expression genes (DEGs) between recurrent and non-recurrent samples in TCGA COAD samples. Similarly, the differentially expressed proteins (DEPs) were identified between recurrent and non-recurrent samples in CPTAC database using the limma package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35847888", "locator": "sections[4] \"Identification of Co-Differentially Expression Genes\" paragraph 2", "quote": "The limma package of R3.6.1 Version 3.34.7 (17) was used to identify the differentially expression genes (DEGs) between recurrent and non-recurrent samples in TCGA COAD samples. Similarly, the differentially expressed proteins (DEPs) were identified between recurrent and non-recurrent samples in CPTAC database using the limma package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35847888", "locator": "sections[24] \"Data Availability Statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/ Supplementary Material , further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "ROC curve showed that the AUC for OS was 0.870 in the training dataset, 0.771 in the validation dataset 1, and 0.799 in the validation dataset 2, indicating a better predictive performance in established prognostic model ( Figure 3D ).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35847888", "locator": "sections[18] \"Establishing the Prognostic Model and Performance Evaluation\" paragraph 4", "quote": "ROC curve showed that the AUC for OS was 0.870 in the training dataset, 0.771 in the validation dataset 1, and 0.799 in the validation dataset 2, indicating a better predictive performance in established prognostic model ( Figure 3D )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "ROC curve showed that the AUC for OS was 0.870 in the training dataset, 0.771 in the validation dataset 1, and 0.799 in the validation dataset 2, indicating a better predictive performance in established prognostic model ( Figure 3D ).", "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:35847888"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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Similarly, the differentially expressed proteins (DEPs) were identified between recurrent and non-recurrent samples in CPTAC database using the limma package."}}, {"id": "method-2", "family": "group_comparison", "specific": "wilcoxon", "label": "Group comparison", "dsl_operations": [], "missing_operations": ["group_comparison_wilcoxon"], "expressible": false, "parameters": {}, "data_shapes": ["two_or_more_groups_continuous"], "evidence": {"score": 0.8, "band": "high", "quote": "Wilcoxon"}}, {"id": "method-3", "family": "survival_analysis", "specific": "kaplan_meier", "label": "Survival analysis", "dsl_operations": ["survival_km"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 0.7, "band": "medium", "quote": "Kaplan-Meier"}}, {"id": "method-4", "family": "differential_expression", "specific": "limma_voom", "label": "Differential expression analysis", "dsl_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["count_matrix_with_condition"], "evidence": {"score": 0.7, "band": "medium", "quote": "limma"}}, {"id": "method-5", "family": "diagnostic_evaluation", "specific": "roc", "label": "Diagnostic test evaluation", "dsl_operations": ["confusion_counts", "roc_curve", "sensitivity_specificity", "solve_confusion_matrix", "classification_metrics", "published_precision_envelope"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["reference_standard_and_test"], "evidence": {"score": 0.7, "band": "medium", "quote": "AUROC"}}], "targets": [], "gaps": ["method.reader_differs_from_extractor"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["cox_ph"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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