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pmid:33028275

Prognosis prediction model based on competing endogenous RNAs for recurrence of colon adenocarcinoma.

Li Peng Jin · Tao Liu · Fan Qi Meng · Jian Dong Tai

BMC cancer · 2020 doi:10.1186/s12885-020-07163-y

Paper

complete

Experiment

complete

Data

partial

Analysis

partial

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:33028275
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2026-11-01T15:41:12Z
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title
Prognosis prediction model based on competing endogenous RNAs for recurrence of colon adenocarcinoma.
doi
10.1186/s12885-020-07163-y
pmid
33028275
authors array · 4 items
[0]
Li Peng Jin
[1]
Tao Liu
[2]
Fan Qi Meng
[3]
Jian Dong Tai
journal
BMC cancer
year
2020
abstract
BACKGROUND: Colon adenocarcinoma (COAD) patients who develop recurrence have poor prognosis. Our study aimed to establish effective prognosis prediction model based on competing endogenous RNAs (ceRNAs) for recurrence of COAD. METHODS: COAD expression profilings downloaded from The Cancer Genome Atlas (TCGA) were used as training dataset, and expression profilings of GSE29623 retrieved from Gene Expression Omnibus (GEO) were set as validation dataset. Differentially expressed RNAs (DERs) between non-recurrent and recurrent specimens in training dataset were screened, and optimum prognostic signature DERs were revealed to establish prognostic score (PS) model. Kaplan-Meier survival analysis was conducted for PS model, and GEO dataset was used for validation. Prognosis prediction efficiencies were evaluated by area under curve (AUC) and C-index. Meanwhile, ceRNA regulatory network was constructed by using signature mRNAs, lncRNAs and miRNAs. RESULTS: We identified 562 DERs including 42 lncRNAs, 36 miRNAs, and 484 mRNAs. PS prediction model, consisting of 17 optimum prognostic signature DERs, showed that high risk group had significantly poorer prognosis (5-year AUC = 0.951, C-index = 0.788), which also validated in GSE29623. Prognosis prediction model incorporating multi-RNAs with pathologic distant metastasis (M) and pathologic primary tumor (T) (5-year AUC = 0.969, C-index = 0.812) had better efficiency than clinical prognosis prediction model (5-year AUC = 0.712, C-index = 0.680). In the constructed ceRNA regulatory network, lncRNA NCBP2-AS1 could interact with hsa-miR-34c and hsa-miR-363, and lncRNA LINC00115 could interact with hsa-miR-363 and hsa-miR-4709. SIX4, GRAP, NKAIN4, MMAA, and ERVMER34-1 are regulated by hsa-miR-4709. CONCLUSION: Prognosis prediction model incorporating multi-RNAs with pathologic M and pathologic T may have great value in COAD prognosis prediction.
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Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [19] of R3.4.1.
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sections[4] "Identification of differentially expressed RNAs" paragraph 2
quote
Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [19] of R3.4.1.
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The raw data were collected and analyzed by the Authors, and are not ready to share their data because the data have not been published.
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mRNA and microRNA profile in colon cancer
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The optimum prognostic signature DERs identified by L1 penalized estimation based Cox-Proportional Hazards (Cox-PH) model
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Prognostic score prediction model
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Table 1
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The optimum prognostic signature DERs identified by L1 penalized estimation based Cox-Proportional Hazards (Cox-PH) model
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[0] array · 5 items
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Chromosome location
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Type
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Multi-variate Cox regression analysis
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Lasso coefficient
[1] array · 3 items
[0]
HR
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95%CI
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P value
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[0]
Protective RNAs
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[0]
hsa-mir-105-1
[1]
Xq28
[2]
miRNA
[3]
0.629
[4]
0.444–0.891
[5]
4.990E-02
[6]
−0.034
[4] array · 7 items
[0]
hsa-mir-3609
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[5]
9.340E-04
[6]
−0.394
[5] array · 7 items
[0]
MMAA
[1]
4q31.21
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[3]
0.680
[4]
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[5]
3.520E-02
[6]
−0.781
[6] array · 1 item
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[3]
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[4]
1.103–2.595
[5]
1.602E-02
[6]
0.238
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[0]
NCBP2-AS1
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3q29
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[4]
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[5]
1.080E-04
[6]
0.122
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hsa-mir-34c
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[4]
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[5]
3.019E-02
[6]
0.039
[10] array · 7 items
[0]
hsa-mir-363
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[3]
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[4]
1.121–1.395
[5]
6.490E-05
[6]
0.243
[11] array · 7 items
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hsa-mir-4709
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14q24.3
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[3]
1.480
[4]
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[5]
2.510E-04
[6]
0.635
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hsa-mir-891a
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1.695E-02
[6]
0.196
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5.660E-05
[6]
0.395
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9.378E-03
[6]
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1.704E-03
[6]
0.542
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9.050E-03
[6]
0.231
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20q13.33
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[3]
2.221
[4]
1.396–3.533
[5]
7.510E-04
[6]
0.220
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PADI1
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1p36.13
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1.326
[4]
1.159–1.517
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3.970E-05
[6]
0.373
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[0]
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14q23.1
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mRNA
[3]
1.543
[4]
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[5]
3.713E-02
[6]
0.423
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SLC16A8
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22q13.1
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mRNA
[3]
2.161
[4]
1.303–3.583
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2.821E-03
[6]
0.881
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The independent prognostic clinical factors according to univariate and multivariate Cox regression analyses
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Independent prognostic clinical factors and prognosis prediction efficiencies of different models
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The independent prognostic clinical factors according to univariate and multivariate Cox regression analyses
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TCGA (N = 367)
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Uni-variables cox
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HR
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6.40E-08
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3.786
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1.168–12.265
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2.65E-02
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223/90/54
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2.053
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1.07E-08
[5]
1.404
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0.755–2.609
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2.83E-01
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Pathologic T (T1/T2/T3/T4)
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10/66/252/39
[2]
2.687
[3]
1.774–4.071
[4]
7.12E-06
[5]
2.159
[6]
1.116–4.176
[7]
2.23E-02
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[2]
1.982
[3]
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[4]
2.45E-08
[5]
0.733
[6]
0.313–1.716
[7]
4.75E-01
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[0]
Lymphovascular invasion (Yes/No/−)
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121/213/33
[2]
1.968
[3]
1.266–3.059
[4]
2.19E-03
[5]
0.709
[6]
0.357–1.407
[7]
3.25E-01
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[0]
Vascular invasion (Yes/No/−)
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68/250/49
[2]
2.211
[3]
1.361–3.589
[4]
1.00E-03
[5]
0.983
[6]
0.464–2.079
[7]
9.63E-01
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[0]
PS model status (High/ Low)
[1]
183/184
[2]
4.522
[3]
2.740–7.465
[4]
1.06E-10
[5]
4.053
[6]
2.161–7.601
[7]
1.29E-05
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[0]
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[2]
–
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–
[4]
–
[5]
–
[6]
–
[7]
–
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Disease free survival time (months, mean ± SD)
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Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [19] of R3.4.1.
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candidate
Automated Assays Laravel application and simulated adapters
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complete
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The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: limma_voom.
capability object · 4 fields
tier
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capability_id
capability:computational_analysis
availability_state
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The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
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accessions array · 1 item
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resolved_accessions array · 1 item
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statements array · 1 item
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The raw data were collected and analyzed by the Authors, and are not ready to share their data because the data have not been published.
basis
The paper names GSE29623 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
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machine_critique object · 5 fields
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MiniMax-M3.1-Flash
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minimax
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pmid:33028275
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PMID 33028275 full text
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GEO GSE29623
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agent:corpus-assay-engine
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agent:minimax-reader
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activity-resolve-GSE29623
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used array · 1 item
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Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [19] of R3.4.1.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33028275", "kind": "science-record", "revision": 8, "content_hash": "sha256:40d8f3af2a60a6aec286bd6bd3246bd26008e3465b7dff9dd898274c4ef7b6ea", "created_at": "2026-11-01T15:41:12Z", "updated_at": "2026-11-01T22:45:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Prognosis prediction model based on competing endogenous RNAs for recurrence of colon adenocarcinoma.", "doi": "10.1186/s12885-020-07163-y", "pmid": "33028275", "authors": ["Li Peng Jin", "Tao Liu", "Fan Qi Meng", "Jian Dong Tai"], "journal": "BMC cancer", "year": 2020, "abstract": "BACKGROUND: Colon adenocarcinoma (COAD) patients who develop recurrence have poor prognosis. Our study aimed to establish effective prognosis prediction model based on competing endogenous RNAs (ceRNAs) for recurrence of COAD.\nMETHODS: COAD expression profilings downloaded from The Cancer Genome Atlas (TCGA) were used as training dataset, and expression profilings of GSE29623 retrieved from Gene Expression Omnibus (GEO) were set as validation dataset. Differentially expressed RNAs (DERs) between non-recurrent and recurrent specimens in training dataset were screened, and optimum prognostic signature DERs were revealed to establish prognostic score (PS) model. Kaplan-Meier survival analysis was conducted for PS model, and GEO dataset was used for validation. Prognosis prediction efficiencies were evaluated by area under curve (AUC) and C-index. Meanwhile, ceRNA regulatory network was constructed by using signature mRNAs, lncRNAs and miRNAs.\nRESULTS: We identified 562 DERs including 42 lncRNAs, 36 miRNAs, and 484 mRNAs. PS prediction model, consisting of 17 optimum prognostic signature DERs, showed that high risk group had significantly poorer prognosis (5-year AUC = 0.951, C-index = 0.788), which also validated in GSE29623. Prognosis prediction model incorporating multi-RNAs with pathologic distant metastasis (M) and pathologic primary tumor (T) (5-year AUC = 0.969, C-index = 0.812) had better efficiency than clinical prognosis prediction model (5-year AUC = 0.712, C-index = 0.680). In the constructed ceRNA regulatory network, lncRNA NCBP2-AS1 could interact with hsa-miR-34c and hsa-miR-363, and lncRNA LINC00115 could interact with hsa-miR-363 and hsa-miR-4709. SIX4, GRAP, NKAIN4, MMAA, and ERVMER34-1 are regulated by hsa-miR-4709.\nCONCLUSION: Prognosis prediction model incorporating multi-RNAs with pathologic M and pathologic T may have great value in COAD prognosis prediction.", "claims": [{"id": "claim-method", "statement": "Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [19] of R3.4.1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33028275", "locator": "sections[4] \"Identification of differentially expressed RNAs\" paragraph 2", "quote": "Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; https://bioconductor.org/packages/release/bioc/html/limma.html) [19] of R3.4.1."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data were collected and analyzed by the Authors, and are not ready to share their data because the data have not been published.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33028275", "locator": "sections[18] \"Availability of data and materials\" paragraph 2", "quote": "The raw data were collected and analyzed by the Authors, and are not ready to share their data because the data have not been published."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:33028275"], "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:GSE29623"], "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:GSE29623"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33028275", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12885-020-07163-y", "sha256": "sha256:df25d8a2c793922c5da25eb6497bb9ecbcb926a684c94cc8a9463cfbc04a12f7", "bytes": 34827, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12885-020-07163-y", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7541229", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE29623", "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=GSE29623", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE29623", "version": null, "note": "The raw data were collected and analyzed by the Authors, and are not ready to share their data because the data have not been published.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The raw data were collected and analyzed by the Authors, and are not ready to share their data because the data have not been published.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM734111", "GSM734112", "GSM734113", "GSM734114", "GSM734115", "GSM734116", "GSM734117", "GSM734118", "GSM734119", "GSM734120", "GSM734121", "GSM734122", "GSM734123", "GSM734124", "GSM734125", "GSM734126", "GSM734127", "GSM734128", "GSM734129", "GSM734130", "GSM734131", "GSM734132", "GSM734133", "GSM734134", "GSM734135", "GSM734136", "GSM734137", "GSM734138", "GSM734139", "GSM734140", "GSM734141", "GSM734142", "GSM734143", "GSM734144", "GSM734145", "GSM734146", "GSM734147", "GSM734148", "GSM734149", "GSM734150", "GSM734151", "GSM734152", "GSM734153", "GSM734154", "GSM734155", "GSM734156", "GSM734157", "GSM734158", "GSM734159", "GSM734160", "GSM734161", 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"uri": null, "sha256": "sha256:547085094cb37376c43360f57431b9ace725c0735b1086a945ce338c8320ceb0", "bytes": 1572, "access": "open", "state": "verified", "reader": "read_table", "reader_status": "available", "identifier": "section[11]/table-wrap[1]#Tab2", "version": null, "note": "The independent prognostic clinical factors according to univariate and multivariate Cox regression analyses", "structure": {"pmcid": "PMC7541229", "section_index": 11, "section_title": "Independent prognostic clinical factors and prognosis prediction efficiencies of different models", "row_count": 13, "column_count": 8}, "table": {"label": "Table 2", "caption": "The independent prognostic clinical factors according to univariate and multivariate Cox regression analyses", "rows": [["Clinical characteristics", "TCGA (N = 367)", "Uni-variables cox", "Multi-variables cox"], ["HR", "95%CI", "P", "HR", "95%CI", "P"], ["Age (years, mean ± SD)", "65.40 ± 13.21", "0.989", "0.973–1.006", "2.26E-01", "–", "–", "–"], ["Gender (Male/Female)", "198/169", "1.485", "0.967–2.283", "6.93E-02", "–", "–", "–"], ["Pathologic M (M0/M1/−)", "279/40/48", "3.712", "2.230–6.180", "6.40E-08", "3.786", "1.168–12.265", "2.65E-02"], ["Pathologic N (N0/N1/N2)", "223/90/54", "2.053", "1.588–2.654", "1.07E-08", "1.404", "0.755–2.609", "2.83E-01"], ["Pathologic T (T1/T2/T3/T4)", "10/66/252/39", "2.687", "1.774–4.071", "7.12E-06", "2.159", "1.116–4.176", "2.23E-02"], ["Pathologic stage (I / II / III / IV)", "66/144/108/40", "1.982", "1.550–2.535", "2.45E-08", "0.733", "0.313–1.716", "4.75E-01"], ["Lymphovascular invasion (Yes/No/−)", "121/213/33", "1.968", "1.266–3.059", "2.19E-03", "0.709", "0.357–1.407", "3.25E-01"], ["Vascular invasion (Yes/No/−)", "68/250/49", "2.211", "1.361–3.589", "1.00E-03", "0.983", "0.464–2.079", "9.63E-01"], ["PS model status (High/ Low)", "183/184", "4.522", "2.740–7.465", "1.06E-10", "4.053", "2.161–7.601", "1.29E-05"], ["Tumor status (Recurrence/Free)", "88/279", "–", "–", "–", "–", "–", "–"], ["Disease free survival time (months, mean ± SD)", "28.30 ± 25.41", "–", "–", "–", "–", "–", "–"]], "row_count": 13, "column_count": 8, "numeric_cells": 62, "header_rows": 2, "locator": "section[11]/table-wrap[1]#Tab2"}}], "gaps": ["asset:data:geo:GSE29623 not downloaded", "asset:data:geo:GSE29623 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "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": 1.0, "band": "verbatim", "quote": "Afterwards, the differentially expressed RNAs between non-recurrent and recurrent specimens in training dataset were screened by Limma (Linear Models for Microarray Data) package (Version 3.34.7; 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