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pmid:33061845

A four prognosis-associated lncRNAs (PALnc) based risk score system reflects immune cell infiltration and predicts patient survival in pancreatic cancer.

Hongkai Zhuang · Shanzhou Huang · Zixuan Zhou · Zuyi Ma · Zedan Zhang · Chuanzhao Zhang · Baohua Hou

Cancer cell international · 2020 doi:10.1186/s12935-020-01588-y

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Experiment

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:33061845
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2026-11-01T15:41:13Z
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title
A four prognosis-associated lncRNAs (PALnc) based risk score system reflects immune cell infiltration and predicts patient survival in pancreatic cancer.
doi
10.1186/s12935-020-01588-y
pmid
33061845
authors array · 7 items
[0]
Hongkai Zhuang
[1]
Shanzhou Huang
[2]
Zixuan Zhou
[3]
Zuyi Ma
[4]
Zedan Zhang
[5]
Chuanzhao Zhang
[6]
Baohua Hou
journal
Cancer cell international
year
2020
abstract
BACKGROUND: Pancreatic cancer (PC) is one of the most common cancers and the leading cause of cancer-related death worldwide. Exploring novel predictive biomarkers for PC patients' prognosis is in urgent need. METHODS: In the present study, we conducted Cox proportional hazards regression to identify critical prognosis-associated lncRNAs (PALncs) in TCGA PC dataset. Based on the results of multivariate analysis, a PALnc-based risk score system was established, and validated in GSE62452 dataset. The validity and reliability of the risk score system for prognosis of PC were evaluated through ROC analysis. And function enrichment analyses for the PALncs were also performed. RESULT: In the multivariate analysis, four PALncs (LINC00476, C9orf163, LINC00346 and DSCR9) were screened out to develop a risk score system, which showed a high AUC at 3 and 5 years overall survival (0.785 at 3 year OS, 0.863 at 5 year OS) in TCGA datasets. And the ROC analysis of the risk score system for RFS in TCGA dataset revealed that AUC for RFS was 0.799 at 3 years and 0.909 at 5 years. Further, the AUC for OS in the validation cohort was 0.705 at 3 years and 0.959 at 5 years. Furthermore, the functional enrichment analysis revealed that these PALncs may be involved in various pathways related to cancer, including Ras family activation, autophagy in cancer, MAPK signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. And correlation analysis of these tumor infiltrating immune cells and risk score system revealed that the infiltration level of B cell naïve, plasma cells, and CD8+ T cells are negatively correlated to the risk score system, while macrophages M2 positively correlated to the risk score system. CONCLUSION: Our study established a four PALncs based risk score system, which reflects immune cell infiltration and predicts patient survival for PC.
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The univariable Cox proportional regression was performed to evaluate the association between individual DELnc expression and patients’ overall survival (OS) in batches using the R packages survival, survminer, and parallel in R version 3.5.2 (https://www.r-project.org/).
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sections[5] "Identification of prognosis-associated lncRNAs (PALnc)" paragraph 2
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The univariable Cox proportional regression was performed to evaluate the association between individual DELnc expression and patients’ overall survival (OS) in batches using the R packages survival, survminer, and parallel in R version 3.5.2 (https://www.r-project.org/).
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The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA), https://cancergenome.nih.gov/, and Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/.
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sections[26] "notes" paragraph 1
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The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA), https://cancergenome.nih.gov/, and Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/.
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f2132e96ae6c7a4e
required_operations array · 1 item
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cox_ph
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source_hashes object · 1 field
dsl_catalogue
f2132e96ae6c7a4e
evidence array · 1 item
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The univariable Cox proportional regression was performed to evaluate the association between individual DELnc expression and patients’ overall survival (OS) in batches using the R packages survival, survminer, and parallel in R version 3.5.2 (https://www.r-project.org/).
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analysis_only
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:33061845", "kind": "science-record", "revision": 4, "content_hash": "sha256:3779159ee16c214e026f4b24de0338edb81e90426569e7172504f2ab557bc400", "created_at": "2026-11-01T15:41:13Z", "updated_at": "2026-11-01T22:02:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A four prognosis-associated lncRNAs (PALnc) based risk score system reflects immune cell infiltration and predicts patient survival in pancreatic cancer.", "doi": "10.1186/s12935-020-01588-y", "pmid": "33061845", "authors": ["Hongkai Zhuang", "Shanzhou Huang", "Zixuan Zhou", "Zuyi Ma", "Zedan Zhang", "Chuanzhao Zhang", "Baohua Hou"], "journal": "Cancer cell international", "year": 2020, "abstract": "BACKGROUND: Pancreatic cancer (PC) is one of the most common cancers and the leading cause of cancer-related death worldwide. Exploring novel predictive biomarkers for PC patients' prognosis is in urgent need.\nMETHODS: In the present study, we conducted Cox proportional hazards regression to identify critical prognosis-associated lncRNAs (PALncs) in TCGA PC dataset. Based on the results of multivariate analysis, a PALnc-based risk score system was established, and validated in GSE62452 dataset. The validity and reliability of the risk score system for prognosis of PC were evaluated through ROC analysis. And function enrichment analyses for the PALncs were also performed.\nRESULT: In the multivariate analysis, four PALncs (LINC00476, C9orf163, LINC00346 and DSCR9) were screened out to develop a risk score system, which showed a high AUC at 3 and 5 years overall survival (0.785 at 3 year OS, 0.863 at 5 year OS) in TCGA datasets. And the ROC analysis of the risk score system for RFS in TCGA dataset revealed that AUC for RFS was 0.799 at 3 years and 0.909 at 5 years. Further, the AUC for OS in the validation cohort was 0.705 at 3 years and 0.959 at 5 years. Furthermore, the functional enrichment analysis revealed that these PALncs may be involved in various pathways related to cancer, including Ras family activation, autophagy in cancer, MAPK signaling pathway, HIF-1 signaling pathway, PI3K-Akt signaling pathway, etc. And correlation analysis of these tumor infiltrating immune cells and risk score system revealed that the infiltration level of B cell naïve, plasma cells, and CD8+ T cells are negatively correlated to the risk score system, while macrophages M2 positively correlated to the risk score system.\nCONCLUSION: Our study established a four PALncs based risk score system, which reflects immune cell infiltration and predicts patient survival for PC.", "claims": [{"id": "claim-method", "statement": "The univariable Cox proportional regression was performed to evaluate the association between individual DELnc expression and patients’ overall survival (OS) in batches using the R packages survival, survminer, and parallel in R version 3.5.2 (https://www.r-project.org/).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33061845", "locator": "sections[5] \"Identification of prognosis-associated lncRNAs (PALnc)\" paragraph 2", "quote": "The univariable Cox proportional regression was performed to evaluate the association between individual DELnc expression and patients’ overall survival (OS) in batches using the R packages survival, survminer, and parallel in R version 3.5.2 (https://www.r-project.org/)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA), https://cancergenome.nih.gov/, and Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:33061845", "locator": "sections[26] \"notes\" paragraph 1", "quote": "The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA), https://cancergenome.nih.gov/, and Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/."}], "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:33061845"], "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:GSE60979"], "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:GSE60979"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:33061845", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s12935-020-01588-y", "sha256": "sha256:cedf973abe21f7adb046e97f05307cffdaeb207e60aab96f2d5dabe23e941063", "bytes": 33678, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1186/s12935-020-01588-y", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC7547431", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE60979", "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=GSE60979", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE60979", "version": null, "note": "The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA), https://cancergenome.nih.gov/, and Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets generated and analyzed during the current study are available in The Cancer Genome Atlas (TCGA), https://cancergenome.nih.gov/, and Gene Expression Omnibus, https://www.ncbi.nlm.nih.gov/geo/.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM1494933", "GSM1494934", "GSM1494935", "GSM1494936", "GSM1494937", "GSM1494938", "GSM1494939", "GSM1494940", "GSM1494941", "GSM1494942", "GSM1494943", "GSM1494944", "GSM1494945", "GSM1494946", "GSM1494947", "GSM1494948", "GSM1494949", "GSM1494950", "GSM1494951", "GSM1494952", "GSM1494953", "GSM1494954", "GSM1494955", "GSM1494956", "GSM1494957", "GSM1494958", "GSM1494959", "GSM1494960", "GSM1494961", "GSM1494962", "GSM1494963", "GSM1494964", "GSM1494965", "GSM1494966", "GSM1494967", "GSM1494968", "GSM1494969", "GSM1494970", "GSM1494971", "GSM1494972", "GSM1494973", "GSM1494974", "GSM1494975", "GSM1494976", "GSM1494977", "GSM1494978", "GSM1494979", "GSM1494980", "GSM1494981", "GSM1494982", "GSM1494983", "GSM1494984", "GSM1494985", "GSM1494986", "GSM1494987", "GSM1494988", "GSM1494989", "GSM1494990", "GSM1494991", "GSM1494992", "GSM1494993", "GSM1494994", "GSM1494995", "GSM1494996", "GSM1494997", "GSM1494998", "GSM1494999", "GSM1495000", "GSM1495001", "GSM1495002", "GSM1495003", "GSM1495004", "GSM1495005", "GSM1495006", "GSM1495007", "GSM1495008", "GSM1495009", "GSM1495010", "GSM1495011", "GSM1495012", "GSM1495013", "GSM1495014", "GSM1495015", "GSM1495016", "GSM1495017", "GSM1495018", "GSM1495019", "GSM1495020", "GSM1495021", "GSM1495022", "GSM1495023", "GSM1495024", "GSM1495025"], "sample_count": 93, "group_sizes": null, "group_sizes_basis": null, "platform": "14550", "taxon": "Homo sapiens", "title": "mRNA and miRNA expression profiles for fresh frozen periampullary adenocarcinomas (pancreatobiliary and intestinal type) and adjacent normal tissues. 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"group_sizes": null, "group_sizes_basis": null, "platform": "6244", "taxon": "Homo sapiens", "title": "Microarray gene-expression profiles of 69 pancreatic tumors and 61 adjacent non-tumor tissue from patients with pancreatic ductal adenocarcinoma", "resolution_checked_at": "2026-11-01T15:39:55Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}], "gaps": ["asset:data:geo:GSE60979 not downloaded", "asset:data:geo:GSE60979 licence not read", "asset:data:geo:GSE62452 not downloaded", "asset:data:geo:GSE62452 licence not read"]}, "analysis": {"state": "complete", "methods": [{"id": "method-1", "family": "survival_analysis", "specific": "cox_proportional_hazards", "label": "Survival analysis", "dsl_operations": ["cox_ph"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["time_to_event_with_censoring"], "evidence": {"score": 1.0, "band": "verbatim", "quote": "The univariable Cox 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