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pmid:39259370

Identification of circadian clock-related immunological prognostic index and molecular subtypes in prostate cancer.

Lu Che · Dengxiong Li · Jie Wang · Zhouting Tuo · Koo Han Yoo · Dechao Feng · Yun Ou · Ruicheng Wu · Wuran Wei

Discover oncology · 2024 doi:10.1007/s12672-024-01276-7

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Experiment

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Data

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pmid:39259370
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title
Identification of circadian clock-related immunological prognostic index and molecular subtypes in prostate cancer.
doi
10.1007/s12672-024-01276-7
pmid
39259370
authors array · 9 items
[0]
Lu Che
[1]
Dengxiong Li
[2]
Jie Wang
[3]
Zhouting Tuo
[4]
Koo Han Yoo
[5]
Dechao Feng
[6]
Yun Ou
[7]
Ruicheng Wu
[8]
Wuran Wei
journal
Discover oncology
year
2024
abstract
BACKGROUND: Evidence suggests that the circadian clock (CIC) is among the important factors for tumorigenesis. We aimed to provide new insights into CIC-mediated molecular subtypes and gene prognostic indexes for prostate cancer (PCa) patients undergoing radical prostatectomy (RP) or radical radiotherapy (RT). METHODS: PCa data from TCGA was analyzed to identify differentially expressed genes (DEGs) with significant fold changes and p-values. A prognostic index called CIC-related gene prognostic index (CICGPI) was developed through clustering methods and survival analysis and validated on multiple data sets. The diagnostic accuracy of CICGPI for resistance to chemotherapy and radiotherapy was confirmed. Additionally, the interaction between tumor immune environment and CICGPI score was explored, along with their correlation with prognosis. RESULTS: TOP2A, APOE, and ALDH2 were used to classify the PCa patients into two subtypes. Cluster 2 had a higher risk of biochemical recurrence (BCR) than cluster 1 for PCa patients undergoing RP or RT. A CIC-related gene prognostic index (CICGPI) was constructed using the above three genes for PCa patents in the TCGA database. The CICGPI score showed good prognostic value in the TCGA database and was externally confirmed by PCa patients in GSE116918, MSKCC2010 and GSE46602. In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988). For gene set variation analysis, we observed that both beta alanine metabolism and limonene and pinene degradation were upregulated in cluster 1 for PCa patients undergoing RP or RT. For PCa patients undergoing RP, cell cycle, homologous recombination, mismatch repair, and DNA replication were upregulated in cluster 2. A strongly positive relationship between cancer-related fibroblasts and CICGPI score was observed in PCa patients undergoing RP or RT. Moreover, a high density of CAFs was highly closely associated with poorer BCR-free survival of PCa patients. CONCLUSIONS: In this study, we established CIC-related immunological prognostic index and molecular subtypes, which might be useful for the clinical practice.
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We used the clustered genes to establish a CIC-related gene prognostic index (CICGPI) through Cox regression analysis using PCa patients in TCGA.
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We used the clustered genes to establish a CIC-related gene prognostic index (CICGPI) through Cox regression analysis using PCa patients in TCGA.
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The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
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In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988).
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In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988).
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In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988).
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GEO accession URL
access_statement_verbatim
The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
access_statement_kind
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sample_accessions array · 50 items
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sample_count
50
group_sizes
null
group_sizes_basis
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platform
570
taxon
Homo sapiens
title
Expression data from prostate cancer and benign prostate glands
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
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[5] object · 15 fields
id
data:geo:GSE53902
role
raw
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geo-series
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application/vnd.ncbi.geo-series+json
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https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE53902
sha256
null
bytes
null
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open
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resolved
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null
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reader_required
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GSE53902
version
null
note
The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
access_statement_kind
data_availability_statement
sample_accessions array · 18 items
[0]
GSM1303309
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GSM1303313
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[13]
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[14]
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[15]
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[16]
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[17]
GSM1303326
sample_count
18
group_sizes
null
group_sizes_basis
null
platform
16699
taxon
Homo sapiens
title
Comparative analysis of gene expression of radioresistant prostate cancer cells and prostate cancer progenitors
resolution_checked_at
2026-11-01T20:16:08Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
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[6] object · 15 fields
id
data:geo:GSE62872
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raw
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geo-series
media_type
application/vnd.ncbi.geo-series+json
uri
https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE62872
sha256
null
bytes
null
access
open
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resolved
reader
null
reader_status
reader_required
identifier
GSE62872
version
null
note
The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
structure object · 15 fields
repository
GEO
access_route
GEO accession URL
access_statement_verbatim
The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
access_statement_kind
data_availability_statement
sample_accessions array · 424 items
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[29]
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[32]
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[39]
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[46]
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[47]
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[48]
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[49]
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[50]
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[51]
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[52]
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[53]
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[54]
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[55]
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[56]
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[57]
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[58]
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[59]
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[60]
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[61]
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[62]
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[63]
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[64]
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[65]
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[66]
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[67]
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[68]
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[69]
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[70]
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[71]
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[72]
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[73]
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[74]
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[75]
GSM1534868
[76]
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[77]
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[78]
GSM1534871
[79]
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[80]
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[81]
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[82]
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[83]
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[84]
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[85]
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[86]
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[87]
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[88]
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[89]
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[90]
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[91]
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[92]
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[93]
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[94]
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[95]
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[96]
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[97]
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[98]
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[99]
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[100]
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[101]
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[102]
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[103]
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[104]
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[105]
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[107]
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[108]
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[109]
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[110]
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[111]
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[112]
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[113]
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[114]
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[115]
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[116]
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[117]
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[118]
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[149]
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[150]
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[163]
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[172]
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[173]
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[179]
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[180]
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[183]
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[186]
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[193]
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[194]
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[195]
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[196]
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[197]
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[198]
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[199]
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[200]
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[201]
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[202]
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[203]
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[204]
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[205]
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[206]
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[207]
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[208]
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[209]
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[210]
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[211]
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[212]
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[213]
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[214]
GSM1535007
[215]
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[216]
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[217]
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[218]
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[219]
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[220]
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[221]
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[222]
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[223]
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[224]
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[225]
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[226]
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[227]
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[228]
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[229]
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[230]
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[231]
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[232]
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[233]
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[234]
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[235]
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[236]
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[237]
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[238]
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[239]
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[240]
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[241]
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[242]
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[243]
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[244]
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[245]
GSM1535038
[246]
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[247]
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[248]
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[249]
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[250]
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[251]
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[252]
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[253]
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[254]
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[255]
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[256]
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[257]
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[258]
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[259]
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[260]
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[261]
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[262]
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[263]
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[264]
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[265]
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[266]
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[267]
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[268]
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[269]
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[270]
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[271]
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[272]
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[273]
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[274]
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[275]
GSM1535068
[276]
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[277]
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[278]
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[280]
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[281]
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[282]
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[283]
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[284]
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[285]
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[286]
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[287]
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[288]
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[289]
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[290]
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[292]
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[293]
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[294]
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[295]
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[296]
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[297]
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[298]
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[299]
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[302]
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[303]
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[304]
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[305]
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[306]
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[307]
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[308]
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[309]
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[315]
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[317]
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[318]
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[320]
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[327]
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[330]
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[333]
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[334]
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[336]
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[337]
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[338]
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[339]
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[340]
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[341]
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[343]
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[344]
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[347]
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[348]
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[350]
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[351]
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[353]
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[354]
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[356]
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[357]
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[358]
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[359]
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[360]
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[361]
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[395]
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[418]
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[422]
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[423]
GSM1535216
sample_count
424
group_sizes
null
group_sizes_basis
null
platform
19370
taxon
Homo sapiens
title
Association of Prostate Cancer Risk Variants with Gene Expression in Normal and Tumor Tissue
resolution_checked_at
2026-11-01T18:46:34Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 12 items
[0]
asset:data:geo:GSE116918 not downloaded
[1]
asset:data:geo:GSE116918 licence not read
[2]
asset:data:geo:GSE32571 not downloaded
[3]
asset:data:geo:GSE32571 licence not read
[4]
asset:data:geo:GSE42913 not downloaded
[5]
asset:data:geo:GSE42913 licence not read
[6]
asset:data:geo:GSE46602 not downloaded
[7]
asset:data:geo:GSE46602 licence not read
[8]
asset:data:geo:GSE53902 not downloaded
[9]
asset:data:geo:GSE53902 licence not read
[10]
asset:data:geo:GSE62872 not downloaded
[11]
asset:data:geo:GSE62872 licence not read
analysis object · 4 fields
state
complete
methods array · 2 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
1
band
verbatim
quote
We used the clustered genes to establish a CIC-related gene prognostic index (CICGPI) through Cox regression analysis using PCa patients in TCGA.
[1] object · 10 fields
id
method-2
family
survival_analysis
specific
log_rank_test
label
Survival analysis
dsl_operations array · 1 item
[0]
logrank_test
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
0.8
band
high
quote
log-rank
targets array · 0 items
gaps array · 0 items
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
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
tier
R1
reachable
true
accessions array · 6 items
[0]
GSE46602
[1]
GSE32571
[2]
GSE62872
[3]
GSE116918
[4]
GSE42913
[5]
GSE53902
resolved_accessions array · 6 items
[0]
GSE46602
[1]
GSE32571
[2]
GSE62872
[3]
GSE116918
[4]
GSE42913
[5]
GSE53902
statements array · 1 item
[0]
The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.
basis
The paper names GSE46602, GSE32571, GSE62872, GSE116918, GSE42913, GSE53902 in Gene Expression Omnibus and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
cox_ph
assets array · 2 items
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We used the clustered genes to establish a CIC-related gene prognostic index (CICGPI) through Cox regression analysis using PCa patients in TCGA.
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39259370", "kind": "science-record", "revision": 2, "content_hash": "sha256:c9cadbf8bf799ad818489540aa8765b9f5c5c9c9e8fdbfb67c2e67600140d21b", "created_at": "2026-11-01T20:29:24Z", "updated_at": "2026-11-01T21:33:52Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of circadian clock-related immunological prognostic index and molecular subtypes in prostate cancer.", "doi": "10.1007/s12672-024-01276-7", "pmid": "39259370", "authors": ["Lu Che", "Dengxiong Li", "Jie Wang", "Zhouting Tuo", "Koo Han Yoo", "Dechao Feng", "Yun Ou", "Ruicheng Wu", "Wuran Wei"], "journal": "Discover oncology", "year": 2024, "abstract": "BACKGROUND: Evidence suggests that the circadian clock (CIC) is among the important factors for tumorigenesis. We aimed to provide new insights into CIC-mediated molecular subtypes and gene prognostic indexes for prostate cancer (PCa) patients undergoing radical prostatectomy (RP) or radical radiotherapy (RT).\nMETHODS: PCa data from TCGA was analyzed to identify differentially expressed genes (DEGs) with significant fold changes and p-values. A prognostic index called CIC-related gene prognostic index (CICGPI) was developed through clustering methods and survival analysis and validated on multiple data sets. The diagnostic accuracy of CICGPI for resistance to chemotherapy and radiotherapy was confirmed. Additionally, the interaction between tumor immune environment and CICGPI score was explored, along with their correlation with prognosis.\nRESULTS: TOP2A, APOE, and ALDH2 were used to classify the PCa patients into two subtypes. Cluster 2 had a higher risk of biochemical recurrence (BCR) than cluster 1 for PCa patients undergoing RP or RT. A CIC-related gene prognostic index (CICGPI) was constructed using the above three genes for PCa patents in the TCGA database. The CICGPI score showed good prognostic value in the TCGA database and was externally confirmed by PCa patients in GSE116918, MSKCC2010 and GSE46602. In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988). For gene set variation analysis, we observed that both beta alanine metabolism and limonene and pinene degradation were upregulated in cluster 1 for PCa patients undergoing RP or RT. For PCa patients undergoing RP, cell cycle, homologous recombination, mismatch repair, and DNA replication were upregulated in cluster 2. A strongly positive relationship between cancer-related fibroblasts and CICGPI score was observed in PCa patients undergoing RP or RT. Moreover, a high density of CAFs was highly closely associated with poorer BCR-free survival of PCa patients.\nCONCLUSIONS: In this study, we established CIC-related immunological prognostic index and molecular subtypes, which might be useful for the clinical practice.", "claims": [{"id": "claim-method", "statement": "We used the clustered genes to establish a CIC-related gene prognostic index (CICGPI) through Cox regression analysis using PCa patients in TCGA.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39259370", "locator": "sections[3] \"Identifying key genes of molecular subtypes and prognostic index\" paragraph 3", "quote": "We used the clustered genes to establish a CIC-related gene prognostic index (CICGPI) through Cox regression analysis using PCa patients in TCGA."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39259370", "locator": "sections[15] \"notes\" paragraph 1", "quote": "The results showed here are in whole or part based upon data generated by the TCGA Research Network: https://www.cancer.gov/tcga."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39259370", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988).", "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:39259370"], "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:GSE116918"], "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:GSE116918"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39259370", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1007/s12672-024-01276-7", "sha256": 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