Evidence map›Paper›PMID 37178426›Full record

ArticleJournal of cancer research and clinical oncology2023

Molecular cluster mining of high-grade serous ovarian cancer via multi-omics data analysis aids precise medicine.

Daren Cai, Tiantian Liu, Jingya Fang, Yingbo Liu

Open access · greenAbstract read
In one paragraph

Article in Journal of cancer research and clinical oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.5field-weighted citation impact, top 28% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors at 1 institution in 1 country.

Daren CaiDepartment of Biostatistics, China Pharmaceutical University, Nanjing, China.
Tiantian LiuDepartment of Biostatistics, China Pharmaceutical University, Nanjing, China.
Jingya FangDepartment of Biostatistics, China Pharmaceutical University, Nanjing, China. jyfang1904@163.com.
Yingbo LiuDepartment of Biostatistics, China Pharmaceutical University, Nanjing, China. lyb11206@163.com.
China Pharmaceutical University · CN

Funding

Key R&D Program of Jiangsu Province (Social Development) BE2020694National Natural Science Foundation of China 81973145Natural Science Foundation of Jiangsu Province BK20190549
6 · The paper itself

Abstract

purposeHGSOC is a kind of gynecological cancer with high mortality and strong heterogeneity. The study used multi-omics and multiple algorithms to identify novel molecular subtypes, which can help patients obtain more personalized treatments.

methodsFirstly, the consensus clustering result was obtained using a consensus ensemble of ten classical clustering algorithms, based on mRNA, lncRNA, DNA methylation, and mutation data. The difference in signaling pathways was evaluated using the single-sample gene set enrichment analysis (ssGSEA). Meanwhile, the relationship between genetic alteration, response to immunotherapy, drug sensitivity, prognosis, and subtypes was further analyzed. Finally, the reliability of the new subtype was verified in three external datasets.

resultsThree molecular subtypes were identified. Immune desert subtype (CS1) had little enrichment in the immune microenvironment and metabolic pathways. Immune/non-stromal subtype (CS2) was enriched in the immune microenvironment and metabolism of polyamines. Immune/stromal subtype (CS3) not only enriched anti-tumor immune microenvironment characteristics but also enriched pro-tumor stroma characteristics, glycosaminoglycan metabolism, and sphingolipid metabolism. The CS2 had the best overall survival and the highest response rate to immunotherapy. The CS3 had the worst prognosis and the lowest response rate to immunotherapy but was more sensitive to PARP and VEGFR molecular-targeted therapy. The similar differences among three subtypes were successfully validated in three external cohorts.

conclusionWe used ten clustering algorithms to comprehensively analyze four types of omics data, identified three biologically significant subtypes of HGSOC patients, and provided personalized treatment recommendations for each subtype. Our findings provided novel views into the HGSOC subtypes and could provide potential clinical treatment strategies.

Indexed as

Ovarian NeoplasmsData AnalysisFemaleHumansMultiomicsPrecision MedicinePrognosisReproducibility of ResultsTumor MicroenvironmentHigh-grade serous ovarian cancerImmunotherapyMolecular classificationMulti-omics dataPrecise medicine

Identifiers

PMID37178426
PMCPMC11797266
OpenAlexW4376505586

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.