Evidence map›Paper›PMID 41644592›Full record

ArticleScientific reports2026

Metabolomic and transcriptomic analyses identify metabolic alterations and immune suppression in ovarian cancer.

Maiko Yamaguchi, Daiki Higuchi, Hiroshi Yoshida, Erisa Fujii, Mayumi Kobayashi Kato, Kengo Hiranuma, Yuka Asami, Hanako Ono, Takafumi Koyama, Masaaki Komatsu and 10 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
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

1 citing paper in PubMed.

  1. Review
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

20 authors.

Maiko YamaguchiDivision of Genome Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Daiki HiguchiDivision of Genome Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Hiroshi YoshidaDepartment of Diagnostic Pathology, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Erisa FujiiDivision of Genome Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Mayumi Kobayashi KatoDivision of Genome Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Kengo HiranumaDivision of Genome Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Yuka AsamiDivision of Genome Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Hanako OnoDepartment of Clinical Genomics, National Cancer Center Research Institute, Tokyo, 1004-0045, Japan.
Takafumi KoyamaDepartment of Experimental Therapeutics, National Cancer Center Hospital, Tokyo, 104-0045, Japan.
Masaaki KomatsuDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
Ryuji HamamotoDivision of Medical AI Research and Development, National Cancer Center Research Institute, Tokyo, 104-0045, Japan.
Koji MatsumotoDepartment of Obstetrics and Gynecology, Showa Medical University, Tokyo, 142-8666, Japan.
Akihiko SekizawaDepartment of Obstetrics and Gynecology, Showa Medical University, Tokyo, 142-8666, Japan.
Yasuhisa TeraoDepartment of Obstetrics and Gynecology, Faculty of Medicine, Juntendo University, Tokyo, 113-8421, Japan.
Atsuo ItakuraDepartment of Obstetrics and Gynecology, Faculty of Medicine, Juntendo University, Tokyo, 113-8421, Japan.
Takashi KohnoDivision of Genome Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
Tomoyasu KatoDepartment of Gynecology, National Cancer Center Hospital, Tsukiji 5-1-1, Chuo-ku, Tokyo, 104-0045, Japan.
Hideki MakinoshimaTsuruoka Metabolomics Laboratory, National Cancer Center Tsuruoka, Yamagata, 997-0052, Japan.
Mitsuya IshikawaDepartment of Gynecology, National Cancer Center Hospital, Tsukiji 5-1-1, Chuo-ku, Tokyo, 104-0045, Japan. miishika@ncc.go.jp.
Kouya ShiraishiDivision of Genome Biology, National Cancer Center Research Institute, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan. kshirais@ncc.go.jp.

Funding

Japan Agency for Medical Research and Development 23ama221520h0001Japan Society for the Promotion of Science 20H03668National Cancer Center Research and Development Fund 2022-A-20
6 · The paper itself

Abstract

Reprogramming of cellular metabolism is a hallmark of cancer, particularly ovarian cancer (OC), that contributes to rapid cancer growth and survival. However, studies using clinical specimens are limited. To identify metabolic alterations specific to OC, we performed metabolomic analysis of OC and benign ovarian tumors. The relationship between metabolomics and transcriptomics was investigated by transcriptome analysis. Fifty-one patients with OC and three with benign ovarian tumors, diagnosed between 2011 and 2014 using available frozen tissue and plasma specimens, were enrolled at the National Cancer Center Hospital. To identify metabolic alterations, plasma samples from 51 patients with OC and three with benign tumors, along with both cancerous and non-cancerous tissue samples from 44 of the 51 patients with OC, were analyzed using gas chromatography-mass spectrometry. In addition, we performed transcriptomic analysis of cancerous tissues obtained from 39 of the 44 patients with OC. It was not possible to classify patients based on plasma metabolite levels; therefore, the 44 patients with OC were classified into two groups based on metabolite levels: high and low, based on tissue analysis. The group with high metabolite levels had more advanced-stage tumors (P = 0.02). Transcriptome pathway analysis revealed suppression of pathways related to natural killer (NK) cells and immune responses in the group with high metabolite levels. NK cell percentages were lower in the group with high metabolite levels than in the group with low metabolite levels (P = 0.04). Thus, the group with high metabolite levels was associated with advanced stages and a reduced fraction of NK cells, suggesting that high metabolite levels may play a direct or indirect role in immune activity or in the malignant progression of OC.

Indexed as

Gene Expression ProfilingMetabolomeMetabolomicsOvarian NeoplasmsTranscriptomeAdultAgedFemaleGene Expression Regulation, NeoplasticHumansKiller Cells, NaturalMiddle AgedImmune suppressionMetabolic alterationMetabolomic analysisOvarian cancerTranscriptomic analysis

Identifiers

PMID41644592
PMCPMC12929718

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Registered trials

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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.