Evidence map›Paper›PMID 34795577›Full record

ArticleFrontiers in pharmacology2021

Clinical Significance of Screening Differential Metabolites in Ovarian Cancer Tissue and Ascites by LC/MS.

Miao Liu, Yu Liu, Hua Feng, Yixin Jing, Shuang Zhao, Shujia Yang, Nan Zhang, Shi Jin, Yafei Li, Mingjiao Weng and 5 more

Open access · goldAbstract read
In one paragraph

Article in Frontiers in pharmacology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
1.5field-weighted citation impact, top 17% 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

11 citing papers in PubMed, 22 citations in OpenAlex.

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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

15 authors at 2 institutions in 1 country.

Miao LiuDepartment of Pathology, Harbin Medical University, Harbin, China.
Yu LiuDepartment of Pathology, Harbin Medical University, Harbin, China.
Hua FengDepartment of Pathology, Harbin Medical University, Harbin, China.
Yixin JingDepartment of Pathology, Harbin Medical University, Harbin, China.
Shuang ZhaoDepartment of Gynecology, Tumor Hospital of Harbin Medical University, Harbin, China.
Shujia YangDepartment of Pathology, Harbin Medical University, Harbin, China.
Nan ZhangDepartment of Pathology, Harbin Medical University, Harbin, China.
Shi JinDepartment of Pathology, Harbin Medical University, Harbin, China.
Yafei LiDepartment of Pathology, Harbin Medical University, Harbin, China.
Mingjiao WengDepartment of Pathology, Harbin Medical University, Harbin, China.
Xinzhu XueDepartment of Pathology, Harbin Medical University, Harbin, China.
Fuya WangDepartment of Gynecology, Tumor Hospital of Harbin Medical University, Harbin, China.
Yongheng YangDepartment of Pathology, Harbin Medical University, Harbin, China.
Xiaoming JinDepartment of Pathology, Harbin Medical University, Harbin, China.
Dan KongDepartment of Gynecology, Tumor Hospital of Harbin Medical University, Harbin, China.
Harbin Medical University · CNThird Affiliated Hospital of Harbin Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Tumor cells not only show a vigorous metabolic state, but also reflect the disease progression and prognosis from their metabolites. To judge the progress and prognosis of ovarian cancer is generally based on the formation of ascites, or whether there is ascites recurrence during chemotherapy after ovarian cancer surgery. To explore the relationship between the production of ascites and ovarian cancer tissue, metabolomics was used to screen differential metabolites in this study. The significant markers leading to ascites formation and chemoresistance were screened by analyzing their correlation with the formation of ascites in ovarian cancer and the clinical indicators of patients, and then provided a theoretical basis. The results revealed that nine differential metabolites were screened out from 37 ovarian cancer tissues and their ascites, among which seven differential metabolites were screened from 22 self-paired samples. Sebacic acid and 20-COOH-leukotriene E4 were negatively correlated with the high expression of serum CA125. Carnosine was positively correlated with the high expression of serum uric acid. Hexadecanoic acid was negatively correlated with the high expression of serum γ-GGT and HBDH. 20a,22b-Dihydroxycholesterol was positively correlated with serum alkaline phosphatase and γ-GGT. In the chemotherapy-sensitive and chemotherapy-resistant ovarian cancer tissues, the differential metabolite dihydrothymine was significantly reduced in the chemotherapy-resistant group. In the ascites supernatant of the drug-resistant group, the differential metabolites, 1,25-dihydroxyvitamins D3-26, 23-lactonel and hexadecanoic acid were also significantly reduced. The results indicated that the nine differential metabolites could reflect the prognosis and the extent of liver and kidney damage in patients with ovarian cancer. Three differential metabolites with low expression in the drug-resistant group were proposed as new markers of chemotherapy efficacy in ovarian cancer patients with ascites.

Indexed as

differential metaboliteslipid metabolismmetabonomicsovarian cancer and ascitestumor markers

Identifiers

PMID34795577
PMCPMC8593816
OpenAlexW3210760536

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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.