Evidence map›Paper›PMID 36923874›Full record

ArticleHeliyon2023

Paxillin is a potential prognostic biomarker associated with immune cell infiltration in ovarian cancer.

Li-Qun Meng, Ling-Yan Zhang, Wen-Zhi Xu

Open access · goldFull text read
In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.0field-weighted citation impact, top 23% 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

6 citing papers in PubMed, 6 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

3 authors at 2 institutions in 1 country.

Li-Qun MengOperating Room, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang, China.
Ling-Yan ZhangDepartment of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang, China.
Wen-Zhi XuDepartment of Obstetrics and Gynecology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, Zhejiang, China.
Zhejiang University · CNSir Run Run Shaw Hospital · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Objective: To investigate the expression, prognosis, and underlying mechanism of Paxillin (PXN) in ovarian cancer. Materials and methods: By comprehensive use of various bioinformatics tools, we analyzed the expression of PXN and its prognostic value in ovarian cancer. Then, the enrichment analyses were conducted to determine the possible regulatory pathways PXN involved in ovarian cancer. Finally, the associations of PXN expression with immune cell infiltration and immune checkpoints were analyzed. Results: PXN was highly expressed in ovarian cancer and its expression could independently predict the overall survival of ovarian cancer patients. More importantly, PXN had a superior ability in predicting long-term survival than age and tumor residual disease in ovarian cancer patients. In addition, PXN was positively related to adherens junction and tight junction pathways. Significant negative relationships between PXN expression and immune infiltrates were observed, however, PXN was positively connected with immune checkpoint (VSIR) in ovarian cancer. Conclusions: PXN serves as a reliable prognostic biomarker and may be a potent therapeutic target for ovarian cancer. Moreover, high PXN expression may affect ovarian cancer progression via positive regulation of metastasis-related pathways.

Indexed as

Immune cell infiltrationOvarian cancerPaxillinPrognostic biomarker

Identifiers

PMID36923874
PMCPMC10009461
OpenAlexW4322505669

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read24
identifiers read2
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.