Evidence map›Paper›PMID 33165430›Full record

ArticleInternational journal of clinical and experimental pathology2020

Interactions between tumor mutation burden and immune infiltration in ovarian cancer.

Jing-Lin Mi, Meng Xu, Chang Liu, Ren-Sheng Wang

Open access · greenAbstract read
In one paragraph

Article in International journal of clinical and experimental pathology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
0.7field-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

7 citing papers in PubMed, 11 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

4 authors at 1 institution in 1 country.

Jing-Lin MiDepartment of Radiotherapy Oncology Clinical Medical Research Center, Guangxi Medical University Nanning, People's Republic of China.
Meng XuDepartment of Radiotherapy Oncology Clinical Medical Research Center, Guangxi Medical University Nanning, People's Republic of China.
Chang LiuDepartment of Radiotherapy Oncology Clinical Medical Research Center, Guangxi Medical University Nanning, People's Republic of China.
Ren-Sheng WangDepartment of Radiotherapy Oncology Clinical Medical Research Center, Guangxi Medical University Nanning, People's Republic of China.
Guangxi Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of the study was to evaluate the relationship between tumor mutational burden (TMB) and immune infiltration in ovarian cancer. We extracted somatic mutational data and gene expression profiles of ovarian cancer from The Cancer Genome Atlas (TCGA). The samples were separated into low and high TMB groups. Correlations between TMB and cancer prognosis were analyzed and immune cell infiltration in the high and low TMB subgroups was calculated using the CIBERSORT package software. High TMB was significantly related to an improved survival rate. We identified 4 TMB-related core genes that were significantly associated with prognosis. Furthermore, mutations in the 4 genes were associated with immune cell infiltration. We also found a high proportion of naive B cells and activated NK cells in the high TMB group, while increased proportions of memory B cells and plasma cells were found in the low TMB group. Overall, our study indicated that patients with a higher TMB level experienced a favorable survival outcome and this may influence immune infiltration in ovarian cancer. Furthermore, the 4 TMB-related core genes were highly correlated with prognosis and the level of immune cell infiltration.

Indexed as

CIBERSORTimmune infiltrationovarian cancerprognosisTCGATumor mutation burden

Identifiers

PMID33165430
PMCPMC7642696
OpenAlexW3099956993

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.