Evidence map›Paper›PMID 38975079›Full record

ArticleHeliyon2024

ScHGSC-IGDC: Identifying genes with differential correlations of high-grade serous ovarian cancer based on single-cell RNA sequencing analysis.

Yuanqi Li, Qi Wang, Xiao Zheng, Bin Xu, Wenwei Hu, Jinping Zhang, Xiangyin Kong, Yi Zhou, Tao Huang, You Zhou

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Heliyon, 2024. 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. A bioinformatics approach to identify potential biomarkers of high-grade ovarian cancer.Journal of the Turkish German Gynecological Association · 2026
    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

10 authors.

Yuanqi LiTumor Biological Diagnosis and Treatment Center, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China.
Qi WangTumor Biological Diagnosis and Treatment Center, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China.
Xiao ZhengTumor Biological Diagnosis and Treatment Center, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China.
Bin XuTumor Biological Diagnosis and Treatment Center, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China.
Wenwei HuJiangsu Engineering Research Center for Tumor Immunotherapy, Changzhou, 213003, China.
Jinping ZhangInstitutes of Biology and Medical Sciences, Soochow University, Suzhou, 215123, China.
Xiangyin KongCAS Key Laboratory of Tissue Microenvironment and Tumor, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
Yi ZhouTumor Biological Diagnosis and Treatment Center, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China.
Tao HuangBio-Med Big Data Center, Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, Shanghai, 200031, China.
You ZhouTumor Biological Diagnosis and Treatment Center, The Third Affiliated Hospital of Soochow University, Changzhou 213003, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Due to the high heterogeneity of ovarian cancer (OC), it occupies the main cause of cancer-related death among women. As the most aggressive and frequent subtype of OC, high-grade serous cancer (HGSC) represents around 70 % of all patients. With the booming progress of single-cell RNA sequencing (scRNA-seq), unique and subtle changes among different cell states have been identified including novel risk genes and pathways. Here, our present study aims to identify differentially correlated core genes between normal and tumor status through HGSC scRNA-seq data analysis. R package high-dimension Weighted Gene Co-expression Network Analysis (hdWGCNA) was implemented for building gene interaction networks based on HGSC scRNA-seq data. DiffCorr was integrated for identifying differentially correlated genes between tumor and their adjacent normal counterparts. Software Cytoscape was implemented for constructing and visualizing biological networks. Real-time qPCR (RT-qPCR) was utilized to confirm expression pattern of new genes. We introduced ScHGSC-IGDC (Identifying Genes with Differential Correlations of HGSC based on scRNA-seq analysis), an

Indexed as

Differential correlationHGSCSingle-cell sequencingWGCNA

Identifiers

PMID38975079
PMCPMC11226911

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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 Socratic graph.