Evidence map›Paper›PMID 37546388›Full record

ArticleFrontiers in oncology2023

Molecular characterization of colorectal adenoma and colorectal cancer via integrated genomic transcriptomic analysis.

Peng Pan, Jingnan Li, Bo Wang, Xiaoyan Tan, Hekun Yin, Yingmin Han, Haobin Wang, Xiaoli Shi, Xiaoshuang Li, Cuinan Xie and 5 more

Abstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.

0numbers the graph read from it
0cells of the map it votes in
8citing 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

8 citing papers in PubMed.

  1. Review
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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.

Peng PanDepartment of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China.
Jingnan LiDepartment of Gastroenterology, Peking Union Medical College Hospital, Beijing, China.
Bo WangDepartment of Science, Geneis Beijing Co., Ltd., Beijing, China.
Xiaoyan TanDepartment of Gastroenterology, Maoming People's Hospital, Maoming, China.
Hekun YinDepartment of Gastroenterology, Jiangmen Central Hospital, Jiangmen, China.
Yingmin HanDepartment of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China.
Haobin WangDepartment of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China.
Xiaoli ShiDepartment of Science, Geneis Beijing Co., Ltd., Beijing, China.
Xiaoshuang LiDepartment of Science, Geneis Beijing Co., Ltd., Beijing, China.
Cuinan XieDepartment of Science, Geneis Beijing Co., Ltd., Beijing, China.
Longfei ChenDepartment of Science, Geneis Beijing Co., Ltd., Beijing, China.
Lanyou ChenDepartment of Science, Geneis Beijing Co., Ltd., Beijing, China.
Yu BaiDepartment of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China.
Zhaoshen LiDepartment of Gastroenterology, Shanghai Changhai Hospital, Shanghai, China.
Geng TianDepartment of Bioinformatics, Boke Biotech Co., Ltd., Wuxi, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Colorectal adenoma can develop into colorectal cancer. Determining the risk of tumorigenesis in colorectal adenoma would be critical for avoiding the development of colorectal cancer; however, genomic features that could help predict the risk of tumorigenesis remain uncertain. Methods: In this work, DNA and RNA parallel capture sequencing data covering 519 genes from colorectal adenoma and colorectal cancer samples were collected. The somatic mutation profiles were obtained from DNA sequencing data, and the expression profiles were obtained from RNA sequencing data. Results: Despite some similarities between the adenoma samples and the cancer samples, different mutation frequencies, co-occurrences, and mutually exclusive patterns were detected in the mutation profiles of patients with colorectal adenoma and colorectal cancer. Differentially expressed genes were also detected between the two patient groups using RNA sequencing. Finally, two random forest classification models were built, one based on mutation profiles and one based on expression profiles. The models distinguished adenoma and cancer samples with accuracy levels of 81.48% and 100.00%, respectively, showing the potential of the 519-gene panel for monitoring adenoma patients in clinical practice. Conclusion: This study revealed molecular characteristics and correlations between colorectal adenoma and colorectal cancer, and it demonstrated that the 519-gene panel may be used for early monitoring of the progression of colorectal adenoma to cancer.

Indexed as

colorectal adenomacolorectal cancermonitormulti-omicsrandom forestscreening

Identifiers

PMID37546388
PMCPMC10401844

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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.