Evidence map›Paper›PMID 38248808›Full record

ReviewJournal of personalized medicine2024

Application of Single-Cell Sequencing Technology in Research on Colorectal Cancer.

Long Zhao, Quan Wang, Changjiang Yang, Yingjiang Ye, Zhanlong Shen

Abstract readReview
In one paragraph

Review in Journal of personalized medicine, 2024. 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
–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

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Review
  4. Review
  5. Review
  6. Review
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

5 authors.

Long ZhaoDepartment of Gastroenterological Surgery, Peking University People's Hospital, Beijing 100044, China.
Quan WangDepartment of Ambulatory Surgery Center, Xijing Hospital, Air Force Military Medical University, Xi'an 710032, China.
Changjiang YangDepartment of Gastroenterological Surgery, Peking University People's Hospital, Beijing 100044, China.ORCID 0000-0003-2414-2225
Yingjiang YeDepartment of Gastroenterological Surgery, Peking University People's Hospital, Beijing 100044, China.
Zhanlong ShenDepartment of Gastroenterological Surgery, Peking University People's Hospital, Beijing 100044, China.

Funding

National Natural Science Foundation of China 81972240National Natural Science Foundation of China 82272841
6 · The paper itself

Abstract

Colorectal cancer (CRC) is the third most prevalent and second most lethal cancer globally, with gene mutations and tumor metastasis contributing to its poor prognosis. Single-cell sequencing technology enables high-throughput analysis of the genome, transcriptome, and epigenetic landscapes at the single-cell level. It offers significant insights into analyzing the tumor immune microenvironment, detecting tumor heterogeneity, exploring metastasis mechanisms, and monitoring circulating tumor cells (CTCs). This article provides a brief overview of the technical procedure and data processing involved in single-cell sequencing. It also reviews the current applications of single-cell sequencing in CRC research, aiming to enhance the understanding of intratumoral heterogeneity, CRC development, CTCs, and novel drug targets. By exploring the diverse molecular and clinicopathological characteristics of tumor heterogeneity using single-cell sequencing, valuable insights can be gained into early diagnosis, therapy, and prognosis of CRC. Thus, this review serves as a valuable resource for identifying prognostic markers, discovering new therapeutic targets, and advancing personalized therapy in CRC.

Indexed as

colorectal cancerheterogeneityimmune microenvironmentsingle-cell sequencingtherapy

Identifiers

PMID38248808
PMCPMC10820918

What OpenQuestion holds

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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.