Evidence map›Paper›PMID 38910429›Full record

ReviewCurrent protein & peptide science2024

Exploring the Role of Unconventional Post-Translational Modifications in Cancer Diagnostics and Therapy.

Sayan Sharma, Oindrila Sarkar, Rajgourab Ghosh

Abstract readReview
PubMed Publisher
In one paragraph

Review in Current protein & peptide science, 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. 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

3 authors.

Sayan SharmaDepartment of Biotechnology, Amity University Kolkata, AIBNK, Kolkata, West Bengal, India.
Oindrila SarkarDepartment of Biotechnology, Amity University Kolkata, AIBNK, Kolkata, West Bengal, India.
Rajgourab GhoshDepartment of Biotechnology, Amity University Kolkata, AIBNK, Kolkata, West Bengal, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Unconventional Post-Translational Modifications (PTMs) have gained increasing attention as crucial players in cancer development and progression. Understanding the role of unconventional PTMs in cancer has the potential to revolutionize cancer diagnosis, prognosis, and therapeutic interventions. These modifications, which include O-GlcNAcylation, glutathionylation, crotonylation, including hundreds of others, have been implicated in the dysregulation of critical cellular processes and signaling pathways in cancer cells. This review paper aims to provide a comprehensive analysis of unconventional PTMs in cancer as diagnostic markers and therapeutic targets. The paper includes reviewing the current knowledge on the functional significance of various conventional and unconventional PTMs in cancer biology. Furthermore, the paper highlights the advancements in analytical techniques, such as biochemical analyses, mass spectrometry and bioinformatic tools etc., that have enabled the detection and characterization of unconventional PTMs in cancer. These techniques have contributed to the identification of specific PTMs associated with cancer subtypes. The potential use of Unconventional PTMs as biomarkers will further help in better diagnosis and aid in discovering potent therapeutics. The knowledge about the role of Unconventional PTMs in a vast and rapidly expanding field will help in detection and targeted therapy of cancer.

Indexed as

Biomarkers, TumorNeoplasmsProtein Processing, Post-TranslationalAnimalsHumansSignal TransductionBiomarkers, Tumorbiomarkerscancermass- spectrometryproteins.therapeuticsUnconventional post-translational modifications

Identifiers

What OpenQuestion holds

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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.