Evidence map›Paper›PMID 42149255›Full record

ReviewDiscover oncology2026

Peptides as integrative modulators for clinical prognosis and targeted therapy in pancreatic cancer.

Bhawana Yadav, Shiva Prasad Kollur, Amena Ali, Payas Arora, Aanika Gupta, P Saranraj, Vijay Jagdish Upadhye, Pallavi Singh

Abstract readReview
In one paragraph

Review in Discover oncology, 2026. 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

8 authors.

Bhawana YadavDepartment of Biotechnology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, 248002, India.
Shiva Prasad KollurSchool of Physical Sciences, Amrita Vishwa Vidyapeetham, Mysuru Campus, Mysuru, Karnataka, 570026, India. shivaprasad.k@myamrita.edu.in.
Amena AliDepartment of Biotechnology, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, 201309, India.
Payas AroraDepartment of Biotechnology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, 248002, India.
Aanika GuptaDepartment of Biotechnology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, 248002, India.
P SaranrajPG and Research Department of Microbiology, Sacred Heart College (Autonomous), Tirupattur, Tamil Nadu, 635601, India.
Vijay Jagdish UpadhyeResearch and Development cell, Department of Microbiology, Parul Institute of Applied Sciences (PIAS), Parul University, Vadodara, Gujarat, 391760, India. vijay.upadhye35296@paruluniversity.ac.in.
Pallavi SinghDepartment of Biotechnology, Graphic Era (Deemed to be University), Dehradun, Uttarakhand, 248002, India. pallavisingh.bt@geu.ac.in.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pancreatic cancer remains a formidable global health challenge, ranking as the twelfth most common malignancy yet claiming an outsized toll with dismal 5-year survival rates 13.3%, largely due to late-stage diagnosis and limited therapeutic options. This comprehensive review delves into the transformative potential of peptides as innovative biomarkers and prognostic indicators, addressing the critical gaps in early detection and personalized management of this aggressive disease. Drawing from cutting-edge research in molecular oncology, proteomics, and bioinformatics, this paper highlights key genetic drivers such as KRAS, TP53, CDKN2A, and SMAD4 mutations that fuel Pancreatic cancer progression and stromal desmoplasia. Diverse peptide sources, including tumor-derived neoantigens, stromal remodeling fragments, and immune-modulating signals and their release mechanisms via ectodomain shedding and exosomes, were explored. Emphasizing advanced discovery pipelines, from mass spectrometry-based proteomics to immunoassays and machine learning-driven validation, current work spotlights promising candidates like PF4, PRO-C11-511, and CXCL7, which enhance diagnostic accuracy (AUC up to 0.961) and predict outcomes when integrated with established markers like CA19-9. By overcoming limitations of current biomarkers, such as low specificity and stage-dependency. This review underscores peptides' role in revolutionizing precision oncology, paving the way for non-invasive screening, targeted therapies, and improved survival in pancreatic cancer patients.

Indexed as

CA19-9Chemotherapy responseEarly detectionKRAS mutationsMachine learningMolecular profilingPancreatic cancerPeptide biomarkersTumor microenvironment

Identifiers

PMID42149255
PMCPMC13350784

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.