Evidence map›Paper›PMID 39717781›Full record

ArticleFrontiers in immunology2024

Integrating necroptosis into pan-cancer immunotherapy: a new era of personalized treatment.

Yan Gao, Sheng Chen, Lei Li

Abstract read
In one paragraph

Article in Frontiers in immunology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. 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

3 authors.

Yan GaoDepartment of Respiratory and Critical Care Medicine, The Affiliated Huai'an Hospital of Xuzhou Medical University, Huai'an, China.
Sheng ChenDepartment of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.
Lei LiDepartment of Thoracic Surgery, The Affiliated Huaian No.1 People's Hospital of Nanjing Medical University, Huai'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Necroptosis has emerged as a promising biomarker for predicting immunotherapy responses across various cancer types. Its role in modulating immune activation and therapeutic outcomes offers potential for precision oncology. Methods: A comprehensive pan-cancer analysis was performed using bulk RNA sequencing data to develop a necroptosis-related gene signature, termed Necroptosis.Sig. Multi-omics approaches were employed to identify critical pathways and key regulators of necroptosis, including HMGB1. Functional validation experiments were conducted in A549 lung cancer cells to evaluate the effects of HMGB1 knockdown on tumor proliferation and malignancy. Results: The Necroptosis.Sig gene signature effectively predicted responses to immune checkpoint inhibitors (ICIs). Multi-omics analyses highlighted HMGB1 as a key modulator of necroptosis, with potential to enhance immune activation and therapeutic efficacy. Functional experiments demonstrated that HMGB1 knockdown significantly suppressed tumor proliferation and malignancy, reinforcing the therapeutic potential of targeting necroptosis. Discussion: These findings underscore the utility of necroptosis as a biomarker to guide personalized immunotherapy strategies. By advancing precision oncology, necroptosis provides a novel avenue for improving cancer treatment outcomes.

Indexed as

ImmunotherapyNecroptosisNeoplasmsPrecision MedicineA549 CellsAnimalsBiomarkers, TumorCell Line, TumorCell ProliferationGene Expression Regulation, NeoplasticHMGB1 ProteinHumansImmune Checkpoint InhibitorsMiceBiomarkers, TumorHMGB1 ProteinHMGB1 protein, humanImmune Checkpoint Inhibitorsimmune microenvironmentimmunotherapymachine learningnecroptosispan-cancer analysis

Identifiers

PMID39717781
PMCPMC11664130

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.