ArticleFrontiers in immunology2026
NECSO-based classification predicts immunotherapy efficacy and identifies FLAD1 as therapeutic target in kidney renal clear cell carcinoma.
Article in Frontiers in immunology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
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.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: A new type of regulated cell death known as Necrosis by Sodium Overload (NECSO) has been discovered recently. There is growing evidence indicating that NECSO is essential in both anti-tumor immune responses and the proliferation of cancer cells. Nonetheless, the underlying mechanisms and clinical relevance of NECSO are still not well understood, especially regarding its prognostic significance in kidney renal clear cell carcinoma (KIRC). Methods: We utilized Non-negative Matrix Factorization (NMF) to distinguish unique NECSO patterns derived from NECSO-associated genes within the TCGA dataset, which led to the identification of three distinct subgroups. Furthermore, we created an innovative NECSO score (NECSOS) utilizing machine learning techniques and confirmed its clinical relevance through validation in several independent datasets, which comprised one transcriptomic cohort of immune checkpoint inhibitor (ICI)-treated KIRC patients, three pan-cancer ICI-treated cohorts, two single-cell RNA sequencing datasets of KIRC patients, and one single-cell dataset from patients treated with PD-1 inhibitors. To characterize FLAD1, we performed gain- and loss-of-function assays for proliferation, migration, and invasion, sodium overload (NC1) sensitivity assays, subcutaneous xenograft models, and CD8+ T cell co-culture cytokine profiling. Results: The genetic landscape and immune microenvironment of these subgroups were thoroughly characterized, uncovering important insights into the heterogeneity of the tumor microenvironment (TME) and its response to immunotherapy. The NECSO model we developed exhibited strong predictive accuracy for prognosis and immunotherapy responses in patients with KIRC, with validation conducted in diverse pan-cancer ICI cohorts. FLAD1 was identified as a novel prognostic biomarker, and its oncogenic roles in proliferation, migration, and invasion were experimentally confirmed. Mechanistically, FLAD1 regulated cellular sensitivity to TRPM4-mediated sodium overload, promoted tumor growth Discussion: This study has established a robust NECSO-based classification system and prognostic model for KIRC while identifying FLAD1 as a novel biomarker and functional driver of the necrosis-immunity axis. These integrated approaches provide clinically actionable tools for predicting patient outcomes and immunotherapy efficacy.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.