MétaCan
Menu
← Back to cohort
Record W4415273737 · doi:10.3389/fimmu.2025.1719075

Editorial: Community series in immune responses against tumors - from the bench to the bedside, volume II

2025· editorial· en· W4415273737 on OpenAlexaff
Chun Jing Wang, Shisan Bao

Bibliographic record

VenueFrontiers in Immunology · 2025
Typeeditorial
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsInstitute of Infection and Immunity
Fundersnot available
KeywordsImmune systemCancerImmune checkpointImmunotherapyAutoantibodyLung cancerBreast cancerAntibodyIpilimumab

Abstract

fetched live from OpenAlex

Reliable biomarkers continue to be pivotal in cancer management. Zhou et al. (2024a) analysed serum total immunoglobulin E (IgE) levels and lung cancer risk in a retrospective cohort of 675 patients and 1,193 healthy controls. Patients with lung cancer had significantly elevated IgE levels, with 47.9% above 100 IU/ml, which was associated with more advanced tumour stages, although progression-free and overall survival were not significantly different. This study identifies IgE as a possible diagnostic marker and draws attention to allergic immune pathways as therapeutic opportunities, particularly in older patients with smoking histories and altered monocyte counts [https://doi.org/10.3389/fimmu.2024.1637803 ].In a complementary study, Zhou et al. (2024b) examined a panel of seven tumour-associated autoantibodies (7-TAABs) in oesophageal squamous cell carcinoma (ESCC). The combined 7-TAAB assay improved sensitivity and diagnostic accuracy compared with single-antibody tests and was associated with clinical features such as tumour location, size, and TNM stage. These findings highlight how multi-antibody panels could support earlier detection and refined clinical risk assessment in ESCC [https://doi.org/10.3389/fimmu.2024.1518431 IF: 5.9 Q1 B2].A detailed characterisation of the tumour immune microenvironment (TIME) is essential for progress in immunotherapy. Guo et al. (2024) reviewed T cell subsets in cervical cancer, emphasising their functional diversity, spatial distribution, and interactions with other immune cells. Variations in T cell subsets across histological subtypes and disease stages influence anti-tumour responses and affect outcomes of therapies such as immune checkpoint blockade and HPV-directed vaccination [https://doi.org/10.3389/fimmu.2024.1612032 ]. Miranda et al. (2024) showed that local tumour ablation can trigger systemic immune effects, with cryoablation of primary breast cancers inducing an abscopal effect on distant lesions. These results suggest that combining local interventions with systemic immunomodulation can enhance anti-tumour responses, offering a promising route for combined therapeutic strategies [https://doi.org/10.3389/fimmu.2024.1498942 IF: 5.9 Q1 B2].Adding molecular depth, Zhou et al. ( 2025) investigated the CXCR7-TAGLN2 protein complex in papillary thyroid carcinoma (PTC), using an integrated approach that combined clinical tissue analysis, in vitro studies, and mechanistic experiments. Immunohistochemistry of 64 PTC and 24 benign thyroid tissues demonstrated markedly elevated CXCR7 and TAGLN2 expression, both of which were significantly linked to lymph node metastasis and positively correlated with each other. Co-localisation and co-immunoprecipitation assays confirmed their physical interaction. Functionally, silencing TAGLN2 suppressed PTC cell migration, while CXCR7 overexpression reversed this effect. Mechanistic studies revealed that TAGLN2 knockdown reduced phosphorylated Smad2 (p-Smad2) levels, implicating TAGLN2 in TGF-β/Smad2 pathway activity, while re-introduction of CXCR7 restored p-Smad2 expression. Together, these findings indicate that CXCR7 promotes invasion and metastasis through TAGLN2-mediated activation of TGF-β/Smad2 signalling. Identification of the CXCR7-TAGLN2 complex as a regulator of metastatic progression highlights a potential therapeutic target in PTC [https://doi.org/10.3389/fimmu.2025.1627419 ].Managing rare or complex cancers frequently requires personalised and multimodal approaches. Zeng et al. ( 2024) described a patient with synchronous lung adenocarcinoma and oesophageal squamous cell carcinoma who achieved survival beyond three years following chemotherapy, definitive chemoradiotherapy, stereotactic body radiation therapy (SBRT), and anti-PD-1 immunotherapy. This case illustrates the feasibility of integrating systemic and local therapies in patients with multiple primary malignancies while maintaining acceptable safety [https://doi.org/10.3389/fimmu.2024.1548176 ].Similarly, Xiong et al. ( 2024) reported a patient with advanced pulmonary large-cell neuroendocrine carcinoma (LCNEC) who received first-line chemotherapy followed by sovantinib and toripalimab. The patient achieved a partial response and a progression-free survival of 15.1 months, highlighting the promise of combining immune checkpoint inhibition with targeted agents in rare and aggressive cancers. Both cases reinforce the importance of tailoring therapeutic strategies to tumour biology and immune context [https://doi.org/10.3389/fimmu.2024.1527719 ].Vaccination approaches represent a growing frontier in cancer prevention and treatment. Asadollahi et al. ( 2024) designed a multi-neoepitope vaccine (MNEV) against non-small cell lung cancer (NSCLC) using reverse vaccinology and bioinformatics. In murine models, The studies in this volume reflect the breadth and depth of contemporary cancer immunology. From biomarker identification to mechanistic investigations, and from immune microenvironment analysis to innovative therapies and vaccines, these contributions demonstrate how molecular, cellular, and clinical perspectives converge to advance patient care. Mechanistic studies such as the CXCR7-TAGLN2 investigation show how dissecting specific molecular interactions can guide the rational development of targeted therapies and immune-based strategies. By linking molecular targets with immune-focused interventions and vaccine approaches, these works move experimental discoveries closer to clinical application and advance the goals of precision oncology.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.019
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.031
Threshold uncertainty score0.104

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.019
Meta-epidemiology (narrow)0.0050.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0040.002
Science and technology studies0.0020.003
Scholarly communication0.0070.006
Open science0.0040.002
Research integrity0.0120.016
Insufficient payload (model declined to judge)0.0310.020

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.007
GPT teacher head0.257
Teacher spread0.249 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEditorial

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueFrontiers in Immunology→Same topicCancer Immunotherapy and Biomarkers→French-language works237,207→