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Record W6977052470 · doi:10.60692/v6w0n-7ke55

Tumor-infiltrating lymphocyte: features and prognosis of lymphocytes infiltration on colorectal cancer

2023· article· en· W6977052470 on OpenAlexaff

Bibliographic record

VenueGreater South Information System · 2023
Typearticle
Languageen
FieldEngineering
TopicPhysics and Engineering Research Articles
Canadian institutionsMcMaster University
Fundersnot available
KeywordsTumor-infiltrating lymphocytesColorectal cancerCD8Infiltration (HVAC)Tumor microenvironmentLymphocyteLymphocytic infiltrationGeneT lymphocyte

Abstract

fetched live from OpenAlex

Tumor-infiltrating lymphocytes (TILs) are vital elements of the tumor microenvironment (TME), and the anti-tumor activity of TILs on colorectal cancer (CRC) has been a topic of concern. However, the characteristics and prognosis of the various types of lymphocyte infiltration in CRC have not been fully explained. Our study aimed to identify distinct features and prognosis of TILs. We integrated multiple-cohort databases to illustrate the features, proportions, and prognosis of TILs on CRC. We found that macrophages were significantly enriched in CRC. When we used the scRNA-seq database to further evaluate the proportion of TILs, we noticed markedly higher numbers of CD4 + T cell, B cell, and CD8 + T cell in four Gene Expression Omnibus Series (GSE) CRC cohorts. Interestingly, we found that the infiltrating level of TIL subgroups from highest to lowest is always dendritic cells, CD8 + T cells, CD4 + T cells, neutrophils, B cells, and macrophages; the proportion of infiltration is largely constant regardless of mutations in specific genes or somatic copy number variation (sCNV). In addition, the data corroborated that CD4+ TILs and CD8+ TILs have certain application values in the prognosis of CRCs, and age negatively related to CD8+ TILs and B plasma infiltration. Finally, patients with CRC who are older than 70 years have a better response to immune-checkpoint blockade.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.182
Threshold uncertainty score0.422

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.018
GPT teacher head0.210
Teacher spread0.193 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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
Published2023
Admission routes1
Has abstractyes

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