Human NLRC4 expression promotes cancer survival and associates with type I interferon signaling and immune infiltration
Bibliographic record
Abstract
The immune system can control cancer progression. However, even though some innate immune sensors of cellular stress are expressed intrinsically in epithelial cells, their potential role in cancer aggressiveness and subsequent overall survival in humans is mainly unknown. Here, we show that nucleotide-binding oligomerization domain-like receptor (NLR) family CARD domain-containing 4 (NLRC4) is downregulated in epithelial tumor cells of patients with colorectal cancer (CRC) by using spatial tissue imaging. Strikingly, only the loss of tumor NLRC4, but not stromal NLRC4, was associated with poor immune infiltration (mainly DCs and CD4+ and CD8+ T cells) and accurately predicted progression to metastatic stage IV and decrease in overall survival. By combining multiomics approaches, we show that restoring NLRC4 expression in human CRC cells triggered a broad inflammasome-independent immune reprogramming consisting of type I interferon (IFN) signaling genes and the release of chemokines and myeloid growth factors involved in the tumor infiltration and activation of DCs and T cells. Consistently, such reprogramming in cancer cells was sufficient to directly induce maturation of human DCs toward a Th1 antitumor immune response through IL-12 production in vitro. In multiple human carcinomas (colorectal, lung, and skin), we confirmed that NLRC4 expression in patient tumors was strongly associated with type I IFN genes, immune infiltrates, and high microsatellite instability. Thus, we shed light on the epithelial innate immune sensor NLRC4 as a therapeutic target to promote an efficient antitumor immune response against the aggressiveness of various carcinomas.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".