CD155 Is a Potential Biomarker in Basal Cell Carcinoma
Bibliographic record
Abstract
To the Editor: I am writing to submit a letter regarding the potentially important role of CD155 in basal cell carcinoma (BCC). CD155 has been found to play important roles in cancer and immunological fields. CD155, formerly identified as the poliovirus receptor and later as part of the nectin and nectin-like family,1 is a surface protein expressed mostly on normal and transformed malignant cells. Although CD155 is ubiquitously expressed in various tissues, many human tumors significantly upregulate its expression. Notably, the expression of CD155 has been found to be elevated across many types of cancer.2 Interestingly, the protein uses an alternative splicing mechanism, producing 4 isoforms, 2 being soluble (CD155β and CD155γ) and 2 being transmembrane protein (CD155α and CD155δ).3 Human tissues also express soluble isoforms of CD155 (sCD155) that lack the transmembrane region. Recent studies have shown that sCD155 levels are significantly higher in the sera of patients with cancer than in healthy donors, suggesting that sCD155 may serve as a potential biomarker for cancer development and progression.4 The role of CD155 is multifaceted, involving the following 3 receptors: DNAM-1, CD96, and TIGIT. Interestingly, whereas DNAM-1 activates the cytotoxic activity of T cells against tumors, TIGIT suppresses the cytotoxic activity of T cells against CD155-expressing tumor cells. However, TIGIT inhibits the function of DNAM-1. Consequently, the overall balance seems to favor TIGIT and the subsequent suppression of antitumor immunity.5 This intricate interplay between CD155 and its receptors underscores the complexity of immune regulation within the tumor microenvironment. The TIGIT–CD155 pathway is a novel MHC-I–independent education mechanism for cell tolerance and inactivation of NK and T-cell receptor-mediated signaling, making it an important and emergent immune checkpoint (Fig. 1).6FIGURE 1.: The role of TIGIT as an immune checkpoint in the tumor microenvironment. The image shows TIGIT in addition to other common immune checkpoints. TIGIT serves as a ligand that binds to its receptor, CD155, thereby inhibiting the antitumor activities of T cells and allowing cancer cells to proliferate, survive, and escape immune detection. Created with BioRender.It has been found that the expression of the protein CD155 is determined by components of the Sonic hedgehog pathway (SHH) (Fig. 2),7 and the Ras–MEK–ERK pathway,8 which have been associated with the canonical and noncanonical SHH pathways, respectively. Overactivation of SHH (canonical and noncanonical) pathways drives BCC oncogenesis.9 Altogether, evidence suggests that CD155 would be augmented in the BCC serum of patients. Preliminary data from a study conducted in our institution showed that sCD155 levels are statistically significantly elevated in patients with BCC compared with healthy controls.FIGURE 2.: The Hedgehog signaling pathway (SHH) is shown in the diagram. (Left) Under normal conditions, PTCH1 inhibits SMO and no signals are sent to the nucleus to promote transcription. (Right) However, in the presence of either its ligand SHH or mutated PTCH1, SMO is derepressed and can activate SUFU/GLI1, promoting the transcription of target genes, including CD155. Created with BioRender.Given the importance of immune checkpoints in cancer and the emerging evidence surrounding the role of CD155 in skin cancer, future studies should investigate the potential of CD155 as a biomarker of BCC. Thus, a comprehensive investigation into the potential interaction between SHH and CD155, as well as whether CD155 plays a role alongside SHH in the oncogenesis of BCC, has the potential to significantly advance our understanding of this cancer. These research endeavors have the potential to improve patient diagnosis and follow-up strategies, ultimately leading to enhanced patient outcomes. Thank you for considering my comments. Sincerely, Jesús Iván Martínez Ortega.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".