Characterization and comparison of two NPC cell lines, C17 and C666-1, as models for studying the pathogenesis of nasopharyngeal carcinoma
Bibliographic record
Abstract
Nasopharyngeal carcinoma (NPC) is a malignant tumor that originates from the epithelial cells of the nasopharynx. NPC is closely linked to Epstein-Barr virus (EBV) infection, which necessitates the use of EBV-positive cell lines for accurate pathology studies. In this paper, we present a detailed comparison of the C666-1 and C17 cell lines using bulk RNA-sequencing (RNA-Seq) methods. By thoroughly examining the gene expression profiles of these cell lines, we aim to elucidate the molecular mechanisms that drive NPC progression and metastasis. Understanding these mechanisms is crucial for developing effective treatment strategies. Cancer cell line models are essential in this research, as they provide a controlled environment for studying the complex interplay between viral and host cellular factors. Additionally, our study highlights the differences between the two cell lines, which could be pivotal in designing new experiments and tailoring therapeutic approaches.
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 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".