Contemporaneous Perioperative Inflammatory and Angiogenic Cytokine Profiles of Surgical Breast, Colorectal, and Prostate Cancer Patients: Clinical Implications
Bibliographic record
Abstract
Surgery-induced tumor growth acceleration and synchronous metastatic growth promotion have been observed for decades. Surgery-induced wound healing, orchestrated through growth factors, chemokines, and cytokines, can negatively impact patients harboring residual or metastatic disease. We provide detailed clinical evidence of this process in surgical breast, prostate, and colorectal cancer patients. Plasma samples were analyzed from 68 cancer patients who had not received treatment before surgery or adjuvant therapy until at least four weeks post-surgery. The levels of plasma cytokines, chemokines, and growth factors were simultaneously quantified and profiled using multiplexed immunoassays for eight time points sampled per patient. The immunologic processes are induced immediately after surgery in patients, characterized by a drastic short-term shift in the expression levels of pro-inflammatory and angiogenic molecules and cytokines. A rapid and significant spike in circulating plasma levels of hepatocyte growth factor (HGF), interleukin-6 (IL-6), placental growth factor (PLGF), and matrix metalloproteinase-9 (MMP-9) after surgery was noted. The rise in these molecules was concomitant with a significant drop in transforming growth factor-β1 (TGF-β1), platelet-derived growth factor (PDGF-AB/BB), insulin-like growth factor-1 (IGF-1), and monocyte chemoattractant protein-2 (MCP-2). If not earlier, each plasma analyte was normalized to baseline levels within 1-2 weeks after surgery, suggesting that surgical intervention alone was responsible for these effects. The effects of surgical tumor removal on disrupting the pro-inflammatory and angiogenic plasma profiles of cancer patients provide evidence for potentiating malignant progression. Our findings indicate a narrow therapeutic window of opportunity after surgery to prevent disease recurrence.
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 | 0.001 |
| 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".