Targeting PERK and GRP78 in colorectal cancer: Genetic insights and novel therapeutic approaches
Bibliographic record
Abstract
Colorectal cancer (CRC) ranks among the leading causes of cancer-related deaths worldwide. Enhancing CRC diagnosis and prognosis requires the development of improved biomarkers and therapeutic targets. Emerging evidence suggests that the unfolded protein response (UPR) plays a pivotal role in CRC progression, presenting new opportunities for diagnosis, treatment, and prevention. This study hypothesizes that genetic variants in endoplasmic reticulum (ER) stress response genes influence CRC susceptibility. We examined the frequencies of SNPs in PERK (rs13045) and GRP78/BiP (rs430397) within a South Iranian cohort. We mapped the cellular and molecular features of PERK and GRP78 genes in colorectal cancer, observing their differential expressions in tumor and metastatic tissues. We constructed co-expression and protein-protein interaction networks and performed gene set enrichment analysis, highlighting autophagy as a significant pathway through KEGG. Furthermore, the study included 64 CRC patients and 60 control subjects. DNA extraction and genotyping were conducted using high-resolution melting (HRM) analysis. Significant differences in PERK and GRP78 expressions were observed between CRC tissues and controls. Variations in PERK and GRP78 genotypes were significantly correlated with CRC risk. Utilizing a Multi-Target Directed Ligands approach, a dual PERK/GRP78 inhibitor was designed and subjected to molecular modeling studies. Docking experiments indicated high-affinity binding between the proposed inhibitor and both genes, PERK and GRP78, suggesting a novel therapy for CRC. These findings highlight the importance of understanding genetic backgrounds in different populations to assess CRC risk. Polymorphisms in UPR signaling pathway elements may serve as potential markers for predicting CRC susceptibility, paving the way for personalized therapeutic strategies.
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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) | 0.001 | 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 | 0.001 |
| 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".