AGRN (Agrin)

Tier 3 proteins, supported by logistic regression and MR only, included CD5L, GNPDA, ACAN, C7, DMP1, HEPH, CEACAM6, COX6B1, CPXM2 and IL12RB2. Druggability evaluation suggests that existing drugs targeting ITGB2, GP1BA, ACADSB and COX6B1 could potentially be repurposed for the treatment of specific lung cancer subtypes.

High-risk patients showed poorer overall survival (OS), distinct immune cell infiltration, elevated expression of KIR2DL1, LGALS9, TNFRSF18, and TNFRSF4, increased sensitivity to imatinib and axitinib, resistance to multiple chemotherapeutics, and reduced Fusobacteria and Tenericutes abundance. HAAO, ALDH2, and lymph node stage were identified as independent prognostic factors and were used to develop a predictive nomogram. We identified a Trp metabolism-associated fibroblast population in the ESCC tumor microenvironment (TME) and developed a five-gene TrpG signature for prognostic prediction in ESCC patients.

Two first-line anti-tumor drugs, Gemcitabine and Cisplatin, with higher sensitivity to individuals in group L were obtained. The q-PCR results of the feature genes were consistent with the results of gene expression in the database. This study established a 7-gene BM-related prognostic model that could be used to analyze the immune landscape of CRC individuals and predict their sensitivity to immunotherapy and chemotherapy. This research provided a reference for the prognosis prediction and immunotherapy of CRC individuals.

By integrating tumor volume from brain MRI scans with proteomic data, we identified potential intraventricular tumor burden markers for CNSL management, in particular LCP1. The study identified CSF-based proteomic biomarkers, particularly LCP1, that can classify MAIVC response and indicate tumor burden in CNSL patients.

Molecular docking simulations identified ligands with high binding affinity and stability, notably C5 and Hoechst 33258, which were prioritized for further validation and potential drug development. This study employs a novel diagnostic model for HNSCC constructed using machine learning technology, which can provide support for the early diagnosis of HNSCC and thus contribute to improving patient treatment plans and clinical management strategies.

Our results established a basement membrane-derived risk model for esophageal carcinoma and revealed the roles of the model genes in tumor progression regulation. This model advances prognostic stratification and provides insights into therapeutic targets.

IER3 and AGRN are critical components of the glycolysis pathway, driving CRC development and progression while also showing potential as biomarkers for predicting outcomes, diagnosing CRC, and serving as treatment targets.

Our findings elucidate the multi-target mechanism of action of carvacrol against HCC, providing a foundation for future research on its application in HCC management.

AGRN and its related genes may contribute to PNI by promoting tumor cell invasion and neural cell growth. Hence, AGRN may play a crucial role in the initiation and progression of PNI in colon cancer.

AGRN knockdown suppressed tumour growth and metastasis. Our study offers new perspectives on BMs in COCA, concluding that AGRN is a dependable biomarker for patient survival and prognosis.

In the pilot study, we found promising copy number variation biomarkers of bevacizumab response in FFPE samples of mCRC patients. The validation phase should be focused especially on the genes associated with angiogenesis (AGRN, MAPK8, ARHGAP22, LGALS13, LGALS4, ZFP36, and MYC), tumorigenesis (DVL1), and tumor proliferation (IFNL1, IFNL2, IFNL3, MAP3K10, and MAP4K1).

CRC patients with high-risk score more sensitive to docetaxel and rapamycin but resistance to gemcitabine and mitomycin. In the study, a PRGs-based prognostic model and a predictive model were constructed. These models are effective and robust in prediction the 1-, 3-, and 5-year survival of CRC patients.