CYP4B1 (Cytochrome P450 Family 4 Subfamily B Member 1)

CYP4B1 functions as a tumor suppressor in LUAD, regulated by NFIA and exerting its effects through PI3K/AKT/mTOR pathway inhibition. The CRRS and nomogram provide potential tools for prognosis assessment and offer guidance for precision therapy in LUAD, pending further translational validation.

Our findings highlight FAM83A as a potential prognostic biomarker with significant implications for treatment strategies, especially concerning immune modulation. This study offers a more comprehensive insight into the molecular differences in LUAD in smokers and non-smokers and lays the groundwork for targeted therapies tailored to these subgroups.

This study established a transcriptomic heterogeneity-resilient prognostic model for TNBC, enabling precise survival stratification and immune microenvironment assessment. The integrative nomogram and risk-guided therapeutic predictions address clinical challenges in TNBC management, advancing personalized treatment strategies.

Additionally, prolonged exposure to the tobacco carcinogen NNK suppressed 1p‑Enh activity and downregulated CYP4B1 expression. These findings demonstrate the critical role of the NNK‑induced 1p‑Enh/CYP4B1 regulatory axis in both HF and LC, suggesting that CYP4B1 may serve as a potential therapeutic target for the concurrent treatment of HF and LC.

In addition, their expression profiles and alterations were examined across various organs and cancer types. These findings underscore the potential relevance of these genes as predictive biomarkers and prospective therapeutic targets in specific cancer settings.

For the diagnosis of HNSC cancer, CYP4B1 and FMO2 could be potential biomarkers, while for the prognosis of HNSCC survival periods, ACOX2, CYP4F12, and ELOVL6 could hold valuable biomarker potential. The findings could help target the metabolism of FAs using the identified biomarkers for the design of new therapeutic opportunities for patients with HNSCC.

Moreover, the subgroup analysis indicated that the AI model that incorporated only the T3 clinical tumor stage yielded an AUC of 0.78. AI and transcriptome analysis can be used to create a risk model for predicting LNM, and it can enhance prediction accuracy and inform clinical staging and decision-making before surgery.

This study explores the potential of PD-1-modified multifunctional nanovesicles (NVs) loaded with 5-azacytidine (5-Aza) for NSCLC treatment...This synergistic strategy of epigenetic modulation and immune activation offers a promising avenue for NSCLC management. These findings contribute valuable insights into developing targeted nanotherapeutics for effective NSCLC treatment.

The prognostic model based on FAMGs demonstrates considerable potential for guiding diagnosis and clinical management of LUAD patients.

The RF model exhibited an accuracy of 0.97, a kappa value of 0.92, and an area under the curve (AUC) of 0.99 in the external validation dataset. The results of this study have identified biomarkers through ML algorithms which help to identify patients with CRC prone to metastasis.

Our study identified 6 key genes critical to ATC development and constructed a prognostic signature. These findings provide reliable biomarkers and a relatively comprehensive tumorigenesis profile of ATC, which may inform future strategies for clinical diagnosis and pharmaceutical design.

We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.