Two Enzymes May Explain Why Some Smokers Develop Lung Cancer

Smoking remains the dominant environmental cause of lung cancer worldwide, yet the biological reasons why some smokers develop tumors while others with similar exposure histories do not have remained frustratingly opaque. A new integrative study published in the journal 3 Biotech points to two familiar enzymes as potential central players in this puzzle. By combining transcriptomic profiling, machine learning, genetic causal inference, single-cell analysis, molecular docking, and laboratory validation, a research team led by Xiaopeng Yu and Zuomei He of the Hunan Provincial Hospital of Integrated Traditional Chinese and Western Medicine has identified CYP1A1 and CYP1B1, two cytochrome P450 enzymes, as candidate drivers of smoking-related lung carcinogenesis and as possible biomarkers for pinpointing smokers at elevated risk.

The investigation began with a deceptively simple comparative design. The researchers assembled publicly available transcriptomic datasets from three groups: smokers with lung cancer, smokers without lung cancer, and never-smokers who nevertheless developed the disease. This tripartite structure allowed them to isolate molecular signatures that are specifically tied to the interaction between smoking exposure and cancer development, rather than signatures that merely reflect the presence of a tumor. Differential expression analysis flagged genes whose activity changed across the groups, and a complementary technique called weighted gene co-expression network analysis, or WGCNA, grouped thousands of genes into modules whose coordinated activity correlated with smoking status and disease state.

From this convergence of approaches, the team distilled a set of 16 smoking-specific lung cancer-related genes. Functional enrichment analysis revealed that these genes cluster heavily in pathways devoted to cytochrome P450-mediated xenobiotic metabolism, oxidoreductase activity, and chemical carcinogenesis. In plain terms, the signature is dominated by the cellular machinery that metabolizes foreign chemicals, the very machinery that tobacco smoke confronts with each puff. This finding is biologically coherent: cytochrome P450 enzymes are double-edged swords in toxicology, capable of detoxifying harmful compounds but also of converting procarcinogens in tobacco smoke into reactive metabolites that can damage DNA and initiate the mutational cascade leading to cancer.

To move from correlation toward a more discriminating molecular signature, the researchers turned to machine learning algorithms trained to distinguish lung cancer samples from controls based on gene expression patterns. The analysis converged on a compact four-gene classifier comprising CYP1A1, CYP1B1, HLA-DRA, and IDO1. Within this signature, CYP1A1 and CYP1B1 made substantial contributions to the classification of lung cancer, suggesting that the expression levels of these two enzymes carry considerable diagnostic information. HLA-DRA, a major histocompatibility complex class II gene involved in antigen presentation, and IDO1, an enzyme that catabolizes tryptophan and modulates immune responses, add immune dimensions to the signature, hinting that smoking-related lung carcinogenesis involves both carcinogen metabolism and immunological remodeling.

One of the most consequential steps in the study was the application of Mendelian randomization, a statistical technique that uses naturally occurring genetic variants as instruments to probe whether an exposure-gene relationship is causal rather than merely associative. Drawing on genetic data resources including the GWAS Catalog and the eQTLGen Consortium, the team found that genetically predicted expression levels of CYP1A1 and CYP1B1 were positively associated with lung cancer risk. Because genetic variants are randomly allocated at conception in a manner analogous to a randomized trial, this result strengthens the argument that higher expression of these two enzymes is not simply a downstream consequence of the disease but may actively contribute to carcinogenesis.

Single-cell transcriptomic analysis added a spatial dimension to the story. By examining gene expression at the resolution of individual cells, the researchers discovered that CYP1A1 and CYP1B1 are predominantly expressed in epithelial cells, the cell type that lines the airways and gives rise to the vast majority of lung cancers. This cellular localization is significant because it places the enzymes at the very front line of tobacco smoke exposure, where inhaled carcinogens first contact tissue. Epithelial cells bearing elevated levels of these metabolic enzymes would be positioned to both encounter and bioactivate tobacco carcinogens, a combination that could accelerate the accumulation of oncogenic mutations.

Molecular docking studies provided a structural hypothesis for how these enzymes interact with tobacco-related carcinogens. Docking is a computational method that predicts how small molecules fit into the binding pockets of proteins, and the analysis suggested plausible physical interactions between the CYP1A1 and CYP1B1 proteins and carcinogenic compounds found in tobacco smoke. While docking predictions require experimental confirmation, they offer a mechanistic bridge between the genetic and transcriptomic findings: the enzymes that are overexpressed in smokers with lung cancer are also structurally capable of engaging the very chemicals that smoke delivers to the lung.

The final layer of evidence came from the laboratory. The researchers exposed BEAS-2B cells, an immortalized line of human bronchial epithelial cells widely used to model the airway lining, to cigarette smoke extract. The treatment significantly increased CYP1A1 and CYP1B1 expression at both the messenger RNA and protein levels, demonstrating that the upregulation observed in patient datasets can be reproduced experimentally when airway epithelial cells encounter smoke constituents. This in vitro validation closes an important loop, showing that the association detected in human tissue is at least consistent with a direct response of airway cells to tobacco exposure.

Taken together, the findings sketch a coherent mechanistic model. Tobacco smoke delivers a complex cocktail of carcinogens to the bronchial epithelium, where cells respond by ramping up expression of CYP1A1 and CYP1B1. These enzymes then metabolize smoke constituents, and in doing so may generate reactive intermediates that damage DNA, deplete antioxidant defenses, and drive oxidative stress responses. Over years of repeated exposure, this cycle of activation and damage could push epithelial cells down the path toward malignancy. The positive Mendelian randomization results lend genetic support to this causal interpretation, while the single-cell and docking analyses explain where in the tissue and through which molecular interactions the process is likely to unfold.

The clinical implications are cautiously promising. If CYP1A1 and CYP1B1 expression levels can be reliably measured, for example in airway brushings, sputum, or blood-derived signatures, they could serve as biomarkers for identifying smokers whose lungs show molecular evidence of heightened carcinogenic activation, allowing earlier and more intensive screening. The four-gene signature that also includes HLA-DRA and IDO1 may further refine such risk stratification. At the same time, the authors and observers alike emphasize that these results arise from computational and cell-line analyses of public datasets, and that the enzymes’ driver status must be confirmed in prospective cohorts and functional studies before they can inform clinical practice. Nevertheless, by converging multiple independent lines of evidence on two enzymes that sit at the intersection of tobacco chemistry and lung biology, the study offers one of the clearest molecular accounts to date of why smoking and lung cancer are so tightly, and so variably, linked.

Subject of Research: Molecular mechanisms of smoking-related lung carcinogenesis involving CYP1A1 and CYP1B1

Article Title: Integrated identification of CYP1A1 and CYP1B1 as potential drivers of smoking-related lung carcinogenesis

Article References: Yu, X., Yang, R., Xu, Y., Li, L., & He, Z. (2026). Integrated identification of CYP1A1 and CYP1B1 as potential drivers of smoking-related lung carcinogenesis. 3 Biotech, 16(11), Article 465. https://doi.org/10.1007/s13205-026-05099-x

Image Credits: AI Generated

DOI: 10.1007/s13205-026-05099-x

Keywords: lung cancer, smoking, CYP1A1, CYP1B1, cytochrome P450, carcinogen metabolism, Mendelian randomization, machine learning, single-cell transcriptomics, oxidative stress, biomarkers, BEAS-2B