Epigenome-wide gene-age interaction analysis reveals reversed effects of PRODH DNA methylation on survival between young and elderly early-stage NSCLC patients

A two-phase epigenome-wide four-way gene-smoking interaction study of overall survival for early-stage non-small cell lung cancer

High-order interactions associated with non-small cell lung cancer (NSCLC) survival may elucidate underlying molecular mechanisms and identify potential therapeutic targets. Our previous work has identified a three-way interaction among pack-year of smoking (the number of packs of cigarettes smoked per day multiplied by the number of years the person has smoked) and two DNA methylation probes (cg05293407TRIM27 and cg00060500KIAA0226). However, whether a four-way interaction exists remains unclear. Therefore, we adopted a two-phase design to identify the four-way gene-smoking interactions by a hill-climbing strategy on the basis of the previously detected three-way interaction. One CpG probe, cg16658473SHISA9, was identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase. Meanwhile, the four-way interaction improved the discrimination ability for the prognostic prediction model, as indicated by the area under the receiver operating characteristic curve (AUC) for both 3- and 5-year survival. In summary, we identified a four-way interaction associated with NSCLC survival among pack-year of smoking, cg05293407TRIM27, cg00060500KIAA0226 and g16658473SHISA9, providing novel insights into the complex mechanisms underlying NSCLC progression.

BHAFT: Bayesian heredity-constrained accelerated failure time models for detecting gene-environment interactions in survival analysis

In addition to considering the main effects, understanding gene-environment (G × E) interactions is imperative for determining the etiology of diseases and the factors that affect their prognosis. In the existing statistical framework for censored survival outcomes, there are several challenges in detecting G × E interactions, such as handling high-dimensional omics data, diverse environmental factors, and algorithmic complications in survival analysis. The effect heredity principle has widely been used in studies involving interaction identification because it incorporates the dependence of the main and interaction effects. However, Bayesian survival models that incorporate the assumption of this principle have not been developed. Therefore, we propose Bayesian heredity-constrained accelerated failure time (BHAFT) models for identifying main and interaction (M-I) effects with novel spike-and-slab or regularized horseshoe priors to incorporate the assumption of effect heredity principle. The R package rstan was used to fit the proposed models. Extensive simulations demonstrated that BHAFT models had outperformed other existing models in terms of signal identification, coefficient estimation, and prognosis prediction. Biologically plausible G × E interactions associated with the prognosis of lung adenocarcinoma were identified using our proposed model. Notably, BHAFT models incorporating the effect heredity principle could identify both main and interaction effects, which are highly useful in exploring G × E interactions in high-dimensional survival analysis. The code and data used in our paper are available at https://github.com/SunNa-bayesian/BHAFT.

Proline Dehydrogenase (PRODH) Is Expressed in Lung Adenocarcinoma and Modulates Cell Survival and 3D Growth by Inducing Cellular Senescence

The identification of markers for early diagnosis, prognosis, and improvement of therapeutic options represents an unmet clinical need to increase survival in Non-Small Cell Lung Cancer (NSCLC), a neoplasm still characterized by very high incidence and mortality. Here, we investigated whether proline dehydrogenase (PRODH), a mitochondrial flavoenzyme catalyzing the key step in proline degradation, played a role in NSCLC tumorigenesis. PRODH expression was investigated by immunohistochemistry; digital PCR, quantitative PCR, immunoblotting, measurement of reactive oxygen species (ROS), and functional cellular assays were carried out. PRODH expression was found in the majority of lung adenocarcinomas (ADCs). Patients with PRODH-positive tumors had better cancer-free specific and overall survival compared to those with negative tumors. Ectopic modulation of PRODH expression in NCI-H1299 and the other tested lung ADC cell lines decreased cell survival. Moreover, cell proliferation curves showed delayed growth in NCI-H1299, Calu-6 and A549 cell lines when PRODH-expressing clones were compared to control clones. The 3D growth in soft agar was also impaired in the presence of PRODH. PRODH increased reactive oxygen species production and induced cellular senescence in the NCI-H1299 cell line. This study supports a role of PRODH in decreasing survival and growth of lung ADC cells by inducing cellular senescence.

Proline promotes proliferation and drug resistance of multiple myeloma by downregulation of proline dehydrogenase

Amino acids in the bone marrow microenvironment (BMME) are a critical factor for multiple myeloma (MM) progression. Here, we have determined that proline is elevated in BMME of MM patients and links to poor prognosis in MM. Moreover, exogenous proline regulates MM cell proliferation and drug resistance. Elevated proline in BMME is due to bone collagen degradation and abnormal expression of the key enzyme of proline catabolism, proline dehydrogenase (PRODH). PRODH is downregulated in MM patients, mainly as a result of promoter hypermethylation with high expression of DNMT3b. Thus, overexpression of PRODH suppresses cell proliferation and drug resistance of MM and exhibits therapeutic potential for treatment of MM. Altogether, we identify proline as a key metabolic regulator of MM, unveil PRODH governing MM progression and provide a promising therapeutic strategy for MM treatment.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with PBonferroni  ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (βinteraction  = 0.018, P = 1.87 × 10-12 ), which mapped to RELA × HLA-G (βinteraction  = 0.218, P = 8.82 × 10-11 ) and cg08872738 × cg27077312 (βinteraction  = -0.010, P = 1.16 × 10-11 ), which mapped to TUBA1B × TOMM40 (βinteraction =-0.250, P = 3.83 × 10-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

Epigenome-wide gene–age interaction study reveals reversed effects of MORN1 DNA methylation on survival between young and elderly oral squamous cell carcinoma patients

DNA methylation serves as a reversible and prognostic biomarker for oral squamous cell carcinoma (OSCC) patients. It is unclear whether the effect of DNA methylation on OSCC overall survival varies with age. As a result, we performed a two-phase gene–age interaction study of OSCC prognosis on an epigenome-wide scale using the Cox proportional hazards model. We identified one CpG probe, cg11676291_MORN1, whose effect was significantly modified by age (HR_discovery = 1.018, p = 4.07 × 10⁻⁰⁷, FDR-q = 3.67 × 10⁻⁰²; HR_validation = 1.058, p = 8.09 × 10⁻⁰³; HR_combined = 1.019, p = 7.36 × 10⁻¹⁰). Moreover, there was an antagonistic interaction between hypomethylation of cg11676291_MORN1 and age (HR_interaction = 0.284; 95% CI, 0.135–0.597; p = 9.04 × 10⁻⁰⁴). The prognosis of OSCC patients was well discriminated by the prognostic score incorporating cg11676291_MORN1–age interaction (HR_{high vs. low} = 3.66, 95% CI: 2.40–5.60, p = 1.93 × 10⁻⁰⁹). By adding 24 significant gene–age interactions using a looser criterion, we significantly improved the area under the receiver operating characteristic curve (AUC) of the model at 3- and 5-year prognostic prediction (AUC_3-year = 0.80, AUC_5-year = 0.79, C-index = 0.75). Our study identified a significant interaction between cg11676291_MORN1 and age on OSCC survival, providing a potential therapeutic target for OSCC patients.

A two-phase comprehensive NSCLC prognostic study identifies lncRNAs with significant main effect and interaction

Long noncoding RNA (lncRNA) are involved in regulating physiological behaviors for various malignant tumors, including non-small-cell lung cancer (NSCLC). However, few studies comprehensively evaluated both lncRNA-lncRNA interaction effects and main effects of lncRNA on overall survival of NSCLC. Hence, we performed a two-phase designed study of lncRNA expression in tumor tissues using 604 NSCLC patients from The Cancer Genome Atlas as the discovery phase and 839 patients from Gene Expression Omnibus as the validation phase. In the discovery phase, we adopted a two-step strategy, Screening before Testing, for dimension reduction and signal detection. These candidate lncRNAs first screened out by the weighted random forest (Ranger), were then tested through the Cox proportional hazards model adjusted for covariates. Significant lncRNAs with either type of effects aforementioned were carried forward into the validation phase to confirm their significances again. As a result, in the discovery phase, 19 lncRNAs were identified by Ranger, among which five lncRNAs and one pair of lncRNA-lncRNA interaction exhibited significant effects (FDR-q ≤ 0.05) main and interaction effects on NSCLC survival, respectively, through Cox model. After the independent validation, we finally observed that one lncRNA (ENSG00000227403.1) with main effect was robustly associated with NSCLC prognosis (HR_discovery = 0.90, P = 1.20 × 10^-3; HR_validation = 0.94, P = 4.11 × 10^-3) and one pair of lncRNAs (ENSG00000267121.4 and ENSG00000272369.1) had significant interaction effect on NSCLC survival (HR_discovery = 1.12, P = 3.07 × 10^-4; HR_validation = 1.11, P = 0.0397). Our comprehensive NSCLC prognostic study of lncRNA provided population-level evidence for further functional study.

Epigenome-wide three-way interaction study identifies a complex pattern between TRIM27, KIAA0226, and smoking associated with overall survival of early-stage NSCLC

The interaction between DNA methylation of tripartite motif containing 27 (cg05293407TRIM27 ) and smoking has previously been identified to reveal histologically heterogeneous effects of TRIM27 DNA methylation on early-stage non-small-cell lung cancer (NSCLC) survival. However, to understand the complex mechanisms underlying NSCLC progression, we searched three-way interactions. A two-phase study was adopted to identify three-way interactions in the form of pack-year of smoking (number of cigarettes smoked per day × number of years smoked) × cg05293407TRIM27  × epigenome-wide DNA methylation CpG probe. Two CpG probes were identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase: cg00060500KIAA0226 and cg17479956EXT2 . Compared to a prediction model with only clinical information, the model added 42 significant three-way interactions using a looser criterion (discovery: FDR-q ≤ 0.10, validation: P ≤ 0.05) had substantially improved the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model for both 3-year and 5-year survival. Our research identified the complex interaction effects among multiple environment and epigenetic factors, and provided therapeutic target for NSCLC patients.

Proline dehydrogenase in cancer: apoptosis, autophagy, nutrient dependency and cancer therapy

L-proline catabolism is emerging as a key pathway that is critical to cellular metabolism, growth, survival, and death. Proline dehydrogenase (PRODH) enzyme, which catalyzes the first step of proline catabolism, has diverse functional roles in regulating many pathophysiological processes, including apoptosis, autophagy, cell senescence, and cancer metastasis. Notably, accumulated evidence demonstrated that PRODH plays complex role in many types of cancers. In this review, we briefly introduce the function of PRODH, then its expression in different types of cancer. We next discuss the regulation of PRODH in cancer, the downstream pathways of PRODH and the therapies that are under investigation. Finally, we propose novel insights for future perspectives on the modulation of PRODH.