Identification of novel pQTL-SNPs associated with lung adenocarcinoma risk: A multi-stage study.

BACKGROUND AND OBJECTIVE: To explore the association between protein quantitative trait loci (pQTL-SNPs) and the risk of LUAD. METHODS: "Blood +" high depth blood proteomics analysis was performed on plasma from female LUAD patients and female healthy controls, and combined with proteomics data from tumors and adjacent non-tumor tissues of female LUAD patients to screen proteins uniformly expressed in plasma and tissues. pQTL-SNPs were then screened through multiple databases and subjected to multilevel screening. The associations between selected pQTL-SNPs and LUAD risk were evaluated by Female Lung Cancer Consortium in Asia GWAS (FLCCA GWAS). Enzyme linked immunosorbent assay (ELISA) is used to determine the levels of candidate protein. RESULTS: A total of 7 pQTL-SNPs were significantly associated with altered LUAD risk (p < 0.05). Meanwhile, the expression of their corresponding target proteins were all decreased in both plasma and tumor tissues of LUAD cases, which may play a role of tumor suppressor proteins. After mutation of 3 pQTL-SNPs (rs7683000, rs73224660, and rs2776937), the expression of corresponding target proteins BST1 and NRP1 decreased, and as potential tumor suppressor proteins, which may promote tumorigenesis and further increasing the risk of developing LUAD (OR >1, p < 0.05); while after mutation the other pQTL-SNP rs62069916, the corresponding target protein APOH expression was increased, while as a potential tumor suppressor protein, which may inhibit tumorigenesis and further reduced the risk of developing LUAD (OR <1, p < 0.05). In addition, the expression of NRP1 and APOH were significant decreased in LUAD cell lines and validated in plasma of LUAD patients. CONCLUSION: A total of 4 pQTL-SNPs (rs7683000, rs73224660, rs2776937, and rs62069916) may associate with altered LUAD risk by regulating the expression of target proteins (BST1, NRP1, and APOH) after mutation.

ARTEMIS: An independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions.

INTRODUCTION: Breast cancer, a heterogeneous disease, is influenced by multiple genetic and epigenetic factors. The majority of prognostic models for breast cancer focus merely on the main effects of predictors, disregarding the crucial impacts of gene-gene interactions on prognosis. OBJECTIVES: Using DNA methylation data derived from nine independent breast cancer cohorts, we developed an independently validated prognostic prediction model of breast cancer incorporating epigenetic biomarkers with main effects and gene-gene interactions (ARTEMIS) with an innovative 3-D modeling strategy. ARTEMIS was evaluated for discrimination ability using area under the receiver operating characteristics curve (AUC), and calibration using expected and observed (E/O) ratio. Additionally, we conducted decision curve analysis to evaluate its clinical efficacy by net benefit (NB) and net reduction (NR). Furthermore, we conducted a systematic review to compare its performance with existing models. RESULTS: ARTEMIS exhibited excellent risk stratification ability in identifying patients at high risk of mortality. Compared to those below the 25th percentile of ARTEMIS scores, patients with above the 90th percentile had significantly lower overall survival time (HR = 15.43, 95% CI: 9.57-24.88, P = 3.06 × 10-29). ARTEMIS demonstrated satisfactory discrimination ability across four independent populations, with pooled AUC3-year = 0.844 (95% CI: 0.805-0.883), AUC5-year = 0.816 (95% CI: 0.775-0.857), and C-index = 0.803 (95% CI: 0.776-0.830). Meanwhile, ARTEMIS had well calibration performance with pooled E/O ratio 1.060 (95% CI: 1.038-1.083) and 1.090 (95% CI: 1.057-1.122) for 3- and 5-year survival prediction, respectively. Additionally, ARTEMIS is a clinical instrument with acceptable cost-effectiveness for detecting breast cancer patients at high risk of mortality (Pt = 0.4: NB3-year = 19‰, NB5-year = 62‰; NR3-year = 69.21%, NR5-year = 56.01%). ARTEMIS has superior performance compared to existing models in terms of accuracy, extrapolation, and sample size, as indicated by the systematic review. ARTEMIS is implemented as an interactive online tool available at http://bigdata.njmu.edu.cn/ARTEMIS/. CONCLUSION: ARTEMIS is an efficient and practical tool for breast cancer prognostic prediction.

Identification of the consistently differential expressed hub mRNAs and proteins in lung adenocarcinoma and construction of the prognostic signature: a multidimensional analysis.

BACKGROUND: This study aimed to elucidate the consistency of differentially expressed hub mRNAs and proteins in lung adenocarcinoma (LUAD) across populations and to construct a comprehensive LUAD prognostic signature. METHODS: The transcriptomic and proteomics data from different populations were standardized and analyzed using the same criteria to identify the consistently differential expressed mRNAs and proteins across genders and races. We then integrated prognosis-related mRNAs with clinical, pathological, and EGFR (epidermal growth factor receptor) mutation data to construct a survival model, subsequently validating it across populations. Through plasma proteomics, plasma proteins that consistently differential expressed with LUAD tissues were screened and validated, with their associations discerned by measuring expressions in tumor tissues and tumor vascular normalization. RESULTS: The consistency rate of differentially expressed mRNAs and proteins was ~20-40%, with ethnic factors leading to about 40-60% consistency of differentially expressed mRNA or protein across populations. The survival model based on the identified eight hub mRNAs as well as stage, smoking status, and EGFR mutations, demonstrated good prognostic prediction capabilities in both Western and East Asian populations, with a higher number of unfavorable variables indicating poorer LUAD prognosis. Notably, GPI expression in tumor tissues was inversely correlated with vascular normalization and positively correlated with plasma GPI expression. CONCLUSION: Our study underscores the significance of integrating transcriptomics and proteomics data, emphasizing the need to account for genetic diversity among ethnic groups. The developed survival model may offer a holistic perspective on LUAD progression, enhancing prognosis and therapeutic strategies.