Single nucleotide polymorphisms within the Wnt pathway predict the risk of bone metastasis in patients with non-small cell lung cancer

Preoperative Prediction of STAS Risk in Primary Lung Adenocarcinoma Using Machine Learning: An Interpretable Model with SHAP Analysis

BACKGROUND

Accurate preoperative prediction of spread through air spaces (STAS) in primary lung adenocarcinoma (LUAD) is critical for optimizing surgical strategies and improving patient outcomes.

OBJECTIVE

To develop a machine learning (ML) based model to predict STAS using preoperative CT imaging features and clinicopathological data, while enhancing interpretability through shapley additive explanations (SHAP) analysis.

MATERIALS AND METHODS

This multicenter retrospective study included 1237 patients with pathologically confirmed primary LUAD from three hospitals. Patients from Center 1 (n=932) were divided into a training set (n=652) and an internal test set (n=280). Patients from Centers 2 (n=165) and 3 (n=140) formed external validation sets. CT imaging features and clinical variables were selected using Boruta and least absolute shrinkage and selection operator regression. Seven ML models were developed and evaluated using five-fold cross-validation. Performance was assessed using F1 score, recall, precision, specificity, sensitivity, and area under the receiver operating characteristic curve (AUC).

RESULTS

The Extreme Gradient Boosting (XGB) model achieved AUCs of 0.973 (training set), 0.862 (internal test set), and 0.842/0.810 (external validation sets). SHAP analysis identified nodule type, carcinoembryonic antigen, maximum nodule diameter, and lobulated sign as key features for predicting STAS. Logistic regression analysis confirmed these as independent risk factors.

CONCLUSION

The XGB model demonstrated high predictive accuracy and interpretability for STAS. By integrating widely available clinical and imaging features, this model offers a practical and effective tool for preoperative risk stratification, supporting personalized surgical planning in primary LUAD management.

Development and validation of a nomogram model based on blood-based genomic mutation signature for predicting the risk of brain metastases in non-small cell lung cancer

PURPOSE

Available research indicates that the mammalian target of rapamycin complex 1 (mTORC1) signaling pathway is significantly correlated with lung cancer brain metastasis (BM). This study established a clinical predictive model for assessing the risk of BM based on the mTORC1-related single nucleotide polymorphisms (SNPs).

METHODS

In this single-center retrospective study, 395 patients with non-small cell lung cancer were included. Clinical, pathological, imaging, and mTORC1-related single nucleotide polymorphism data were collected. Lasso regression was used to identify variables related to the risk of BM in lung cancer, and a nomogram was constructed. Internal validation was performed using 1,000 bootstrap samples. We plotted the receiver operating characteristic (ROC) curve and calculated the area under the curve (AUC). The calibration of the model was assessed using calibration curves and the Hosmer-Lemeshow goodness-of-fit test, and decision curve analysis (DCA) was plotted to evaluate the net clinical benefit.

RESULTS

The nomogram's predictive factors included lung cancer histology, clinical N stage, CEA, neutrophil to lymphocyte ratio (NLR), lymphocyte to monocyte ratio (LMR), RPTOR: rs1062935, and RPTOR: rs3751934. The AUC of the model in the training set and internal validation were 0.849 and 0.801, respectively. The calibration curves and Hosmer-Lemeshow test both indicated a good fit.

CONCLUSION

The nomogram has practicality and efficacy in predicting the high risk of BM in lung cancer patients, confirming that single nucleotide polymorphisms in the mTORC1 pathway genes may be good predictors in clinical prediction models.

Exploring the key pathogenic mechanisms and potential intervention targets for Sophorae Flavescentis radix in managing bone metastasis of lung cancer based on network pharmacology and molecular docking techniques

Background

Lung cancer often metastasizes to the bone, which significantly complicates treatment and worsens patient prognosis. Thus, new therapeutic strategies need to be established. Using network pharmacology and bioinformatics analysis, this study sought to determine the molecular targets and associated mechanisms of the traditional Chinese medicine (TCM) Sophorae Flavescentis radix in the treatment of lung cancer bone metastasis.

Methods

The active components of Sophorae Flavescentis radix were screened using the TCM Systems Pharmacology (TCMSP) platform based on drug-likeness and oral bioavailability. The target genes of these active compounds were obtained from the DrugBank database. Differentially expressed genes (DEGs) between primary and bone metastatic lung cancer samples were screened in the GSE175601 dataset from the Gene Expression Omnibus (GEO) database using GEO2R. The intersecting DEGs from both groups were used to construct a Venn diagram to identify the candidate target genes. The expression and prognostic relevance of these genes were validated in The Cancer Genome Atlas (TCGA) database. The GeneMania and Search Tool for Recurring Instances of Neighbouring Genes (STRING) databases were used to generate the protein-protein interaction networks. Molecular docking was performed using the PubChem, Protein Data Bank (PDB), and CB-DOCK2 databases. A Gene Set Enrichment Analysis (GSEA) was conducted to explore the possible mechanisms of action.

Results

In the TCMSP database, 28 active compounds and 227 target genes of the Sophorae Flavescentis radix were identified. In total, 952 DEGs related to lung cancer bone metastasis were found in the GSE175601 dataset from the GEO database. Five common DEGs were identified via Venn diagram construction (i.e., F10, JUN, AKR1B1, MMP1, and CCND1). MMP1 was selected as the candidate gene. MMP1 was upregulated in lung cancer tissues, and patients with low MMP1 expression had better survival rates than those with high MMP1 expression (P<0.05). MMP1 has an affinity of -8.9 with luteolin. The GSEA results suggested that MMP1 might influence biological processes in lung cancer by participating in pathways such as chemokine signaling, apoptosis, Wingless/Integrated (Wnt) signaling, tumor protein p53-regulated cell cycle arrest, Hedgehog signaling, and mitogen-activated protein kinase signaling.

Conclusions

Patients with lower MMP1 levels had prolonged overall survival and may serve as a novel predictive biomarker for lung cancer. Sophorae Flavescentis radix appears to exert therapeutic effects on lung cancer bone metastasis by inhibiting MMP1 expression and modulating the abnormal activation of the Wnt pathway. Our findings further extend the understanding of the pathogenic mechanisms and potential therapeutic interventions of Sophorae Flavescentis radix in lung cancer bone metastasis, providing a theoretical basis for clinical diagnosis and treatment research.

Wnt/β-catenin signaling in the development and therapeutic resistance of non-small cell lung cancer

Wnt/β-catenin signaling is a critical pathway that influences development and therapeutic response of non-small cell lung cancer (NSCLC). In recent years, many Wnt regulators, including proteins, miRNAs, lncRNAs, and circRNAs, have been found to promote or inhibit signaling by acting on Wnt proteins, receptors, signal transducers and transcriptional effectors. The identification of these regulators and their underlying molecular mechanisms provides important implications for how to target this pathway therapeutically. In this review, we summarize recent studies of Wnt regulators in the development and therapeutic response of NSCLC.

Exosomal hsa-miR-151a-3p and hsa-miR-877-5p are potential novel biomarkers for predicting bone metastasis in lung cancer

Exosomal miRNAs (exo-miRNAs) have arisen as novel diagnostic biomarkers for various cancers. However, few reports on exo-miRNAs related to bone metastasis (BM) in lung cancer exist. This study aims to screen out key exo-miRNAs and estimate their prognostic values for predicting BM in lung cancer. The differentially expressed exo-miRNAs between the highly-metastatic (95D) and lowly-metastatic (A549) human lung cancer cell lines were comprehensively analyzed using high-throughput sequencing followed by bioinformatic analyses. 29 candidate exo-miRNAs were identified, and 101 BM-related target genes were predicted. Enrichment analysis revealed that these target genes were mainly involved in regulating transcription and pathways in cancer. An exosomal miRNA-mRNA regulatory network consisting of 7 key miRNAs and 10 hub genes was constructed. Further function analysis indicated that these 10 hub genes were mainly enriched in regulating cancer's apoptosis and central carbon metabolism. The survival analysis indicated that 7 of 10 hub genes were closely related to prognosis. Mutation analysis showed that lung cancer patients presented certain genetic alterations in the 7 real hub genes. GSEA for a single hub gene suggested that 6 of 7 real hub genes had close associations with lung cancer development. Finally, ROC analysis revealed that hsa-miR-151a-3p and hsa-miR-877-5p provided high diagnostic accuracy in discriminating patients with bone metastasis (BM+) from patients without bone metastasis (BM-). These findings provided a comprehensive analysis of exo-miRNAs and target genes in the regulatory network of BM in lung cancer. In particular, hsa-miR-151a-3p and hsa-miR-877-5p may be novel biomarkers for predicting BM in lung cancer.

Association of AXIN1 rs12921862 C/A and rs1805105 G/A and CTSB rs12898 G/A polymorphisms with papillary thyroid carcinoma: A case-control study

BACKGROUND

Papillary thyroid cancer (PTC) is the most common type of thyroid cancer which its precise etiology remains unknown. However, environmental and genetic factors contribute to the etiology of PTC. Axis inhibition protein 1 (Axin1) is a scaffold protein that exerts its role as a tumor suppressor. In addition, Cathepsin B (Ctsb) is a cysteine protease with higher expression in several types of tumors. Therefore, the aim of this study was to investigate the possible association of AXIN1 rs12921862 C/A and rs1805105 G/A and CTSB rs12898 G/A polymorphisms with PTC susceptibility.

MATERIALS & METHODS

In total, 156 PTC patients and 158 sex-, age-, and BMI-matched control subjects were enrolled in the study. AXIN1 rs12921862 C/A and rs1805105 G/A and CTSB rs12898 G/A polymorphisms were genotyped using the PCR-RFLP method.

RESULTS

There was a relationship between AXIN1 rs12921862 C/A polymorphism and an increased risk of PTC in all genetic models except the overdominant model. The AXIN1 rs1805105 G/A polymorphism was associated with an increased PTC risk only in codominant and overdominant models. The frequency of AXIN1 A_rs12921862 A_rs1805105 haplotype was higher in the PTC group and also this haplotype was associated with an increased risk of PTC. Moreover, the AXIN1 rs12921862 C/A polymorphism was not associated with PTC clinical and pathological findings, but AXIN1 rs1805105 G/A polymorphism was associated with almost three folds of larger tumor size (≥1 cm). There was no association between CTSB rs12898 G/A polymorphism and PTC and its findings.

CONCLUSION

The AXIN1 rs12921862 C/A and rs1805105 G/A polymorphisms were associated with PTC. AXIN1 rs1805105 G/A polymorphism was associated with higher tumor size.

FAIM2 Promotes Non-Small Cell Lung Cancer Cell Growth and Bone Metastasis by Activating the Wnt/β-Catenin Pathway

Aim

Bone metastasis is the major reason for the poor prognosis and high mortality rate of non-small cell lung cancer (NSCLC) patients. This study explored the function and underlying mechanism of Fas apoptotic inhibitory molecule 2 (FAIM2) in the bone metastasis of NSCLC.

Methods

Samples of normal lung tissue and NSCLC tissue (with or without bone metastasis) were collected and analyzed for FAIM2 expression. HARA cells with FAIM2 overexpression and HARA-B4 cells with FAIM2 knockdown were tested for proliferation, migration, invasion, anoikis, and their ability to adhere to osteoblasts. Next, whether FAIM2 facilitates bone metastasis by regulating the epithelial mesenchymal transformation (EMT) process and Wnt/β-catenin signaling pathway were investigated. Finally, an in vivo model of NSCLC bone metastasis was established and used to further examine the influence of FAIM2 on bone metastasis.

Results

FAIM2 was highly expressed in NSCLC tissues and NSCLC tissues with bone metastasis. FAIM2 expression was positively associated with the tumor stage, lymph node metastasis, bone metastasis, and poor prognosis of NSCLC. FAIM2 upregulation promoted HARA cell proliferation, migration, and invasion, but inhibited cell apoptosis. FAIM2 knockdown in HARA-B4 cells produced the opposite effects. HARA-B4 cells showed a stronger adhesive ability to osteocytes than did HARA cells. FAIM2 was found to be related to the adhesive ability of HARA and HARA-B4 cells to osteocytes. FAIM2 facilitated bone metastasis by regulating the EMT process and Wnt/β-catenin signaling pathway. Finally, FAIM2 was found to participate in regulating NSCLC bone metastasis in vivo.

Conclusions

FAIM2 promoted NSCLC cell growth and bone metastasis by regulating the EMT process and Wnt/β-catenin signaling pathway. FAIM2 might be useful for diagnosing and treating NSCLC bone metastases.

The metastasizing mechanisms of lung cancer: Recent advances and therapeutic challenges

Lung cancer is one of the common malignant tumors that threaten human life with serious incidence and high mortality. According to the histopathological characteristics, lung cancer is mainly divided into non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). NSCLC accounts for about 80-85% of lung cancers. In fact, lung cancer metastasis is a major cause of treatment failure in clinical patients. The underlying reason is that the mechanisms of lung cancer metastasis are still not fully understood. The metastasis of lung cancer cells is controlled by many factors, including the interaction of various components in the lung cancer microenvironment, epithelial-mesenchymal transition (EMT) transformation, and metastasis of cancer cells through blood vessels and lymphatics. The molecular relationships are even more intricate. Further study on the mechanisms of lung cancer metastasis and in search of effective therapeutic targets can bring more reference directions for clinical drug research and development. This paper focuses on the factors affecting lung cancer metastasis and connects with related molecular mechanisms of the lung cancer metastasis and mechanisms of lung cancer to specific organs, which mainly reviews the latest research progress of NSCLC metastasis. Besides, in this paper, experimental models of lung cancer and metastasis, mechanisms in SCLC transfer and the challenges about clinical management of lung cancer are also discussed. The review is intended to provide reference value for the future research in this field and promising treatment clues for clinical patients.