Analysis of risk factors and gene mutation characteristics of different metastatic sites of lung cancer

Clinicopathological significance of mutation profile detected by next generation sequencing in different metastatic organs of non-small cell lung cancers

BACKGROUND

The primary tumor and it's metastases show heterogeneity in molecular studies for targeted therapies in Non-Small Cell Lung Cancer(NSCLC), the leading cause of cancer-related deaths worldwide. The study aimed to identify somatic mutations in biopsies from NSCLC patients' metastatic organs using Next-Generation Sequencing(NGS) and examine their association with clinicopathological parameters.

MATERIALS AND METHODS

The study included 128 NSCLC patients and, NGS was performed on tumor biopsies from different metastatic organs at Molecular Pathology laboratory of the Department of Medical Pathology in Aydın Adnan Menderes University Faculty of Medicine. The age, gender, histopathological diagnoses, metastatic organs, smoking and mutation status were all recorded, along with the analysis results of 72 genes and 4149 primers in the panel of the NGS system.

RESULTS

53.9 % of the cases had a history of smoking and patients with brain metastases had a higher smoking rate(p=0.000). The most common occurrence(39.8 %) was lymph node metastasis, followed by brain(19.5 %). There was a strong correlation between mutation presence and metastasis in the liver(p=0.012), bone(p=0.002), and pleura(p=0.008). Smokers had a higher frequency of KRAS(p=0.000) and TP53(p=0.001) mutations. Brain metastases showed a statistically significant NF1 mutation(p=0.001), while the liver exhibited a significant BRAF mutation(p=0.000). NF1-TP53, PTEN-TP53 and NF1-PTEN were the most common concomitant mutations and, the brain was the most common metastatic organ in which they occurred.

CONCLUSION

Our results suggest prizing assessing detected mutations, in the prediction, follow-up and management of metastases, especially in patients with lung adenocarcinoma. The assessment also needs to consider the tumor's mutation status in metastatic organs. New therapeutic agents targeting NF1 mutations will be available in the future to treat NSCLC, especially in metastases.

Regulatory mechanism and expression level of PRPS2 in lung cancer

BACKGROUND

Lung cancer, with high morbidity and mortality, is the commonest respiratory system neoplasm, which seriously endangers the life safety of patients. In this study, the effect of PRPS2 on cell progression was preliminarily investigated.

METHODS

Immunohistochemical staining, western blot and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) were performed to verify the expression level of PRPS2 in lung cancer. Lung cancer cell lines with stable downregulation of PRPS2 were constructed in A549 cells and NCIH460 cells. The function of PRPS2 silencing on the proliferation ability was verified by the EdU and cell colony formation experiment. Scratch and transwell tests were conducted to verify the role of PRPS2 silencing on the migratory and invasive ability of cells. The impact of PRPS2 silencing on cell apoptosis and cell cycle was verified by flow cytometry test. The effects of PRPS2 silencing on apoptosis-associated proteins were assessed by western blot assay. The function of PRPS2 silencing on tumor growth in vivo was studied through xenograft tumor experiment.

RESULTS

In comparison with normal tissues, PRPS2 was upregulated in lung cancer tissues. PRPS2 knockdown notably hindered the migratory ability, invasive ability and proliferation, but accelerated cell apoptosis. In vivo experiments confirmed that PRPS2 silencing blocked the growth of transplanted tumors.

CONCLUSION

In lung cancer, PRPS2 silencing suppressed the malignant progression, indicating that PRPS2 might be a novel biomarker for lung cancer treatment and diagnosis.

Single-cell RNA sequencing reveals distinct transcriptomic profiles and evolutionary patterns in lung cancer brain metastasis

Background

Lung cancer metastasis to the brain presents significant clinical challenges. Therefore, elucidating its underlying mechanisms and characterizing its transcriptomic landscape is essential for developing therapeutic interventions.

Methods

We analyzed two distinct single-cell RNA sequencing datasets of lung cancer metastasis to analyze the evolutionary trajectory of brain metastatic tumors. In addition, a systematic comparison of cell-cell interaction between tumor cells and lymphocytes was conducted within primary and brain metastatic tumors.

Results

The brain metastatic tumors showed greater transcriptomic changes (reflected by a higher pseudotime) than tumors in the lymph nodes and primary tumors. Furthermore, our investigation has not only revealed specific shared ligand-receptor pairs in both mLN and mBrain, exemplified by the interaction between SPP1 and CD99 in T cells, but has also unveiled a diverse array of ligand-receptor pairs exclusive to the mBrain. Notably, this includes distinctive pairs such as APP and IL1 observed specifically in myeloid cells.

Conclusion

The distinct microenvironment in the brain may influence the observed transcriptomic changes in tumors, emphasizing the significance of the specific environment in determining tumor behavior and therapeutic response.

Exploring the factors affecting the occurrence of postoperative MVI and the prognosis of hepatocellular carcinoma patients treated with hepatectomy: A multicenter retrospective study

OBJECTIVE

To investigate the influencing factors affecting the occurrence of microvascular invasion (MVI) and the prognosis of hepatocellular carcinoma (HCC) patients treated with hepatectomy, and to explore how MVI affects prognosis in subgroups with different prognostic factors.

METHODS

Clinical data of a total of 1633 patients treated surgically for HCC in four treatment centers were included, including 754 patients with MVI. By using the Cox risk regression model and the Mann-Whitney U-test, the common independent influences on prognosis and MVI were made clear. The incidence of MVI in various subgroups was then examined, as well as the relationship between MVI in various subgroups and prognosis.

RESULTS

The Cox risk regression model showed that MVI, Child-Pugh classification, alpha-fetoprotein (AFP), hepatocirrhosis, tumor diameter, lymphocyte-to-monocyte ratio (LMR), and, Barcelona clinic liver cancer (BCLC) grade were independent determinants of overall survival (OS), and MVI, AFP, hepatocirrhosis, tumor diameter, and LMR were influencing determinants for disease-free survival (DFS). The receiver operating characteristic (ROC) curve showed that MVI was most closely associated with patient prognosis compared to other prognostic factors. AFP, hepatocirrhosis, tumor diameter, and LMR were discovered to be common influences on the prognosis of patients with HCC and MVI when combined with the results of the intergroup comparison of MVI. After grouping, it was showed that patients with hepatocirrhosis, positive AFP (AFP ≥ 20 ng/mL), tumor diameter >50 mm, and LMR ≤3.4 had a significantly higher incidence of MVI than patients in other subgroups, and all four subgroups of MVI-positive patients had higher rates of early recurrence and mortality (p < 0.05).

CONCLUSIONS

MVI was found to be substantially linked with four subgroups of HCC patients with hepatocirrhosis, positive AFP, tumor diameter >50 mm, and LMR ≤3.4, and the prognosis of MVI-positive patients in all four subgroups tended to be worse.

Evaluating the predictive significance of systemic immune-inflammatory index and tumor markers in lung cancer patients with bone metastases

Objective

This study aims to develop a predictive model for identifying lung cancer patients at elevated risk for bone metastases, utilizing the Unified Immunoinflammatory Index and various tumor markers. This model is expected to facilitate timely and effective therapeutic interventions, especially in the context of the growing significance of immunotherapy for lung cancer treatment.

Methods

A retrospective analysis was conducted on 324 lung cancer patients treated between January 2019 and January 2021. After meeting the inclusion criteria, 241 patients were selected, with 56 exhibiting bone metastases. The cohort was divided into a training group (169 patients) and a validation group (72 patients) at a 7:3 ratio. Lasso regression was employed to identify critical variables, followed by logistic regression to construct a Nomogram model for predicting bone metastases. The model's validity was ascertained through internal and external evaluations using the Concordance Index (C-index) and Receiver Operating Characteristic (ROC) curve.

Results

The study identified several factors influencing bone metastasis in lung cancer, such as the Systemic Immune-Inflammatory Index (SII), Carcinoembryonic Antigen (CEA), Neuron Specific Enolase (NSE), Cyfra21-1, and Neutrophil-to-Lymphocyte Ratio (NLR). These factors were incorporated into the Nomogram model, demonstrating high validation accuracy with C-index scores of 0.936 for internal and 0.924 for external validation.

Conclusion

The research successfully developed an intuitive and accurate Nomogram prediction model utilizing clinical indicators to predict the risk of bone metastases in lung cancer patients. This tool can be instrumental in aiding clinicians in developing personalized treatment plans, thereby optimizing patient outcomes in lung cancer care.

An effective prognostic model for assessing prognosis of non-small cell lung cancer with brain metastases

Background: Brain metastasis, with an incidence of more than 30%, is a common complication of non-small cell lung cancer (NSCLC). Therefore, there is an urgent need for an assessment method that can effectively predict brain metastases in NSCLC and help understand its mechanism. Materials and methods: GSE30219, GSE31210, GSE37745, and GSE50081 datasets were downloaded from the GEO database and integrated into a dataset (GSE). The integrated dataset was divided into the training and test datasets. TCGA-NSCLC dataset was regarded as an independent verification dataset. Here, the limma R package was used to identify the differentially expression genes (DEGs). Importantly, the RiskScore model was constructed using univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Moreover, we explored in detail the tumor mutational signature, immune signature, and sensitivity to treatment of brain metastases in NSCLC. Finally, a nomogram was built using the rms package. Results: First, 472 DEGs associated with brain metastases in NSCLC were obtained, which were closely associated with cancer-associated pathways. Interestingly, a RiskScore model was constructed using 11 genes from 472 DEGs, and the robustness was confirmed in GSE test, entire GSE, and TCGA datasets. Samples in the low RiskScore group had a higher gene mutation score and lower immunoinfiltration status. Moreover, we found that the patients in the low RiskScore group were more sensitive to the four chemotherapy drugs. In addition, the predictive nomogram model was able to effectively predict the outcome of patients through appropriate RiskScore stratification. Conclusion: The prognostic RiskScore model we established has high prediction accuracy and survival prediction ability for brain metastases in NSCLC.

Development and Validation of the Random Forest Model via Combining CT-PET Image Features and Demographic Data for Distant Metastases among Lung Cancer Patients

The work aimed at developing and validating a random forest model of CT-PET image features combined with demographic data to diagnose distant metastases among lung cancer patients. This study involved lung cancer patients from The Cancer Genome Atlas lung adenocarcinoma (TCGA-LUAD) dataset, the lung PET-CT dataset, the lung squamous cell carcinoma (LSCC) dataset, and the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium lung adenocarcinoma (CPTAC-LUAD) dataset and collected the information on 178 CT, 178 PET, and the patients' age, history of smoking, and gender. We conducted image processing and feature extraction. Finally, 4 computed tomography (CT) image features and 2 positron emission tomography (PET) image features were extracted. Four prediction models based on CT image features, PET image features, and demographic data were developed, and the area under the receiver operating characteristic (ROC) curve was used to evaluate the performance of prediction models. A total of 178 eligible samples were randomly divided into a training set (n = 134) and a testing set (n = 44) at a ratio of 3 : 1, with 2021 as a random number. ROC analyses illustrated that the predictive performance for distant metastases of combining CT-PET image features and demographic data for training and testing were 0.923 (95% confidence interval (CI): 0.873-0.973) and 0.873 (95% CI: 0.757-0.990). In addition, the predictive performance of the combined model in the testing set was significantly better than that of the CT-demographic data model (0.716, 95% CI: 0.531-0.902), PET-demographic data model (0.802, 95% CI: 0.633-0.970), and CT-PET model (0.797, 95% CI: 0.666-0.928). The random forest model via combining CT-PET image features and demographic data could have great performance in predicting distant metastases among lung cancer patients.

Use of gold nanoparticle-silibinin conjugates: A novel approach against lung cancer cells

Lung cancer presents one of the most challenging carcinomas with meager 5-year survival rates (less than 20%), high metastasis and high recurrence due to chemo- and radio- resistance. An alternative or complementation to existing prognosis modalities is the use of phytochemicals such as silibinin, which targets essential cytokines, angiogenic factors and transcription factors for a profound anti-tumor effect. However, the problems of low solubility in an aqueous physiological environment, poor penetration, high metabolism and rapid systemic clearance limit the therapeutic use of silibinin. Conjugation of gold nanoparticles (GNPs) with silibinin may overcome the above challenges along with distinct advantages of biocompatibility, optical properties for monitoring and causation of cytotoxicity in cancer cells. The current study thus aims to develop silibinin conjugated gold nanoparticles (Sb-GNPs) with pH responsive release in the cancer microenvironment, optimizing several parameters for its higher activity and further evaluate the nanoplatform for their efficacy in inducing cell death in vitro against A549 lung cancer cells. GNPs was synthesized using trisodium citrate dihydrate as the reducing agent and further used for the conjugation of silibinin. The synthesized GNPs were found to be monodispersed and spherical in shape. The silibinin was successfully conjugated with gold nanoparticles and long-term stability of GNPs and Sb-GNPs nanoconjugates in suspension phase was confirmed by FTIR and DLS. Anticancer properties of Sb-GNPs were confirmed by different assay using MTT, Trypan blue dye exclusion assay and cell cycle analysis assay. After conjugation of silibinin with GNPs, the efficacy of silibinin increased 4-5 times in killing the cancer cells. This is the first report on using silibinin gold nanoconjugate system for lung cancer therapy with promising future applications.