[Comparison of the Survival Time in the Non-small Cell Lung Cancer Patients with Different Organ Metastasis]

A novel algorithm to differentiate between primary lung tumors and distant liver metastasis in lung cancers using an exosome based multi gene biomarker panel

The lack of non-invasive methods for detection of early metastasis is a crucial reason for the poor prognosis of lung cancer (LC) liver metastasis (LM) patients. In this study, the goal was to identify circulating biomarkers based on a biomarker model for the early diagnosis and monitoring of patients with LCLM. An 8-gene panel identified in our previous study was validated in CTC, cfRNA and exosomes isolated from primary lung cancer with & without metastasis. Further multivariate analysis including PCA & ROC was performed to determine the sensitivity and specificity of the biomarker panel. Model validation cohort (n = 79) was used to verify the stability of the constructed predictive model. Further, clinic-pathological factors, survival analysis and immune infiltration correlations were also performed. In comparison to our previous tissue data, exosomes demonstrated a good discriminative value with an AUC of 0.7247, specificity (72.48%) and sensitivity (96.87%) for the 8-gene panel. Further individual gene patterns led us to a 5- gene panel that showed an AUC of 0.9488 (p =  < 0.001) and 0.9924 (p =  < 0.001) respectively for tissue and exosomes. Additionally, on validating the model in a larger cohort a risk score was obtained (RS > 0.2) for prediction of liver metastasis with an accuracy of 95%. Survival analysis and immune filtration markers suggested that four exosomal markers were independently associated with poor overall survival. We report a novel blood-based exosomal biomarker panel for early diagnosis, monitoring of therapeutic response, and prognostic evaluation of patients with LCLM.

Development and validation of an early diagnosis model for bone metastasis in non-small cell lung cancer based on serological characteristics of the bone metastasis mechanism

Background

Bone metastasis significantly impact the prognosis of non-small cell lung cancer (NSCLC) patients, reducing their quality of life and shortening their survival. Currently, there are no effective tools for the diagnosis and risk assessment of early bone metastasis in NSCLC patients. This study employed machine learning to analyze serum indicators that are closely associated with bone metastasis, aiming to construct a model for the timely detection and prognostic evaluation of bone metastasis in NSCLC patients.

Methods

The derivation cohort consisted of 664 individuals with stage IV NSCLC, diagnosed between 2015 and 2018. The variables considered in this study included age, sex, and 18 specific serum indicators that have been linked to the occurrence of bone metastasis in NSCLC. Variable selection used multivariate logistic regression analysis and Lasso regression analysis. Six machine learning methods were utilized to develop a bone metastasis diagnostic model, assessed with Area Under the Curve (AUC), Decision Curve Analysis (DCA), sensitivity, specificity, and validation cohorts. External validation used 113 NSCLC patients from the Medical Alliance (2019-2020). Furthermore, a prospective validation study was conducted on a cohort of 316 patients (2019-2020) who were devoid of bone metastasis, and followed-up for at least two years to assess the predictive capabilities of this model. The model's prognostic value was evaluated using Kaplan-Meier survival curves.

Findings

Through variable selection, 11 serum indictors were identified as independent predictive factors for NSCLC bone metastasis. Six machine learning models were developed using age, sex, and these serum indicators. A random forest (RF) model demonstrated strong performance during the training and internal validation cohorts, achieving an AUC of 0.98 (95% CI 0.95-0.99) for internal validation. External validation further confirmed the RF model's effectiveness, yielding an AUC of 0.97 (95% CI 0.94-0.99). The calibration curves demonstrated a high level of concordance between the anticipated risk and the observed risk of the RF model. Prospective validation revealed that the RF model could predict the occurrence of bone metastasis approximately 10.27 ± 3.58 months in advance, according to the results of the SPECT. An online computing platform (https://bonemetastasis.shinyapps.io/shiny_cls_1model/) for this RF model is publicly available and free-to-use by doctors and patients.

Interpretation

This study innovatively employs age, gender, and 11 serological markers closely related to the mechanism of bone metastasis to construct an RF model, providing a reliable tool for the early screening and prognostic assessment of bone metastasis in NSCLC patients. However, as an exploratory study, the findings require further validation through large-scale, multicenter prospective studies.

Funding

This work is supported by the National Natural Science Foundation of China (NO.81974315); Shanghai Municipal Science and Technology Commission Medical Innovation Research Project (NO.20Y11903300); Shanghai Municipal Health Commission Health Industry Clinical Research Youth Program (NO.20204Y034).

[SWI/SNF Complex Gene Mutations Promote the Liver Metastasis of Non-small Cell Lung Cancer Cells in NSI Mice]

BACKGROUND

The switch/sucrose nonfermentable chromatin-remodeling (SWI/SNF) complex is a pivotal chromatin remodeling complex, and the genomic alterations (GAs) of the SWI/SNF complex are observed in several cancer types, correlating with multiple biological features of tumor cells. However, their role in liver metastasis of non-small cell lung cancer (NSCLC) remains unclear. Our study aims to investigate the role and potential mechanisms underlying NSCLC liver metastasis induced by the GAs of SWI/SNF complex.

METHODS

The GAs of SWI/SNF complex in NSCLC cell lines (H1299, H23 and H460) were identified by whole-exome sequencing (WES). ARID1A knockout H1299 cell was constructed with the CRISPR/Cas9 technology. The mouse model of liver metastasis from NSCLC was established to simulate lung cancer liver metastasis and observe the metastasis rate under different gene mutation conditions. RNA sequencing and Western blot were conducted for differential gene expression analysis. Immunohistochemistry (IHC) analysis was used to assess protein expression levels of SWI/SNF-regulated target molecules in mouse liver metastases.

RESULTS

WES analysis revealed intracellular gene mutations. The animal experiments demonstrated a correlation between the GAs of SWI/SNF complex and a higher liver metastasis rate in immunodeficient mice. Transcriptome sequencing and Western blot analysis showed upregulated expression of ALDH1A1 and APOBEC3B in SWI/SNF-mut cells, particularly in ARID1A-deficient H460 and H1299 sgARID1A cells. IHC staining of mouse liver metastases further demonstrated elevated expression of ALDH1A1 in the H460 and H1299 sgARID1A group.

CONCLUSIONS

This study underscores the critical role of the GAs of SWI/SNF complex, such as ARID1A and SMARCA4, in promoting liver metastasis of lung cancer cells. The GAs of SWI/SNF complex may promote liver-specific metastasis by upregulating ALDH1A1 and APOBEC3B expression, providing novel insights into the molecular mechanisms underlying lung cancer liver metastasis.

[Investigation and Analysis of Primary Lung Cancer with Endotracheal and Endobronchial Metastases]

背景与目的

气管支气管转移（endotracheal and endobronchial metastases, EEM）在肺癌中罕见，国外文献报道可发生于手术切除后，但国内目前尚未见相关报道，本研究旨在总结和分析肺癌发生EEM的临床特征。

方法

回顾2015年1月-2018年12月于北京大学第三医院确诊原发性肺癌并行支气管镜的患者，同时检索截至2020年2月PubMed检索系统中的病例，采集并比较两组患者的临床、病理、影像、支气管镜和预后等资料。

结果

我院共有6例肺癌伴EEM入选，发生率为0.62%（6/967），均为初诊为肺癌时即伴有EEM。鳞癌4例，腺癌1例，小细胞肺癌1例。Ⅲb期1例，Ⅳ期5例。中央型肺癌5例，周围型1例。EEM在支气管镜下表现为肺癌原发灶之外的气道黏膜结节或息肉性病变5例、局灶性黏膜异常1例。转移至对侧支气管5例，至同侧支气管和气管各1例。中位总生存期为7.5个月。从PubMed数据库共检索到13例，其中12例为肺癌术后随诊胸部计算机断层扫描（computed tomography, CT）异常继而确诊为EEM。中央型9例，鳞癌8例，EEM在CT上表现为腔内结节10例，气管壁局限增厚2例，支气管镜下均表现为气道黏膜结节或息肉样病变。转移至气管10例，至对侧支气管5例，至同侧支气管1例。

结论

EEM是原发性肺癌罕见的转移方式，可发生于初诊时，也可发生于术后，多见于晚期中央型鳞癌，预后差。

The Combination of CA125 and NSE Is Useful for Predicting Liver Metastasis of Lung Cancer

Purpose

Liver metastasis is the final stage of cancer progression and is associated with poor prognosis. Although numerous indicators have been identified as having prognostic value for lung cancer and liver metastasis, liver metastases are still not diagnosed by imaging in many patients. To provide a more accurate method for clinical prediction of liver metastasis, we analyzed multiple factors to identify potential predictive factors for liver metastasis of lung cancer.

Methods

Patients first diagnosed with lung cancer between 2002 and 2016 (n = 1746) were divided into two groups, with and without liver metastasis. Serum concentrations of calcium, carcinoembryonic antigen (CEA), cancer antigen-125 (CA125), cancer antigen-153 (CA153), carbohydrate antigen-199 (CA199), cytokeratin fraction 21-1 (CYFRA21-1), total prostate-specific antigen (TPSA), and neuron-specific enolase (NSE) were analyzed in both patient groups.

Results

There was no significant difference in age or sex between the two groups. CA125 and NSE were significantly associated with liver metastasis. Compared with CA125, NSE was more specific, while it was less sensitive (P < 0.001). Further analysis of NSE concentrations was conducted in patients with non-small-cell lung cancer and indicated that NSE concentration differed significantly between those with and without liver metastasis (P = 0.023). We conducted analysis with NSE and CA125 combined, resulting in acceptable sensitivity (51.2%), specificity (72.6%), and area under the curve (0.64) values; sensitivity and area under the curve values were higher than those for individual factors, while specificity was higher than that for CA125.

Conclusions

The combination of CA125 and NSE can assist prediction of liver metastasis of lung cancer, providing improved diagnostic accuracy.

Mechanisms and Future of Non-Small Cell Lung Cancer Metastasis

Lung cancer, renowned for its fast progression and metastatic potency, is rising to become a leading cause of death globally. It has been long observed that lung cancer is particularly ept in spawning distant metastasis at its early stages, and it can readily colonize virtually any human organ. In recent years, cancer research has shed light on why lung cancer is endowed with its exceptional ability to metastasize. In this review, we will take a comprehensive look at the current research on lung cancer metastasis, including molecular pathways, anatomical features and genetic traits that make lung cancer intrinsically metastatic, as we go from lung cancer’s general metastatic potential to the particular metastasis mechanisms in multiple organs. We highly concerned about the advanced discovery and development of lung cancer metastasis, indicating the importance of lung cancer specific gene mutations, heterogeneity or biomarker discovery, and discussing potential opportunities and challenges. We will also introduce some current treatments that targets certain metastatic strategies of non-small cell lung cancer (NSCLC). Advances made in these regards could be critical to our current knowledge base of lung cancer metastasis.

Prognostic Prediction Models Based on Clinicopathological Indices in Patients With Resectable Lung Cancer

Serum enzymes, blood cytology indices, and pathological features are associated with the prognosis of patients with lung cancer, and we construct prognostic prediction models based on clinicopathological indices in patients with resectable lung cancer. The study includes 420 patients with primary lung cancer who underwent pneumonectomy. Cox proportional hazards regression was conducted to analyze the prognostic values of individual clinicopathological indices. The prediction accuracies of models for overall survival (OS) and progression-free survival (PFS) were estimated through Harrell’s concordance indices (C-index) and Brier scores. Nomograms of the prognostic models were plotted for individualized evaluations of death and cancer progression. We find that the prognostic model based on alkaline phosphatase (ALP), lactate dehydrogenase (LDH), age, history of tuberculosis, and pathological stage present exceptional performance for OS prediction [C-index: 0.74 (95% CI, 0.69-0.79) and Brier score: 0.10], and the prognostic model based on ALP, LDH, and platelet distribution width (PDW), age, pathological stage, and histological type presented outstanding performance for PFS prediction [C-index: 0.71 (95% CI, 0.66-0.75) and Brier score: 0.18]. These findings show that the models based on clinicopathological indices might serve as economic and efficient prognostic tools for resectable lung cancer.

miR-192-5p suppresses the progression of lung cancer bone metastasis by targeting TRIM44

Lung cancer is the leading cause of cancer-related deaths worldwide, with 50–70% of patients suffering from bone metastasis. Accumulating evidence has demonstrated that miRNAs are involved in cell proliferation, migration, and invasion in malignancy, such as lung cancer bone metastasis. In the present study, we demonstrated that reduced miR-192-5p and increased TRIM44 levels were associated with the proliferation, migration and invasion of lung cancer. Furthermore, the potential functions of miR-192-5p were explored in A549 and NCI-H1299 cells. We found that miR-192-5p upregulation suppressed tumour behaviours in lung cancer cells. To further investigate whether miR-192-5p is associated with TRIM44, we used TargetScan software to predict the binding site between miR-192-5p and TRIM44. Luciferase activity assays were performed to verify this prediction. In addition, the significant role of miR-192-5p in negatively regulating TRIM44 expression was manifested by our research group. our results suggest that miR-192-5p inhibited the proliferation, migration and invasion of lung cancer through TRIM44.

Clinical associations and prognostic value of site-specific metastases in non-small cell lung cancer: A population-based study

The prognosis of non-small cell lung cancer (NSCLC) is poor, particularly for patients with metastatic disease. Numerous efforts have been made to improve the prognosis of these patients; however, only a small number of studies have explored the occurrence rate and prognostic value of different patterns of distant metastasis (DM) in NSCLC systematically. To investigate these, information from patients diagnosed with NSCLC between 2010 and 2014 was collected from the Surveillance, Epidemiology and End Results database. Survival rate comparisons were performed using Kaplan-Meier analysis and log-rank tests. A Cox proportional hazard model was established to determine factors associated with improved overall survival (OS) and cancer-specific survival (CSS). The present study revealed that the most common site of single metastasis occurrence was bone, and the least common was the liver for NSCLC. As for multi-site metastases, the most common two-site metastasis involved bone and lung, and the most common three-site metastasis involved bone, liver and lung. As for NSCLC subtypes, large cell carcinoma (LCC) exhibited more specific metastatic features. The most common single metastatic site was the brain for patients with LCC, and the most common two-site metastatic combination was bone and liver. Patients with isolated liver metastasis exhibited the worst OS and CSS among patients with single metastasis. Furthermore, for patients with multi-site metastases, metastases involving the liver were associated with the worst OS and CSS among various combinations. To the best of our knowledge, the present study is the first to investigate the occurrence rate and prognostic value of different metastatic patterns of site-specific DM for NSCLC using a large population-based dataset. The findings of the present study may have vital implications for classifying patients with advanced NSCLC, thus laying a foundation for individualized precise treatment.