Identification of a high-risk group for brain metastases in non-small cell lung cancer patients

Circ_0006324 regulates cell proliferation, cell-cycle progression, apoptosis, and glycolysis of non-small cell lung cancer cells through miR-496/TRIM59 axis

Increasing evidence suggests that circular RNA (circRNA) plays an important role in non-small cell lung cancer (NSCLC) progression. This study aimed to investigate the role and potential molecular mechanism of circ_0006324 in NSCLC. The expression levels of circ_0006324, miR-496, miR-488-5p, and tripartite motif-containing 59 (TRIM59) mRNA were determined by quantitative real-time polymerase chain reaction (PCR). 3-(4,5-dimethyl-2-thiazolyl)-2,5-diphenyl-2-H-tetrazolium bromide assay, EdU assay, and flow cytometry were carried out to evaluate cell proliferation and apoptosis. The extracellular acidification rate and lactic acid production were examined to assess cell glycolysis. Western blot assay was used to detect protein levels. The target relationship of circ_0006324/miR-496/TRIM59 axis was validated by RNA pull-down assay, dual luciferase reporter assay, and radio immunoprecipitation assay. Xenograft tumor assay was performed to reveal the function of circ_0006324 in vivo. Circ_0006324 was upregulated in NSCLC and related to tumor node metastasis stage and distant metastasis. Knockdown of circ_00006324 impeded NSCLC cell proliferation, glycolysis, and promoted cell apoptosis. MiR-496 was verified as a target of circ_0006324 and circ_00006324 mediated the altering of cell proliferation, apoptosis, and glycolysis of NSCLC cells through targeting miR-496. TRIM59 was verified as a target of miR-496, and circ_0006324 positively regulated TRIM59 expression by targeting miR-496. Overexpression of TRIM59 could reverse the effects of circ_0006324 silencing on the proliferation, apoptosis, and glycolysis of NSCLC cells. Circ_0006324 knockdown impeded NSCLC tumor growth in vivo. Circ_0006324 functioned as a tumor promoter in NSCLC to promote cell proliferation, cell cycle progression, and glycolysis and inhibit cell apoptosis via miR-496/TRIM59 axis.

Perilesional edema diameter associated with brain metastases as a predictive factor of response to radiotherapy in non-small cell lung cancer

Background

Different prognostic scales exist in patients with brain metastasis, particularly in lung cancer. The Graded Prognostic Assessment for lung cancer using molecular markers (Lung-molGPA index) for brain metastases is a powerful prognostic tool that effectively identifies patients at different risks. However, these scales do not include perilesional edema diameter (PED) associated with brain metastasis. Current evidence suggests that PED might compromise the delivery and efficacy of radiotherapy to treat BM. This study explored the association between radiotherapy efficacy, PED extent, and gross tumor diameter (GTD).

Aim

The aim of this study was to evaluate the intracranial response (iORR), intracranial progression-free survival (iPFS), and overall survival (OS) according to the extent of PED and GT.

Methods

Out of 114 patients with BM at baseline or throughout the disease, 65 were eligible for the response assessment. The GTD and PED sum were measured at BM diagnosis and after radiotherapy treatment. According to a receiver operating characteristic (ROC) curve analysis, cutoff values were set at 27 mm and 17 mm for PED and GT, respectively.

Results

Minor PED was independently associated with a better iORR [78.8% vs. 50%, OR 3.71 (95% CI 1.26-10.99); p = 0.018] to brain radiotherapy. Median iPFS was significantly shorter in patients with major PED [6.9 vs. 11.8 months, HR 2.9 (95% CI 1.7-4.4); p < 0.001] independently of other prognostic variables like the Lung-molGPA and GTD. A major PED also negatively impacted the median OS [18.4 vs. 7.9 months, HR 2.1 (95% CI 1.4-3.3); p = 0.001].

Conclusion

Higher PED was associated with an increased risk of intracranial progression and a lesser probability of responding to brain radiotherapy in patients with metastatic lung cancer. We encourage prospective studies to confirm our findings.

Prediction of adenocarcinoma and squamous carcinoma based on CT perfusion parameters of brain metastases from lung cancer: a pilot study

Objectives

Predicting pathological types in patients with adenocarcinoma and squamous carcinoma using CT perfusion imaging parameters based on brain metastasis lesions from lung cancer.

Methods

We retrospectively studied adenocarcinoma and squamous carcinoma patients with brain metastases who received treatment and had been pathologically tested in our hospital from 2019 to 2021. CT perfusion images of the brain were used to segment enhancing tumors and peritumoral edema and to extract CT perfusion parameters. The most relevant perfusion parameters were identified to classify the pathological types. Of the 45 patients in the study cohort (mean age 65.64 ± 10.08 years; M:F = 24:21), 16 were found to have squamous cell carcinoma. Twenty patients were with brain metastases only, and 25 patients were found to have multiple organ metastases in addition to brain metastases. After admission, all patients were subjected to the CT perfusion imaging examination. Differences in CT perfusion parameters between adenocarcinoma and squamous carcinoma were analyzed. The receiver operating characteristic (ROC) curves were used to predict the types of pathology of the patients.

Results

Among the perfusion parameters, cerebral blood flow (CBF) and mean transit time (MTT) were significantly different between the two lung cancers (adenocarcinoma vs. squamous cell carcinoma: p < 0.001, p = 0.012.). Gender and tumor location were identified as the clinical predictive factors. For the classification of adenocarcinoma and squamous carcinoma, the model combined with CBF and clinical predictive factors showed better performance [area under the curve (AUC): 0.918, 95% confidence interval (CI): 0.797-0.979). The multiple organ metastasis model showed better performance than the brain metastasis alone model in subgroup analyses (AUC: 0.958, 95% CI: 0.794-0.999).

Conclusion

CT perfusion parameter analysis of brain metastases in patients with primary lung cancer could be used to classify adenocarcinoma and squamous carcinoma.

Individual risk and prognostic value prediction by machine learning for distant metastasis in pulmonary sarcomatoid carcinoma: a large cohort study based on the SEER database and the Chinese population

Background

This study aimed to develop diagnostic and prognostic models for patients with pulmonary sarcomatoid carcinoma (PSC) and distant metastasis (DM).

Methods

Patients from the Surveillance, Epidemiology, and End Results (SEER) database were divided into a training set and internal test set at a ratio of 7 to 3, while those from the Chinese hospital were assigned to the external test set, to develop the diagnostic model for DM. Univariate logistic regression was employed in the training set to screen for DM-related risk factors, which were included into six machine learning (ML) models. Furthermore, patients from the SEER database were randomly divided into a training set and validation set at a ratio of 7 to 3 to develop the prognostic model which predicts survival of patients PSC with DM. Univariate and multivariate Cox regression analyses have also been performed in the training set to identify independent factors, and a prognostic nomogram for cancer-specific survival (CSS) for PSC patients with DM.

Results

For the diagnostic model for DM, 589 patients with PSC in the training set, 255 patients in the internal and 94 patients in the external test set were eventually enrolled. The extreme gradient boosting (XGB) algorithm performed best on the external test set with an area under the curve (AUC) of 0.821. For the prognostic model, 270 PSC patients with DM in the training and 117 patients in the test set were enrolled. The nomogram displayed precise accuracy with AUC of 0.803 for 3-month CSS and 0.869 for 6-month CSS in the test set.

Conclusion

The ML model accurately identified individuals at high risk for DM who needed more careful follow-up, including appropriate preventative therapeutic strategies. The prognostic nomogram accurately predicted CSS in PSC patients with DM.

Brain parenchymal and leptomeningeal metastasis in non-small cell lung cancer

Patients with advanced non-small cell lung cancer (NSCLC) are prone to brain metastases (BM), which essentially include brain parenchymal metastases (PM) and leptomeningeal metastases (LM). We conducted a retrospective study to comprehensively assess the clinical characteristics and risk factors of patients with advanced NSCLC who develop PM and LM. Patients with advanced NSCLC were enrolled. These patients were then divided into three groups for analysis: patients without BM (No-BM), patients with PM and patients with LM. Data on clinical characteristics of each patient at the time of diagnosis advanced NSCLC were extracted and analyzed. In addition, prediction models were developed and evaluated for PM and LM. A total of 592 patients were enrolled in the study. BM was present in 287 patients (48.5%). Among them, 185 and 102 patients had PM or LM. Patients with LM had a higher proportion of EGFR exon 21point mutations (L858R) compared to patients with No-BM and PM (p < 0.0001). The median time to the onset of PM and LM from the diagnosis of advanced NSCLC was 0 months and 8.3 months, respectively. Patients with LM had a statistically shorter over survival (OS) compared to either No-BM or PM patients (p < 0.0001). Based on independent predictive variables, two nomogram models were constructed to predict the development of PM and LM in advanced NSCLC patients, and the C-indexes were 0.656 and 0.767, respectively. Although both considered as BM, PM and LM had different clinical characteristics. And the nomogram showed good performance in predicting LM development, but not PM.

Machine Learning for the Prediction of Synchronous Organ-Specific Metastasis in Patients With Lung Cancer

Background

This study aimed to develop an artificial neural network (ANN) model for predicting synchronous organ-specific metastasis in lung cancer (LC) patients.

Methods

A total of 62,151 patients who diagnosed as LC without data missing between 2010 and 2015 were identified from Surveillance, Epidemiology, and End Results (SEER) program. The ANN model was trained and tested on an 75/25 split of the dataset. The receiver operating characteristic (ROC) curves, area under the curve (AUC) and sensitivity were used to evaluate and compare the ANN model with the random forest model.

Results

For distant metastasis in the whole cohort, the ANN model had metrics AUC = 0.759, accuracy = 0.669, sensitivity = 0.906, and specificity = 0.613, which was better than the random forest model. For organ-specific metastasis in the cohort with distant metastasis, the sensitivity in bone metastasis, brain metastasis and liver metastasis were 0.913, 0.906 and 0.925, respectively. The most important variable was separate tumor nodules with 100% importance. The second important variable was visceral pleural invasion for distant metastasis, while histology for organ-specific metastasis.

Conclusions

Our study developed a “two-step” ANN model for predicting synchronous organ-specific metastasis in LC patients. This ANN model may provide clinicians with more personalized clinical decisions, contribute to rationalize metastasis screening, and reduce the burden on patients and the health care system.