A nomogram to predict brain metastasis as the first relapse in curatively resected non-small cell lung cancer patients

The clinical, molecular, and therapeutic implications of time from primary diagnosis to brain metastasis in lung and breast cancer patients

PURPOSE

Lung cancer (LC) and breast cancer (BC) are the most common causes of brain metastases (BMs). Time from primary diagnosis to BM (TPDBM) refers to the time interval between initial LC or BC diagnosis and development of BM. This research aims to identify clinical, molecular, and therapeutic risk factors associated with shorter TPDBM.

METHODS

We retrospectively reviewed all diagnosed LC and BC patients with BM at Harbin Medical University Cancer Hospital from 2016 to 2020. A total of 570 patients with LC brain metastasis (LCBM) and 173 patients with breast cancer brain metastasis (BCBM) patients who met the inclusion criteria were enrolled for further analysis. BM free survival time curves were generated using Kaplan-Meier analyses. Univariate and multivariate Cox regression analyses were applied to identify risk factors associated with earlier development of BM in LC and BC, respectively.

RESULTS

The median TPDBM was 5.3 months in LC and 44.4 months in BC. In multivariate analysis, clinical stage IV and M1 stage were independent risk factors for early development of LCBM. LC patients who received chemotherapy, targeted therapy, pulmonary radiotherapy, and pulmonary surgery had longer TPDBM. For BC patients, age ≥ 50 years, Ki67 ≥ 0.3, HER2 positive or triple-negative breast cancer subtype, advanced N stage, and no mastectomy were correlated with shorter TPDBM.

CONCLUSIONS

This single-institutional study helps identify patients who have a high risk of developing BM early. For these patients, early detection and intervention could have clinical benefits.

Survival Predictive Nomograms for Non-Surgical Brain Metastases Patients From Non-Small Cell Lung Cancer Receiving Radiotherapy: A Population-Based Study

OBJECTIVE

A high number of Non-Small Cell Lung Cancer (NSCLC) patients with brain metastasis who have not had surgery often have a negative outlook. Radiotherapy remains a most common and effective method. Nomograms were developed to forecast the cancer-specific survival (CSS) and overall survival (OS) in NSCLC individuals with nonoperative brain metastases who underwent radiotherapy.

METHODS

Information was gathered from the Surveillance, Epidemiology, and End Results (SEER) database about patients diagnosed with NSCLC who had brain metastases not suitable for surgery. Nomograms were created and tested using multivariate Cox regression models to forecast CSS and OS at intervals of 1, 2, and 3 years.

RESULTS

The research involved 3413 individuals diagnosed with NSCLC brain metastases who had undergone radiotherapy but had not experienced surgery. These participants were randomly divided into two categories. The analysis revealed that gender, age, ethnicity, marital status, tumor location, tumor laterality, tumor grade, histology, T stage, N stage, chemotherapy, tumor size, lung metastasis, bone metastasis, and liver metastasis were significant independent predictors for OS and CSS. The C-index for the training set for predicting OS was .709 (95% CI, .697-.721), and for the validation set, it was .705 (95% CI, .686-.723), respectively. The C-index for predicting CSS was .710 (95% CI, .697-.722) in the training set and .703 (95% CI, .684-.722) in the validation set, respectively. The nomograms model, as suggested by the impressive C-index, exhibits outstanding differentiation ability. Moreover, the ROC and calibration curves reveal its commendable precision and distinguishing potential.

CONCLUSIONS

For the first time, highly accurate and reliable nomograms were developed to predict OS and CSS in NSCLC patients with non-surgical brain metastases, who have undergone radiotherapy treatment. The nomograms may assist in tailoring counseling strategies and choosing the most effective treatment method.

A novel risk signature for predicting brain metastasis in patients with lung adenocarcinoma

BACKGROUND

Brain metastasis (BM) are a devastating consequence of lung cancer. This study was aimed to screen risk factors for predicting BM.

METHODS

Using an in vivo BM preclinical model, we established a series of lung adenocarcinoma (LUAD) cell subpopulations with different metastatic ability. Quantitative proteomics analysis was used to screen and identify the differential protein expressing map among subpopulation cells. Q-PCR and Western-blot were used to validate the differential proteins in vitro. The candidate proteins were measured in LUAD tissue samples (n = 81) and validated in an independent TMA cohort (n = 64). A nomogram establishment was undertaken by performing multivariate logistic regression analysis.

RESULTS

The quantitative proteomics analysis, qPCR and Western blot assay implied a five-gene signature that might be key proteins associated with BM. In multivariate analysis, the occurrence of BM was associated with age ≤ 65 years, high expressions of NES and ALDH6A1. The nomogram showed an area under the receiver operating characteristic curve (AUC) of 0.934 (95% CI, 0.881-0.988) in the training set. The validation set showed a good discrimination with an AUC of 0.719 (95% CI, 0.595-0.843).

CONCLUSIONS

We have established a tool that is able to predict occurrence of BM in LUAD patients. Our model based on both clinical information and protein biomarkers will help to screen patient in high-risk population of BM, so as to facilitate preventive intervention in this part of the population.

Machine-Learning-Aided Prediction of Brain Metastases Development in Non-Small-Cell Lung Cancers

PURPOSE

Non-small-cell lung cancer (NSCLC) shows a high incidence of brain metastases (BM). Early detection is crucial to improve clinical prospects. We trained and validated classifier models to identify patients with a high risk of developing BM, as they could potentially benefit from surveillance brain MRI.

METHODS

Consecutive patients with an initial diagnosis of NSCLC from January 2011 to April 2019 and an in-house chest-CT scan (staging) were retrospectively recruited at a German lung cancer center. Brain imaging was performed at initial diagnosis and in case of neurological symptoms (follow-up). Subjects lost to follow-up or still alive without BM at the data cut-off point (12/2020) were excluded. Covariates included clinical and/or 3D-radiomics-features of the primary tumor from staging chest-CT. Four machine learning models for prediction (80/20 training) were compared. Gini Importance and SHAP were used as measures of importance; sensitivity, specificity, area under the precision-recall curve, and Matthew's Correlation Coefficient as evaluation metrics.

RESULTS

Three hundred and ninety-five patients compromised the clinical cohort. Predictive models based on clinical features offered the best performance (tuned to maximize recall: sensitivity∼70%, specificity∼60%). Radiomics features failed to provide sufficient information, likely due to the heterogeneity of imaging data. Adenocarcinoma histology, lymph node invasion, and histological tumor grade were positively correlated with the prediction of BM, age, and squamous cell carcinoma histology were negatively correlated. A subgroup discovery analysis identified 2 candidate patient subpopulations appearing to present a higher risk of BM (female patients + adenocarcinoma histology, adenocarcinoma patients + no other distant metastases).

CONCLUSION

Analysis of the importance of input features suggests that the models are learning the relevant relationships between clinical features/development of BM. A higher number of samples is to be prioritized to improve performance. Employed prospectively at initial diagnosis, such models can help select high-risk subgroups for surveillance brain MRI.

The Impact of Time Interval on Prognosis in Patients with Non-Small Cell Lung Cancer Brain Metastases After Metastases Surgery

BACKGROUND

Non-small cell lung cancer (NSCLC) is a prominent malignancy often linked to the development of brain metastases (BM), which commonly appear at diverse time intervals (TI) following the lung cancer diagnosis. This study endeavors to determine the prognostic significance of the time interval in patients with NSCLC who undergo BM surgery. Through this investigation, we aim to improve our understanding of the factors impacting the prognosis of BM cases originating from NSCLC.

METHODS

We analyzed data from 74 patients (2011-2021) who underwent BM surgery at our institution. The relationship between various clinical, radiological, and histopathological factors, as well as TI and overall survival (OS), was examined.

RESULTS

The median TI from initial NSCLC diagnosis to BM surgery was 19 months (range: 9-36 months). Notably, a shorter TI of less than 23 months was found to be independently associated with postoperative survival (adjusted odds ratio [aOR] 2.87, 95% confidence interval [CI] 1.03-8.02, P = 0.045). Additionally, a shorter TI was independently correlated with the absence of adjuvant chemotherapy for NSCLC (aOR 0.25, 95% CI 0.07-0.83, P = 0.023) and lack of targeted therapy (aOR 0.02, 95% CI 0.00-0.16, P < 0.001). Late-onset BM (TI ≥ 36 months) was observed in 15 cases (20.3%), in this subgroup, patients aged 60 years or older at the time of lung cancer diagnosis exhibited a significant independent correlation with late-onset BM (aOR 7.24, 95% CI 1.59-32.95, P = 0.011). NSCLC patients who underwent adjuvant chemotherapy displayed a notable correlation with late-onset BM (aOR 6.46, 95% CI 1.52-27.43, P = 0.011), while those who received targeted therapy also exhibited an independent association (aOR 2.27, 95% CI 1.70-3.03, P < 0.001).

CONCLUSIONS

Multiple factors contribute to the variability in the onset interval of BM subsequent to NSCLC diagnosis. The occurrence of BM within TI < 23 months following the initial diagnosis of NSCLC was demonstrated as an independent factor associated with an unfavorable prognosis following BM surgery. Furthermore, patients with NSCLC who did not receive adjuvant chemotherapy and lacked targeted therapy were shown to have an elevated likelihood of developing BM after a long progression-free survival.

Radiological artificial intelligence - predicting personalized immunotherapy outcomes in lung cancer

Personalized medicine has revolutionized approaches to treatment in the field of lung cancer by enabling therapies to be specific to each patient. However, physicians encounter an immense number of challenges in providing the optimal treatment regimen for the individual given the sheer complexity of clinical aspects such as tumor molecular profile, tumor microenvironment, expected adverse events, acquired or inherent resistance mechanisms, the development of brain metastases, the limited availability of biomarkers and the choice of combination therapy. The integration of innovative next-generation technologies such as deep learning-a subset of machine learning-and radiomics has the potential to transform the field by supporting clinical decision making in cancer treatment and the delivery of precision therapies while integrating numerous clinical considerations. In this review, we present a brief explanation of the available technologies, the benefits of using these technologies in predicting immunotherapy response in lung cancer, and the expected future challenges in the context of precision medicine.

Development and validation of an RBP gene signature for prognosis prediction in colorectal cancer based on WGCNA

BACKGROUND

RNA binding proteins (RBPs) have been implicated in oncogenesis and progression in various cancers. However, the potential value of RBPs as prognostic indicators and therapeutic targets in colorectal cancer (CRC) requires further investigation.

METHODS

Four thousand eighty two RBPs were collected from literature. The weighted gene co-expression network analysis (WGCNA) was performed to identify prognosis-related RBP gene modules based on the data attained from the TCGA cohorts. LASSO algorithm was conducted to establish a prognostic risk model, and the validity of the proposed model was confirmed by an independent GEO dataset. Functional enrichment analysis was performed to reveal the potential biological functions and pathways of the signature and to estimate tumor immune infiltration. Potential therapeutic compounds were inferred utilizing CMap database. Expressions of hub genes were further verified through the Human Protein Atlas (HPA) database and RT-qPCR.

RESULTS

One thousand seven hundred thirty four RBPs were differently expressed in CRC samples and 4 gene modules remarkably linked to the prognosis were identified, based on which a 12-gene signature was established for prognosis prediction. Multivariate Cox analysis suggested this signature was an independent predicting factor of overall survival (P < 0.001; HR:3.682; CI:2.377-5.705) and ROC curves indicated it has an effective predictive performance (1-year AUC: 0.653; 3-year AUC:0.673; 5-year AUC: 0.777). GSEA indicated that high risk score was correlated with several cancer-related pathways, including cytokine-cytokine receptor cross talk, ECM receptor cross talk, HEDGEHOG signaling cascade and JAK/STAT signaling cascade. ssGSEA analysis exhibited a significant correlation between immune status and the risk signature. Noscapine and clofazimine were screened as potential drugs for CRC patients with high-risk scores. TDRD5 and GPC1 were identified as hub genes and their expression were validated in 15 pairs of surgically resected CRC tissues.

CONCLUSION

Our research provides a depth insight of RBPs' role in CRC and the proposed signature are helpful to the personalized treatment and prognostic judgement.

Development and validation of a novel model for predicting the survival of bladder cancer based on ferroptosis-related genes

The role of ferroptosis, a new form of cell death, in bladder cancer (BC) has not been sufficiently studied. This study aimed to establish a prognostic prediction model for BC patients based on the expression profile of ferroptosis-related genes (FRG). The expression profiles of BC samples with clinical information were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). A total of 80 differentially expressed genes (DEGs) related to FRG were identified among which 37 DEGs were found to have a prognostic value. Eleven genetic markers including SLC2A12, CDO1, JDP2, MAFG, CAPG, RRM2, SLC2A3, SLC3A2, VDAC2, GCH1, and ANGPTL7 were identified through the LASSO regression analysis. The ROC curve analysis showed that the AUC was 0.702, 0.664, and 0.655 for the 1-year, 3-year, and 5-year survival outcomes, respectively. The prediction performance was verified in the TCGA-testing set and external set GSE13507. Multivariate Cox proportional hazards analysis showed that the risk score was an independent prognostic predictor. Moreover, we found differences in gene mutation, gene expression, and immune cell infiltration between the high and low-risk groups of BC patients. Finally, a nomogram was constructed by integrating clinical features and FRG signatures to predict the survival outcomes of BC patients. In addition, the differential expression of FRG mRNA and protein was verified through PCR and HPA online site. The characteristics of 11 FRG genes were examined and a prognostic nomogram for predicting the overall survival of BC was established.

Predictive value of baseline metabolic tumor burden on 18F-FDG PET/CT for brain metastases in patients with locally advanced non-small-cell lung cancer

OBJECTIVES

Brain metastases (BMs) are a major cause leading to the failure of treatment management for non-small-cell lung cancer (NSCLC) patients. The purpose of this study was to evaluate the predictive value of baseline metabolic tumor burden on 18F-FDG PET/CT measured with metabolic tumor volume (MTV) and total lesion glycolysis (TLG) for brain metastases (BMs) development in patients with locally advanced non-small-cell lung cancer (NSCLC) after treatment.

METHODS

Forty-seven patients with stage IIB-IIIC NSCLC who underwent baseline 18F-FDG PET/CT examinations were retrospectively reviewed. The maximum standardized uptake value (SUVmax), MTV, and TLG of the primary tumor (SUVmaxT, MTVT, and TLGT), metastatic lymph nodes (SUVmaxN, MTVN, and TLGN), and whole-body tumors (SUVmaxWB, MTVWB, and TLGWB) were measured. The optimal cut-off values of PET parameters to predict brain metastasis-free survival were obtained using Receiver operating characteristic (ROC) analysis, and the predictive value of clinical variables and PET parameters were evaluated using Cox proportional hazards regression analysis.

RESULTS

The median follow-up duration was 25.0 months for surviving patients, and 13 patients (27.7%) developed BM. The optimal cut-off values were 21.1 mL and 150.0 g for MTVT and TLGT, 20.0, 10.9 mL and 55.6 g for SUVmaxN, MTVN and TLGN, and 27.9, 27.4 mL and 161.0 g for SUVmaxWB, MTVWB and TLGWB, respectively. In the Cox proportional hazards models, the risk of BM was significantly associated with MTVN and MTVWB or TLGN and TLGWB after adjusting for histological cell type, N stage, SUVmaxN, and SUVmaxWB.

CONCLUSIONS

Baseline metabolic tumor burden (MTV and TLG) evaluated from the level of metastatic lymph nodes and whole-body tumors are significant predictive factors for BM development in patients with locally advanced NSCLC.

A nomogram for predicting overall survival rate in patients with brain metastatic non-small cell lung cancer

The purpose was to develop a nomogram for the prediction of the 1- and 2-year overall survival (OS) rates in patients with brain metastatic non-small cell lung cancer (BMNSCLC). Patients were collected from the Surveillance Epidemiology and End Results program (SEER) and classified into the training and validation groups. Several independent prognostic factors identified by statistical methods were incorporated to establish a predictive nomogram. The concordance index (C-index), the area under the receiver operating characteristics curve (AUC), and calibration curve were applied to estimate predictive ability of the nomogram. To compare the clinical practicability of the nomogram and TNM staging system by decision curve analysis (DCA). A total of 24,164 eligible patients were collected and assigned into the training (n = 16,916) and validation groups (n = 7248). Based on the prognostic factors, we developed a nomogram with good discriminative ability. The C-indices for training and validation group were 0.727 and 0.728. The AUCs of 1- and 2-year OS rates were both 0.8, and the calibration curves also demonstrated good performance of the nomogram. DCA illustrated that the nomogram provided clinical net benefit compared with the TNM staging system. We developed a predictive nomogram for more accurate and comprehensive prediction of OS in BMNSCLC patients, which can be a useful and convenient tool for clinicians to make proper clinical decisions, and adjust follow-up management strategies.

Investigation of the added value of CT-based radiomics in predicting the development of brain metastases in patients with radically treated stage III NSCLC

Introduction:

Despite radical intent therapy for patients with stage III non-small-cell lung cancer (NSCLC), cumulative incidence of brain metastases (BM) reaches 30%. Current risk stratification methods fail to accurately identify these patients. As radiomics features have been shown to have predictive value, this study aims to develop a model combining clinical risk factors with radiomics features for BM development in patients with radically treated stage III NSCLC.

Methods:

Retrospective analysis of two prospective multicentre studies. Inclusion criteria: adequately staged [¹⁸F-fluorodeoxyglucose positron emission tomography-computed tomography (18-FDG-PET-CT), contrast-enhanced chest CT, contrast-enhanced brain magnetic resonance imaging/CT] and radically treated stage III NSCLC, exclusion criteria: second primary within 2 years of NSCLC diagnosis and prior prophylactic cranial irradiation. Primary endpoint was BM development any time during follow-up (FU). CT-based radiomics features (N = 530) were extracted from the primary lung tumour on 18-FDG-PET-CT images, and a list of clinical features (N = 8) was collected. Univariate feature selection based on the area under the curve (AUC) of the receiver operating characteristic was performed to identify relevant features. Generalized linear models were trained using the selected features, and multivariate predictive performance was assessed through the AUC.

Results:

In total, 219 patients were eligible for analysis. Median FU was 59.4 months for the training cohort and 67.3 months for the validation cohort; 21 (15%) and 17 (22%) patients developed BM in the training and validation cohort, respectively. Two relevant clinical features (age and adenocarcinoma histology) and four relevant radiomics features were identified as predictive. The clinical model yielded the highest AUC value of 0.71 (95% CI: 0.58–0.84), better than radiomics or a combination of clinical parameters and radiomics (both an AUC of 0.62, 95% CIs of 0.47–076 and 0.48–0.76, respectively).

Conclusion:

CT-based radiomics features of primary NSCLC in the current setup could not improve on a model based on clinical predictors (age and adenocarcinoma histology) of BM development in radically treated stage III NSCLC patients.

[Research Progress on Risk Factors of Brain Metastasis in Non-small Cell Lung Cancer]

Brain metastasis of non-small cell lung cancer (NSCLC) is a common treatment failure mode, and the median survival time of NSCLC patients with brain metastasis is only 1 mon-2 mon. Prophylactic cranial irradiation (PCI) can delay the occurrence of brain metastasis, but the survival benefits of NSCLC patients are still controversial. It is particularly important to identify the patients who are most likely to benefit from PCI. This article reviews the high risk factors of brain metastasis in NSCLC.
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Pretreatment Thoracic CT Radiomic Features to Predict Brain Metastases in Patients With ALK-Rearranged Non-Small Cell Lung Cancer

Objective: To identify CT imaging biomarkers based on radiomic features for predicting brain metastases (BM) in patients with ALK-rearranged non-small cell lung cancer (NSCLC).

Methods: NSCLC patients with pathologically confirmed ALK rearrangement from January 2014 to December 2020 in our hospital were enrolled retrospectively in this study. Finally, 77 patients were included according to the inclusion and exclusion criteria. Patients were divided into two groups: BM+ were those patients who were diagnosed with BM at baseline examination (n = 16) or within 1 year’s follow-up (n = 14), and BM− were those without BM followed up for at least 1 year (n = 47). Radiomic features were extracted from the pretreatment thoracic CT images. Sequential univariate logistic regression, LASSO regression, and backward stepwise logistic regression were used to select radiomic features and develop a BM-predicting model.

Results: Five robust radiomic features were found to be independent predictors of BM. AUC for radiomics model was 0.828 (95% CI: 0.736–0.921), and when combined with clinical features, the AUC was increased (p = 0.017) to 0.909 (95% CI: 0.845–0.972). The individualized BM-predicting model incorporated with clinical features was visualized by the nomogram.

Conclusion: Radiomic features extracted from pretreatment thoracic CT images have the potential to predict BM within 1 year after detection of the primary tumor in patients with ALK-rearranged NSCLC. The radiomics model incorporated with clinical features shows improved risk stratification for such patients.

Construction and validation of m6 A RNA methylation regulators associated prognostic model for gastrointestinal cancer

N6-methyladenosine (m⁶ A) RNA methylation is correlated with carcinogenesis and dynamically possessed through the m⁶ A RNA methylation regulators. This paper aimed to explore 13 m⁶ A RNA methylation regulators' role in gastrointestinal cancer (GIC) and determine the risk model and prognosis value of m⁶ A RNA methylation regulators in GIC. We used several bioinformatics methods to identify the differential expression of m⁶ A RNA methylation regulators in GIC, constructed a prognostic model, and carried out functional enrichment analysis. Eleven of 13 m⁶ A RNA methylation regulators were differentially expressed in different clinicopathological characteristics of GIC, and m⁶ A RNA methylation regulators were nearly associated with GIC. We constructed a risk model based on five m⁶ A RNA methylation regulators (METTL3, FTO, YTHDF1, ZC3H13, and WTAP); the risk score is an independent prognosis biomarker. Moreover, the five m⁶ A RNA methylation regulators can also forecast the 1-, 3- and 5-year overall survival through a nomogram. Furthermore, four hallmarks of oxidative phosphorylation, glycolysis, fatty acid metabolism, and cholesterol homoeostasis gene sets were significantly enriched in GIC. m⁶ A RNA methylation regulators were related to the malignant clinicopathological characteristics of GIC and may be used for prognostic stratification and development of therapeutic strategies.

Comprehensive Analysis of Copy Number Variation, Nucleotide Mutation, and Transcription Level of PPAR Pathway-Related Genes in Endometrial Cancer

Endometrial cancer is a common malignant tumor in gynecology, and the prognosis of advanced patients is dismal. Recently, many studies on the peroxisome proliferator-activated receptor pathway have elucidated its crucial involvement in endometrial cancer. Copy number variation (CNA) and nucleotide mutations often occur in tumor tissues, leading to abnormal protein expression and changes in protein structure. We analyzed the exon sequencing data of endometrial cancer patients in the TCGA database and found that somatic changes in PPAR pathway-related genes (PPAR-related-gene) often occur in UCEC patients. Patients with CNA or mutation changes in the exon region of the PPAR-related-gene usually have different prognostic outcomes. Furthermore, we found that the mRNA transcription and protein translation levels of PPAR-related-gene in UCEC are significantly different from that of adjacent tissues/normal uterus. The transcription level of some PPAR-related-gene (DBI, CPT1A, CYP27A1, and ME1) is significantly linked to the prognosis of UCEC patients. We further constructed a prognostic predicting tool called PPAR Risk score, a prognostic prediction tool that is a strong independent risk factor for the overall survival rate of UCEC patients. Comparing to the typical TNM classification system, this tool has higher prediction accuracy. We created a nomogram by combining PPAR Risk score with clinical characteristics of patients in order to increase prediction accuracy and promote clinical use. In summary, our study demonstrated that PPAR-related-gene in UCEC had significant alterations in CNA, nucleotide mutations, and mRNA transcription levels. These findings can provide a fresh perspective for postoperative survival prediction and individualized therapy of UCEC patients.

A Nomogram for Predicting Brain Metastasis in IIIA-N2 Non-Small Cell Lung Cancer After Complete Resection: A Competing Risk Analysis

Background

Brain metastasis (BM) is one of the most common failure patterns of pIIIA-N2 non-small cell lung cancer (NSCLC) after complete resection. Prophylactic cranial irradiation (PCI) can improve intracranial control but not overall survival. Thus, it is particularly important to identify the risk factors that are associated with BM and subsequently provide instructions for selecting patients who will optimally benefit from PCI.

Methods and Materials

Between 2011 and 2014, patients with pIIIA-N2 NSCLC who underwent complete resection in our institution were reviewed and enrolled in the study. Clinical characteristics, pathological parameters, treatment mode, BM time, and overall survival were analyzed. A nomogram was built based on the corresponding parameters by Fine and Gray’s competing risk analysis to predict the 1-, 3-, and 5-year probabilities of BM. Receiver operating characteristic curves and calibration curves were chosen for validation. A statistically significant difference was set as P <0.05.

Results

A total of 517 patients were enrolled in our retrospective study. The median follow-up time for surviving patients was 53.2 months (range, 0.50–123.17 months). The median age was 57 (range, 25–80) years. Of the 517 patients, 122 (23.6%) had squamous cell carcinoma, 391 (75.6%) received adjuvant chemotherapy, and 144 (27.3%) received post-operative radiotherapy. The 1-, 3-, and 5-year survival rates were 94.0, 72.9, and 66.0%, respectively. The 1-, 3-, and 5-year BM rates were 5.4, 15.7, and 22.2%, respectively. According to the univariate analysis, female, non-smokers, patients with non-squamous cell carcinoma, bronchial invasion, perineural invasion, and patients who received adjuvant chemotherapy were more likely to develop BM. In a multivariate analysis, non-squamous cell carcinoma (subdistribution hazard ratios, SHR: 3.968; 95% confidence interval, CI: 1.743–9.040; P = 0.0010), bronchial invasion (SHR: 2.039, 95% CI: 1.325–3.139; P = 0.0012), perineural invasion (SHR: 2.514, 95% CI: 1.058–5.976; P = 0.0370), and adjuvant chemotherapy (SHR: 2.821, 95% CI: 1.424–5.589; P = 0.0030) were independent risk factors for BM. A nomogram model was established based on the final multivariable analysis result. The area under the curve was 0.767 (95% CI, 0.758–0.777).

Conclusions

For patients with IIIA-N2 NSCLC after complete resection, a nomogram was established based on clinicopathological factors and treatment patterns for predicting the BM. Based on this nomogram, patients with a high risk of BM who may benefit from PCI can be screened.

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

PURPOSE

Identification of a high-risk group of brain metastases (BM) in patients with non-small cell lung cancer (NSCLC) could lead to early interventions and probably better prognosis. The objective of the study was to identify this group by generating a multivariable model with recognized and accessible risk factors.

METHODS

A retrospective cohort from patients seen at a single center during 2010-2020, was divided into a training (TD) and validation (VD) datasets, associations with BM were measured in the TD with logit, variables significantly associated were used to generate a multivariate model. Model´s performance was measured with the AUC/C-statistic, Akaike information criterion, and Brier score.

RESULTS

From 570 patients with NSCLC who met the strict eligibility criteria a TD and VD were randomly assembled, no significant differences were found amid both datasets. Variables associated with BM in the multivariate logit analyses were age [P 0.001, OR 0.96 (95% CI 0.93-0.98)]; mutational status positive [P 0.027, OR 1.96 (95% CI 1.07-3.56); and carcinoembryonic antigen levels [P 0.016, OR 1.001 (95% CI 1.000-1.003). BM were diagnosed in 24% of the whole cohort. Stratification into a high-risk group after simplification of the model, displayed a frequency of BM of 63% (P < 0.001).

CONCLUSION

A multivariate model comprising age, carcinoembryonic antigen levels, and mutation status allowed the identification of a truly high-risk group of BM in NSCLC patients.

A Robust Panel Based on Mitochondrial Localized Proteins for Prognostic Prediction of Lung Adenocarcinoma

Due to high energy and material metabolism requirements, mitochondria are frequently active in tumor cells. Our study found that the high energy metabolism status is positively correlated with the poor prognosis of patients with lung adenocarcinoma. We constructed a scoring system (mitoRiskscore) based on the gene expression of specific mitochondrial localized proteins through univariate and LASSO cox regression. It has been shown that high mitoRiskscore was correlated with a shorter survival time after surgery in patients with lung adenocarcinoma. Compared with the typical TNM grading system, the mitoRiskscore gene panel had higher prediction accuracy. A vast number of external verification results ensured its universality. Additionally, the mitoRiskscore could evaluate the metabolic pattern and chemotherapy sensitivity of the tumor samples. Lung adenocarcinoma with higher mitoRiskscore was more active in glycolysis, and oxidative phosphorylation expression of proliferation-related pathway genes was also significantly upregulated. In contrast, patients with low mitoRiskscore had similar metabolic patterns to normal tissues. In order to improve the accuracy of prediction ability and promote clinical usage, we developed a nomogram that combined mitoRiskscore and clinical prognostic factors to predict the 3-year, 5-year, and 10-year survival rates of patients. We also performed in vitro experiments to verify the function of the key genes in the mitoRiskscore panel. In conclusion, the mitoRiskscore scoring system may assist clinicians to judge the postoperative survival rate and chemotherapy of patients with lung adenocarcinoma.

Immune-Related Genes Are Prognostic Markers for Prostate Cancer Recurrence

Background

Prostate cancer (PCa) is an immune-responsive disease. The current study sought to explore a robust immune-related prognostic gene signature for PCa.

Methods

Data were retrieved from the tumor Genome Atlas (TCGA) database and GSE46602 database for performing the least absolute shrinkage and selection operator (LASSO) cox regression model analysis. Immune related genes (IRGs) data were retrieved from ImmPort database.

Results

The weighted gene co-expression network analysis (WGCNA) showed that nine functional modules are correlated with the biochemical recurrence of PCa, including 259 IRGs. Univariate regression analysis and survival analysis identified 35 IRGs correlated with the prognosis of PCa. LASSO Cox regression model analysis was used to construct a risk prognosis model comprising 18 IRGs. Multivariate regression analysis showed that risk score was an independent predictor of the prognosis of PCa. A nomogram comprising a combination of this model and other clinical features showed good prediction accuracy in predicting the prognosis of PCa. Further analysis showed that different risk groups harbored different gene mutations, differential transcriptome expression and different immune infiltration levels. Patients in the high-risk group exhibited more gene mutations compared with those in the low-risk group. Patients in the high-risk groups showed high-frequency mutations in TP53. Immune infiltration analysis showed that M2 macrophages were significantly enriched in the high-risk group implying that it affected prognosis of PCa patients. In addition, immunostimulatory genes were differentially expressed in the high-risk group compared with the low-risk group. BIRC5, as an immune-related gene in the prediction model, was up-regulated in 87.5% of prostate cancer tissues. Knockdown of BIRC5 can inhibit cell proliferation and migration.

Conclusion

In summary, a risk prognosis model based on IGRs was developed. A nomogram comprising a combination of this model and other clinical features showed good accuracy in predicting the prognosis of PCa. This model provides a basis for personalized treatment of PCa and can help clinicians in making effective treatment decisions.

Serum neurofilament light chain or glial fibrillary acidic protein in the diagnosis and prognosis of brain metastases

INTRODUCTION

Brain metastases (BM) remains the most cumbersome disease burden in patients with lung cancer. This study aimed to investigate whether serum brain injury biomarkers can indicate BM, to further establish related diagnostic models, or to predict prognosis of BM.

MATERIALS AND METHODS

This was a prospective study of patients diagnosed with lung cancer with BM (BM group), with lung cancer without BM (NBM group), and healthy participants (control group). Serum neurofilament light chain (NfL) and glial fibrillary acidic protein (GFAP) were detected at baseline. We identified and integrated the risk factors of BM to establish diagnostic models.

RESULTS

A total of 158 patients were included (n = 37, 57, and 64 in the BM, NBM, and control groups, respectively). Serum biomarker levels were significantly higher in the NBM group than in the control group. Higher serum NfL and GFAP concentrations were associated with BM (odds ratios, 3.06 and 1.79, respectively). NfL (area under curve [AUC] = 0.77, p < 0.001) and GFAP (AUC = 0.64, p = 0.02) had diagnostic value for BM. The final diagnostic model included NfL level, age, Karnofsky Performance Status. The model had an AUC value of 0.83 (95% confidence interval [CI] 0.75-0.92). High NfL concentration was correlated with poor overall survival of patients with BM (hazard ratio, 3.31; 95% CI 1.22-9.04; p = 0.019).

CONCLUSION

Serum NfL and GFAP could be potential diagnostic biomarkers for BM in patients with lung cancer. We established a model that can provide individual diagnoses of BM. Higher NfL level may be associated with poor prognosis of patients with BM.

A new nomogram and risk classification system for predicting survival in small cell lung cancer patients diagnosed with brain metastasis: a large population-based study

BACKGROUND

The prognosis of patients with small cell lung cancer (SCLC) is poor, most of them are in the extensive stage at the time of diagnosis, and are prone to brain metastasis. In this study, we established a nomogram combined with some clinical parameters to predict the survival of SCLC patients with brain metastasis.

METHODS

The 3522 eligible patients selected from the SEER database between 2010 and 2015 were randomly divided into training cohort and validation cohort. Univariate and multivariate Cox regression analysis were used to evaluate the ability of each parameter to predict OS. The regression coefficients obtained in multivariate analysis were visualized in the form of nomogram, thus a new nomogram and risk classification system were established. The calibration curves were used to verify the model. And ROC curves were used to evaluate the discrimination ability of the newly constructed nomogram. Survival curves were made by Kaplan-Meier method and compared by Log rank test.

RESULTS

Univariate and multivariate analysis showed that age, race, sex, T stage, N stage and marital status were independent prognostic factors and were included in the predictive model. The calibration curves showed that the predicted value of the 1- and 3-year survival rate by the nomogram was in good agreement with the actual observed value of the 1- and 3-year survival rate. And, the ROC curves implied the good discrimination ability of the predictive model. In addition, the results showed that in the total cohort, training cohort, and validation cohort, the prognosis of the low-risk group was better than that of the high-risk group.

CONCLUSIONS

We established a nomogram and a corresponding risk classification system to predict OS in SCLC patients with brain metastasis. This model could help clinicians make clinical decisions and stratify treatment for patients.

Identification prognosis-associated immune genes in colon adenocarcinoma

Colon adenocarcinoma (COAD) is one of the most prevalent malignant tumors worldwide. Immune genes (IGs) have a considerable correlation with tumor initiation and prognosis. The present paper aims to identify the prognosis value of IGs in COAD and conduct a prognosis model for clinical utility. Gene expression data of COAD were downloaded from The Cancer Genome Atlas (TCGA), screening and analyzing differentially expressed IGs by bioinformatics. Core genes were screened by univariate and multivariate Cox regression analyses. Survival analysis was appraised by the Kaplan–Meier method and the log-rank test. Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis (GSEA) were used to identify IGs’ relevant signal pathways. We predicted the overall survival (OS) by nomogram. Finally, a prognosis model was conducted based on 12 IGs (SLC10A2, CXCL3, NOX4, FABP4, ADIPOQ, IGKV1-33, IGLV6-57, INHBA, UCN, VIP, NGFR, and TRDC). The risk score was an independent prognostic factor, and a nomogram could accurately predict the OS of individual COAD patients. These results were validated in GSE39582, GSE12945, and GSE103479 cohorts. Functional enrichment analysis demonstrated that these IGs are mainly enriched in hormone secretion, hormone transport, lipid transport, cytokine–cytokine receptor interaction, and peroxisome proliferators-activated receptor signaling pathway. In summary, the risk score is an independent prognostic biomarker. We also excavated several IGs related to COAD’s survival and maybe potential biomarkers for COAD diagnosis and treatment.

Construction and validation of an RNA-binding protein-associated prognostic model for colorectal cancer

Colorectal cancer (CRC) is one of the most prevalent and fatal malignancies, and novel biomarkers for the diagnosis and prognosis of CRC must be identified. RNA-binding proteins (RBPs) are essential modulators of transcription and translation. They are frequently dysregulated in various cancers and are related to tumorigenesis and development. The mechanisms by which RBPs regulate CRC progression are poorly understood and no clinical prognostic model using RBPs has been reported in CRC. We sought to identify the hub prognosis-related RBPs and to construct a prognostic model for clinical use. mRNA sequencing and clinical data for CRC were obtained from The Cancer Genome Atlas database (TCGA). Gene expression profiles were analyzed to identify differentially expressed RBPs using R and Perl software. Hub RBPs were filtered out using univariate Cox and multivariate Cox regression analysis. We used functional enrichment analysis, including Gene Ontology and Gene Set Enrichment Analysis, to perform the function and mechanisms of the identified RBPs. The nomogram predicted overall survival (OS). Calibration curves were used to evaluate the consistency between the predicted and actual survival rate, the consistency index (c-index) was calculated, and the prognostic effect of the model was evaluated. Finally, we identified 178 differently expressed RBPs, including 121 up-regulated and 57 down-regulated proteins. Our prognostic model was based on nine RBPs (PNLDC1, RRS1, HEXIM1, PPARGC1A, PPARGC1B, BRCA1, CELF4, AEN and NOVA1). Survival analysis showed that patients in the high-risk subgroup had a worse OS than those in the low-risk subgroup. The area under the curve value of the receiver operating characteristic curve of the prognostic model is 0.712 in the TCGA cohort and 0.638 in the GEO cohort. These results show that the model has a moderate diagnostic ability. The c-index of the nomogram is 0.77 in the TCGA cohort and 0.73 in the GEO cohort. We showed that the risk score is an independent prognostic biomarker and that some RBPs may be potential biomarkers for the diagnosis and prognosis of CRC.

High expression of cell adhesion molecule 2 unfavorably impacts survival in non-small cell lung cancer patients with brain metastases

Background

Lung cancer is one kind of malignant tumor with a high risk for morbidity and mortality compared to other solid organ malignancies. Brain metastases occur in 30-55% of non-small cell lung cancer (NSCLC) patients. Prognosis of NSCLC patients with brain metastases is very poor. Our previous study showed that cell adhesion molecule 2 (CADM2) could regulate the development of brain metastasis in NSCLC cells. Therefore, the objective of the study is to evaluate the effect of CADM2 on the prognosis of NSCLC patients with brain metastases.

Methods

The expression of CADM2 was detected by quantitative real-time polymerase chain reaction (qRT-PCR) in the tissue of the primary tumor. Patients were followed up and overall survival (OS) was calculated. The relationships between CADM2 and clinicopathological features were analyzed using the chi-square test. Kaplan-Meier analysis was carried out to demonstrate the influence of CADM2 on the OS of patients. Univariate and multivariate Cox analyses were used to determine the prognosis of NSCLC patients with brain metastases.

Results

A total of 139 NSCLC patients with brain metastases from the Affiliated Cancer Hospital & Institute of Guangzhou Medical University, treated between January 2015 and December 2017 were evaluated retrospectively. The expression level of CADM2 in patients ranged from 1 to 17.2677, with a median of 6.0772. Chi-square analysis showed that CADM2 gene expression level was not significantly associated with gender, age, tumor location, histological subtype, tumor T stage, extracranial metastasis, or smoking status. However, CADM2 expression was notably associated with risk for lymph node metastasis. The results of the Kaplan-Meier analysis showed that high expression [CADM2 messenger RNA (mRNA) ≥6.0772] of CADM2 was markedly associated with poor prognosis. Univariate and multivariate Cox analyses demonstrated that CADM2 was an independent risk factor for survival in NSCLC patients with brain metastases (P<0.05).

Conclusions

CADM2 expression is up-regulated and closely associated with disease progression and poor prognosis in NSCLC patients with brain metastases. CADM2 expression warrants special consideration given its potential prognostic significance that might help inform clinical decision making.

The effect of EML4-ALK break-apart ratio on crizotinib outcomes in non-small cell lung cancer harboring EML4-ALK rearrangement

PURPOSE

Anaplastic lymphoma kinase (ALK) gene rearrangement exists in approximately 3-7% of non-small cell lung cancer (NSCLC) and more than 15% split or isolated red signals among 50 tumor nuclei scored in the FISH analysis defines as ALK-positive. The previous studies showed that the high EGFR mutational load related to better outcomes in EGFR mutant NSCLC. Therefore, we aimed to investigate the effect of the ALK break-apart ratio on treatment outcome in metastatic ALK-positive NSCLC.

METHODS

The patients (pts) who ALK-positive and treated with crizotinib were retrospectively enrolled. The 30%, 40%, 50%, 60%, and 70% break-apart ratios were determined as a threshold value, and each of these was evaluated separately. Based on the results of these analyses, we detected the optimal threshold value and also performed further investigations.

RESULTS

A total of 70 patients were enrolled in the study. The most significant decrease in the risk of the progression or death was detected at the 50% threshold value and it was accepted as the optimal threshold. The median PFS was 17.9 vs. 7.06 months (mo) in the pts with high ALK rearrangement than low (HR: 0.43, 95% CI 0.24-0.76, p 0.004). The median OS was also significant longer in high ALK rearrange group (44.6 mo vs. 16.8 mo; HR: 0.37, 95% Cl 0.1883-0.7315; p 0.004). The intracranial progression during crizotinib treatment was significantly frequent in the pts with high ALK rearrangement (62.5-32.5%, P 0.039) DISCUSSION: In this study, we found that the high break-apart ratio can predict the extent of benefit from targeted therapy in ALK-positive NSCLC patients. Based on the results of this study, the percentage of the ALK rearrangement can be used to predict treatment outcome and to choose the optimal targeted agent in the treatment of metastatic ALK-positive NSCLC.

CT-based radiomics for predicting brain metastases as the first failure in patients with curatively resected locally advanced non-small cell lung cancer

PURPOSE

Brain metastasis (BM) is the primary first failure pattern in patients with curatively resected locally advanced non-small cell lung cancer (LA-NSCLC). It is not yet possible to accurately predict the occurrence of BM. The purpose of the research is to develop and validate a prediction model of BM-free survival based on radiomics characterising the primary lesions combined with clinical characteristics in patients with curatively resected LA-NSCLC.

METHODS

This study consisted of 124 patients with curatively resected stage IIB-IIIB NSCLC in our institution between January 2014 and June 2018. Patients were randomly divided into training and validation cohorts using a 4:1 ratio. Radiomics features were selected from the chest CT images before surgery. A radiomics signature was constructed using the LASSO algorithm based on the training cohort. Clinical model was developed using the Cox proportional hazards model. The clinical, radiomics, and integrated nomograms were constructed. The prediction performance of the models was assessed based on its discrimination, calibration, and clinical utility.

RESULTS

The radiomics signature is significantly associated with BM-free survival in the overall cohort. The discrimination performance of the integrated nomogram, with the C-indexes 0.889 (0.872-0.906, 95 % CI) and 0.853 (0.788-0.918, 95 % CI) in the training and validation cohorts, respectively, is significantly better than the clinical nomogram (p < 0.0001 for the training cohort, p = 0.0008 for the validation cohort). Compared with the radiomics nomogram, the integrated nomogram is also improved to varying degrees, but not apparent in the validation cohort (p = 0.0007 for the training cohort, p = 0.0554 for the validation cohort). The calibration curve and decision curve analysis demonstrated that the integrated nomogram exceeded the clinical or radiomics nomograms in predicting BM-free survival.

CONCLUSIONS

Compared with the clinical or radiomics nomograms, the predictive performance of the integrated nomogram is significantly improved. The integrated nomogram is most suitable for predicting BM-free survival in patients with curatively resected LA-NSCLC.

Identification of prognosis-associated immune genes and exploration of immune cell infiltration in colorectal cancer

Aim: To identify prognosis-related immune genes (PRIGs) and construct a prognosis model of colorectal cancer (CRC) patients for clinical use. Materials & methods: Expression profiles were obtained from The Cancer Genome Atlas database and identified differentially expressed PRIGs of CRC. Results: A prognostic model was conducted based on nine PRIGs. The risk score, based on prognosis model, was an independent prognostic predictor. Five PRIGs and risk score were significantly associated with the clinical stage of CRC and five immune cells related to the risk score. Conclusion: The risk score was an independent prognostic biomarker for CRC patients. The research excavated immune genes that were associated with survival and that could be potential biomarkers for prognosis and treatment for CRC patients.

A nomogram model to predict death rate among non-small cell lung cancer (NSCLC) patients with surgery in surveillance, epidemiology, and end results (SEER) database

Background

This study aimed to establish a novel nomogram prognostic model to predict death probability for non-small cell lung cancer (NSCLC) patients who received surgery..

Methods

We collected data from the Surveillance, Epidemiology, and End Results (SEER) database of the National Cancer Institute in the United States. A nomogram prognostic model was constructed to predict mortality of NSCLC patients who received surgery.

Results

A total of 44,880 NSCLC patients who received surgery from 2004 to 2014 were included in this study. Gender, ethnicity, tumor anatomic sites, histologic subtype, tumor differentiation, clinical stage, tumor size, tumor extent, lymph node stage, examined lymph node, positive lymph node, type of surgery showed significant associations with lung cancer related death rate (P < 0.001). Patients who received chemotherapy and radiotherapy had significant higher lung cancer related death rate but were associated with significant lower non-cancer related mortality (P<0.001). A nomogram model was established based on multivariate models of training data set. In the validation cohort, the unadjusted C-index was 0.73 (95% CI, 0.72–0.74), 0.71 (95% CI, 0.66–0.75) and 0.69 (95% CI, 0.68–0.70) for lung cancer related death, other cancer related death and non-cancer related death.

Conclusions

A prognostic nomogram model was constructed to give information about the risk of death for NSCLC patients who received surgery.

Development and validation of a five-gene model to predict postoperative brain metastasis in operable lung adenocarcinoma

One of the most common sites of extra-thoracic distant metastasis of nonsmall-cell lung cancer is the brain. Our study was performed to discover genes associated with postoperative brain metastasis in operable lung adenocarcinoma (LUAD). RNA seq was performed in specimens of primary LUAD from seven patients with brain metastases and 45 patients without recurrence. Immunohistochemical (IHC) assays of the differentially expressed genes were conducted in 272 surgical-resected LUAD specimens. LASSO Cox regression was used to filter genes related to brain metastasis and construct brain metastasis score (BMS). GSE31210 and GSE50081 were used as validation datasets of the model. Gene Set Enrichment Analysis was performed in patients stratified by risk of brain metastasis in the TCGA database. Through the initial screening, eight genes (CDK1, KPNA2, KIF11, ASPM, CEP55, HJURP, TYMS and TTK) were selected for IHC analyses. The BMS based on protein expression levels of five genes (TYMS, CDK1, HJURP, CEP55 and KIF11) was highly predictive of brain metastasis in our cohort (12-month AUC: 0.791, 36-month AUC: 0.766, 60-month AUC: 0.812). The validation of BMS on overall survival of GSE31210 and GSE50081 also showed excellent predictive value (GSE31210, 12-month AUC: 0.682, 36-month AUC: 0.713, 60-month AUC: 0.762; GSE50081, 12-month AUC: 0.706, 36-month AUC: 0.700, 60-month AUC: 0.724). Further analyses showed high BMS was associated with pathways of cell cycle and DNA repair. A five-gene predictive model exhibits potential clinical utility for the prediction of postoperative brain metastasis and the individual management of patients with LUAD after radical resection.

A nomogram to predict outcomes of lung cancer patients after pneumonectomy based on 47 indicators

Aims

We aimed to establish a nomogram for lung cancer using patients' characteristics and potential hematological biomarkers.

Methods

Principle component analysis was used to reduce the dimensions of the data, and each component was transformed into categorical variables based on cutoff values obtained using the X‐tile software. Multivariate analysis was used to determine potential prognostic biomarkers. Five components were used in the predictive nomogram. Internal validation of the model was performed by bootstrapping of samples, while external validation was performed on a separate cohort from Shandong Cancer Hospital. The predictive accuracy of the model was measured by concordance index and risk group stratification. Decision curve analysis was performed to evaluate the net benefit of the models.

Results

One hundred patients in the Discovery group and 111 patients in the Validation group were retrospectively analyzed in this study. Forty‐seven indexes were sorted into eight subgroups. Five components based on cox regression analysis were enrolled into the predictive nomogram. The nomogram prediction of the probability of 3‐ and 5‐year overall survival was in great concordance with the actual observations. Of interest, the nomogram allowed better risk stratification of patients and better accuracy in predicting patients' survival compared with pathological tumor‐node‐metastasis staging system.

Conclusion

A nomogram was established for prognosis of lung cancer, which can be used for treatment selection and clinical care management.

Prognostic implications of metabolism-associated gene signatures in colorectal cancer

Colorectal cancer (CRC) is one of the most common and deadly malignancies. Novel biomarkers for the diagnosis and prognosis of this disease must be identified. Besides, metabolism plays an essential role in the occurrence and development of CRC. This article aims to identify some critical prognosis-related metabolic genes (PRMGs) and construct a prognosis model of CRC patients for clinical use. We obtained the expression profiles of CRC from The Cancer Genome Atlas database (TCGA), then identified differentially expressed PRMGs by R and Perl software. Hub genes were filtered out by univariate Cox analysis and least absolute shrinkage and selection operator Cox analysis. We used functional enrichment analysis methods, such as Gene Ontology, Kyoto Encyclopedia of Genes and Genomes, and Gene Set Enrichment Analysis, to identify involved signaling pathways of PRMGs. The nomogram predicted overall survival (OS). Calibration traces were used to evaluate the consistency between the actual and the predicted survival rate. Finally, a prognostic model was constructed based on six metabolic genes (NAT2, XDH, GPX3, AKR1C4, SPHK1, and ADCY5), and the risk score was an independent prognostic prognosticator. Genetic expression and risk score were significantly correlated with clinicopathologic characteristics of CRC. A nomogram based on the clinicopathological feature of CRC and risk score accurately predicted the OS of individual CRC cancer patients. We also validated the results in the independent colorectal cancer cohorts GSE39582 and GSE87211. Our study demonstrates that the risk score is an independent prognostic biomarker and is closely correlated with the malignant clinicopathological characteristics of CRC patients. We also determined some metabolic genes associated with the survival and clinical stage of CRC as potential biomarkers for CRC diagnosis and treatment.

Prognostic implications of autophagy-associated gene signatures in non-small cell lung cancer

Autophagy, a highly conserved cellular proteolysis process, has been involved in non-small cell lung cancer (NSCLC). We tried to develop a prognostic prediction model for NSCLC patients based on the expression profiles of autophagy-associated genes. Univariate Cox regression analysis was used to determine autophagy-associated genes significantly correlated with overall survival (OS) of the TCGA lung cancer cohort. LASSO regression was performed to build multiple-gene prognostic signatures. We found that the 22-gene and 11-gene signatures could dichotomize patients with significantly different OS and independently predict the OS in TCGA lung adenocarcinoma (HR=2.801, 95% CI=2.252-3.486, P<0.001) and squamous cell carcinoma (HR=1.105, 95% CI=1.067-1.145, P<0.001), respectively. The prognostic performance of the 22-gene signature was validated in four GEO lung cancer cohorts. Moreover, GO, KEGG, and GSEA analyses unveiled several fundamental signaling pathways and cellular processes associated with the 22-gene signature in lung adenocarcinoma. We also constructed a clinical nomogram with a concordance index of 0.71 to predict the survival possibility of NSCLC patients by integrating clinical characteristics and the autophagy gene signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. Overall, we constructed and verified a novel autophagy-associated gene signature that could improve the individualized outcome prediction in NSCLC.

A nomogram to predict overall survival of patients with early stage non-small cell lung cancer

Background

Nomograms have been widely used for estimating cancer prognosis. The aim of this study was to construct a clinical nomogram that would well predict overall survival of early stage non-small cell lung cancer (NSCLC) patients after surgery resection.

Methods

A total of 443 patients diagnosed with pathologic stage I and II NSCLC who had undergone curative resection without neoadjuvant chemotherapy or radiotherapy were recruited and analyzed. The log-rank test and multivariate Cox regression analysis were used to select the most significant predictors in the final nomogram for predicting overall survival. Furthermore, the model was validated by bootstrap methods and measured by concordance index (C-index) and calibration plots.

Results

Four independent predictors for overall survival were identified and included into the delineation of the nomogram (tumor differentiation, station of sampled lymph nodes, pathologic T and pathologic N). The model showed comparatively stable discrimination (bootstrap-corrected C-index =0.622, 95% CI: 0.572-0.672) and good calibration.

Conclusions

We successfully developed a nomogram incorporating available clinicopathological variables to predict overall survival of early stage NSCLC patients after surgery resection, which might help clinician select better appropriate treatment decisions.

Survival nomogram for patients with initially diagnosed metastatic non-small-cell lung cancer: a SEER-based study

Aim: Prognosis of patients with metastatic non-small-cell lung cancer differ widely. Methods: All patients were randomly divided into training or validation cohort. Cox-regression analyses were conducted to select independent predictors. We built a nomogram by R code and evaluated the accuracy and the reliability of the model using C-index, calibration curves and decision curve analyses. We made a risk classification system based on the nomogram. Results: In the validation cohort, C-index was 0.729 and 0.738 for 1- and 2-year overall survival. Calibration plots and decision curve analyses presented great prognostic accuracy and clinical applicability. Its prognostic accuracy preceded the American Joint Committee on Cancer staging with evaluated integrated discrimination improvement. Conclusion: The model can be a practical tool in treatment decision and individual counseling.

Brain metastases in resected non-small cell lung cancer: The impact of different tyrosine kinase inhibitors

Objectives

The purpose of this study was to examine the impact of epidermal growth factor receptor (EGFR) mutation status and tyrosine kinase inhibitors (TKIs) on the survival of brain metastases (BM) in patients with surgically resected non-small cell lung cancer (NSCLC).

Methods

We selected the patients who had developed metastatic NSCLC; analyzed the differences between brain metastases and other sites of metastases, including patient characteristics, EGFR status, and survival; and selected the patients who had BM for further investigation. We also compared the treatment effects of first-generation TKIs with those of second-/third-generation TKIs.

Results

A total of 785 cases of stage I-IIIa NSCLC were reviewed. Thirty-six (4.6%) patients were identified as having BM. Among them, 14 patients had a mutated EGFR status. No association between EGFR mutation and the incidence of BM was observed (p = 0.199). Patients with mutated EGFRs had significantly longer overall survival and post-recurrence survival than patients with wild-type EGFR mutation (p = 0.001 for both). However, there was no survival difference between patients with exon 19 and exon 21 mutations (p = 0.426). Furthermore, patients who received the second- and/or third-generation EGFR-TKIs had better survival than patients who only received first-generation EGFR-TKIs (p = 0.031). A multivariate analysis indicated that the next-generation TKIs (HR, 0.007; 95% CI, 0.000 to 0.556; p = 0.026) and a longer interval before BM development (HR, 0.848; 95% CI, 0.733 to 0.980; p = 0.025) were significant factors in longer survival.

Conclusions

EGFR-TKIs were effective in treating NSCLC patients with BM after curative pulmonary surgery, especially in those patients harboring EGFR mutations. Furthermore, the second-/third-generation EGFR-TKIs showed more promising results than the first-generation EGFR-TKIs in treating those particular patients, though larger studies needed to further prove the results.

Natural history, clinical course and predictors of interval time from initial diagnosis to development of subsequent NSCLC brain metastases

PURPOSE

Non-small cell lung cancer (NSCLC) brain metastases are associated with substantial morbidity and mortality. During recent years, accompanying dramatic improvements in systemic disease control, NSCLC brain metastases have emerged as an increasingly relevant clinical problem. However, optimal surveillance practices remain poorly defined. This purpose of this study was to further characterize the natural history, clinical course and risk factors associated with earlier development of subsequent NSCLC brain metastases to better inform clinical practice and help guide survivorship care.

METHODS

We retrospectively reviewed all institutional NSCLC brain metastasis cases treated with radiotherapy between 1997 and 2015. Exclusion criteria included presence of brain metastases at initial NSCLC diagnosis and incomplete staging information. Interval time to brain metastases and subsequent survival were characterized using Kaplan-Meier and multivariate Cox regression analyses.

RESULTS

Among 105 patients within this cohort, median interval time to development of brain metastases was 16 months. Median interval times were 29, 19, 16 and 13 months for Stage I-IV patients, respectively (P = 0.016). Additional independent predictors for earlier development of NSCLC brain metastases included non-adenocarcinomatous histopathology (HR 3.036, P < 0.001), no prior surgical resection (HR 1.609, P = 0.036) and no prior systemic therapy (HR 3.560, P = 0.004). Median survival following intracranial progression was 16 months. Delayed development of brain metastases was associated with better prognosis (HR 0.970, P < 0.001) but not survival following intracranial disease onset.

CONCLUSIONS

Collectively, our results provide valuable insights into the natural history of NSCLC brain metastases. NSCLC stage, histology, prior surgical resection and prior systemic therapy emerged as independent predictors for interval time to brain metastases.

Clear cell adenocarcinoma of the lung: a population-based study

BACKGROUND

Clear cell adenocarcinoma of the lung (CCAL) is a rare diagnosis with poorly understood clinicopathological characteristics and disease progression.

METHODS

A population cohort study was conducted using prospectively extracted data from the Surveillance, Epidemiology and End Results database for patients with histological diagnoses of CCAL. Propensity-matched analysis was performed for survival analysis.

RESULTS

A total of 1,203 patients with CCAL were included. The median overall survival (OS) for all patients was 19.0 months (95% CI 16.0-22.0 months). Data for 1-, 3-, and 5-year OS were 58.7, 37.3, and 27.7%, respectively. Log-rank analysis showed that the prognoses of CCAL patients were better than those with non-CCAL adenocarcinoma after propensity-matched analysis (P<0.001). Cancer-directed surgery significantly improved median OS by almost 40 months (45.0 vs 5.0 months; P<0.01). Radiotherapy after surgery prolonged survival compared with patients who only received surgery (37.0 vs 17.0 months; P<0.01). Multivariate Cox analysis showed that older age (>65 years), larger lesions, and lymph node and distant metastases were independent prognostic factors for worse survival, while cancer-directed surgery was an independent protective factor. Five independent prognostic factors were identified and entered into the nomogram. The concordance index of the nomogram for predicting survival was 0.72 (95% CI 0.69-0.74). The calibration curves for the probability of 3-, 5-, and 10-year OS showed optimal agreement between nomogram prediction and actual observation.

CONCLUSION

CCAL is a rare pathology, and older age, larger lesions, metastases, and cancer-directed surgery were associated with prognosis. A prognostic nomogram was established to provide individual prediction of OS.

Risk factors for brain metastases in patients with non-small-cell lung cancer

Brain metastases (BM) are severe incidents in patients with non-small-cell lung cancer (NSCLC). The controversial value of prophylactic cranial irradiation (PCI) in NSCLC in terms of survival benefit prompted us to explore the possible risk factors for BM in NSCLC and identify the potential population most likely to benefit from PCI. Risk factors for brain metastases in NSCLC are reviewed in this article. Identifying patients with a higher risk of BM could possibly increase the benefit of PCI while reducing the discomfort and risks caused by unnecessary invasive procedures in the NSCLC patient population. Future studies might focus on finding a solid basis for the prediction of the occurrence of brain metastases and for the therapeutic decision on the use of PCI.

Prognostic Dynamic Nomogram Integrated with Inflammation-Based Factors for Non-Small Cell Lung Cancer Patients with Chronic Hepatitis B Viral Infection

Chronic inflammation plays an important role in tumor progression. The aim of this study was to develop an effective predictive dynamic nomogram integrated with inflammation-based factors to predict overall survival (OS) of non-small cell lung cancer (NSCLC) patients with chronic hepatitis B viral (HBV) infection. We retrospectively analyzed NSCLC patients with HBV infection from Sun Yat-sen University Cancer Center between 2008 and 2010. Univariate and multivariate Cox survival analyses were performed to identify prognostic factors associated with OS of patients. All of the independent prognostic factors were utilized to build the dynamic nomogram. The predictive accuracy of the dynamic nomogram was evaluated concordance index (C-index), decision curve analysis and were compared with previous reported model and traditional TNM staging system. According to the total points (TPS) by dynamic nomogram, we further stratified patients into different risk groups. A total of 203 patients were included. Multivariate Cox analysis showed TNM stage (P = 0.019), treatment (P < 0.001), C-reactive protein (P = 0.020) and platelet (P = 0.012) were independent prognostic factors of OS. The dynamic nomogram was established by involving all the factors above. The C-index of dynamic nomogram for predicting OS was 0.76 (95%CI: 0.72-0.80), which was statistically higher than that of traditional TNM staging system (0.70, 95%CI: 0.66-0.74, P<0.001). Decision curve analysis demonstrated that the dynamic nomogram was better than the TNM staging system. The predictive accuracy of the current model keeping almost the same accuracy as previous one. Based on the total points (TPS) of dynamic nomogram, we divided the patients into 3 subgroups: low risk (TPS ≤ 107), intermediate risk (107< TPS ≤ 149), and high risk (TPS > 149). The differences of OS rates were significant in the subgroups. We propose a novel dynamic nomogram model based on inflammatory prognostic factors that is highly predictive of OS in NSCLC patients with HBV infection and outperforms the traditional TNM staging system.

Brain metastases in ALK-positive NSCLC - time to adjust current treatment algorithms

The progress in molecular biology has revolutionized systemic treatment of advanced non-small-cell lung cancer (NSCLC) from conventional chemotherapy to a treatment stratified by histology and genetic aberrations. Tumors harboring a translocation of the anaplastic-lymphoma-kinase (ALK) gene constitute a distinct genetic and clinico-pathologic NSCLC subtype with patients with ALK-positive disease being at a higher risk for developing brain metastases. Due to the introduction of effective targeted therapy with ALK-inhibitors, today, patients with advanced ALK-positive NSCLC achieve high overall response rates and remain progression-free for long time intervals. Moreover, ALK-inhibitors seem to exhibit efficacy in the treatment of brain metastases. In the light of this, it needs to be discussed how treatment algorithms for managing patients with brain metastases should be modified. By integrating systemic ALK-inhibitor therapy, radiotherapy, in particular whole brain radiotherapy might be postponed deferring potential long-term impairment by neurocognitive deficits to a later time point in the course of the disease. An early treatment of asymptomatic brain metastases might offer patients a longer time without impairment of cerebral symptoms or radiotherapeutic interventions. Based on an updated extensive review of the literature this article provides an overview on the epidemiology and the treatment of patients’ brain metastases. It describes the specifics of ALK-positive disease and proposes an algorithm for the treatment of patients with advanced ALK-positive NSCLC and brain metastases.

Targeted DNA sequencing of non-small cell lung cancer identifies mutations associated with brain metastases

Introduction

This study explores the hypothesis that dominant molecular oncogenes in non-small cell lung cancer (NSCLC) are associated with metastatic spread to the brain.

Methods

NSCLC patient groups with no evidence of metastasis, with metastatic disease to a non-CNS site, who developed brain metastasis after diagnosis, and patients with simultaneous diagnosis of NSCLC and metastatic brain lesions were studied using targeted sequencing.

Results

In patients with brain metastasis versus those without, only 2 variants (one each in BCL6 and NOTHC2) were identified that occurred in ≥ 4 NSCLC of patients with brain metastases but ≤ 1 of the NSCLC samples without brain metastases. At the gene level, 20 genes were found to have unique variants in more than 33% of the patients with brain metastases. When analyzed at the patient level, these 20 genes formed the basis of a predictive test to discriminate those with brain metastasis. Further analysis showed that PI3K/AKT signaling is altered in both the primary and metastases of NSCLC patients with brain lesions.

Conclusion

While no single variant was associated with brain metastasis, this study describes a potential gene panel for the identification of patients at risk and implicates PI3K/AKT signaling as a therapeutic target.

Development and validation of two prognostic nomograms for predicting survival in patients with non-small cell and small cell lung cancer

Purpose

This study aimed to construct two prognostic nomograms to predict survival in patients with non-small-cell lung cancer (NSCLC) and small-cell lung cancer (SCLC) using a novel set of clinical parameters.

Patients and Methods

Two nomograms were developed, using a retrospective analysis of 5384 NSCLC and 647 SCLC patients seen during a 10-year period at Xiang Ya Affiliated Cancer Hospital (Changsha, China). The patients were randomly divided into training and validation cohorts. Univariate and multivariate analyses were used to identify the prognostic factors needed to establish nomograms for the training cohort. The model was internally validated via bootstrap resampling and externally certified using the validation cohort. Predictive accuracy and discriminatory capability were estimated using concordance index (C-index), calibration curves, and risk group stratification.

Results

The largest contributor to overall survival (OS) prognosis in the NSCLC nomogram was the therapeutic regimen and diagnostic method parameters, and in the SCLC nomogram was the therapeutic regimen and health insurance plan parameters. Calibration curves for the nomogram prediction and the actual observation were in optimal agreement for the 3-year OS and acceptable agreement for the 5-year OS in both training datasets. The C-index was higher for the NSCLC cohort nomogram than for the TNM staging system (0.67 vs. 0.64, P = 0.01) and higher for the SCLC nomogram than for the clinical staging system (limited vs. extensive) (0.60 vs. 0.53, P = 0.12).

Conclusion

Treatment regimen parameter made the largest contribution to OS prognosis in both nomograms, and these nomograms might provide clinicians and patients a simple tool that improves their ability to accurately estimate survival based on individual patient parameters rather than using an averaged predefined treatment regimen.

Combining Carcinoembryonic Antigen and Platelet to Lymphocyte Ratio to Predict Brain Metastasis of Resected Lung Adenocarcinoma Patients

We aimed to evaluate the role of pretreatment carcinoembryonic antigen (CEA) and platelet to lymphocyte ratio (PLR) in predicting brain metastasis after radical surgery for lung adenocarcinoma patients. The records of 103 patients with completely resected lung adenocarcinoma between 2013 and 2014 were reviewed. Clinicopathologic characteristics of these patients were assessed in the Cox proportional hazards regression model. Brain metastasis occurred in 12 patients (11.6%). On univariate analysis, N2 stage (P = 0.013), stage III (P = 0.016), increased CEA level (P = 0.006), and higher PLR value (P = 0.020) before treatment were associated with an increased risk of developing brain metastasis. In multivariate model analysis, CEA above 5.2 ng/mL (P = 0.014) and PLR ≥ 120 (P = 0.036) remained as the risk factors for brain metastasis. The combination of CEA and PLR was superior to CEA or PLR alone in predicting brain metastasis according to the receiver operating characteristic (ROC) curve analysis (area under ROC curve, AUC 0.872 versus 0.784 versus 0.704). Pretreatment CEA and PLR are independent and significant risk factors for occurrence of brain metastasis in resected lung adenocarcinoma patients. Combining these two factors may improve the predictability of brain metastasis.

A Nomogram based on Inflammatory Factors C-Reactive Protein and Fibrinogen to Predict the Prognostic Value in Patients with Resected Non-Small Cell Lung Cancer

Purpose: This study aimed to develop an effective nomogram for predicting survival in surgically treated non-small cell lung cancer patients.

Methods: We retrospectively evaluated 856 NSCLC in this study. Cox regression analyses were performed to identify significant prognostic factors for developing a nomogram to predict overall survival (OS). The discriminative ability was assessed with the concordance index (C-index).

Results: On multivariate analysis of the 856 cohort, independent factors for survival were CRP, fibrinogen, tumor status, nodal status, distant metastasis and clinical stage, which were entered into the nomogram. The C-index of the established nomogram 0.720 (95% CI: 0.671-0.769) was higher than that of the seventh edition TNM staging system 0.689 (95% CI: 0.668-0.709) for predicting OS (P < 0.05). Compared with patients with low CRP levels (< 8.6 g/L) and low fibrinogen levels (< 3.7 g/L), patients with high CRP and fibrinogen levels had shorter OS. Subgroup analyses revealed that the nomogram was a favorable prognostic parameter in stage I-IV NSCLC (P < 0.05).

Conclusion: A nomogram integrating CRP and fibrinogen, which could be convenient and feasible to obtain from the serum preoperatively, may assist in risk stratification for individual patient with resected NSCLC.

Statistical modelling for thoracic surgery using a nomogram based on logistic regression

A well-developed clinical nomogram is a popular decision-tool, which can be used to predict the outcome of an individual, bringing benefits to both clinicians and patients. With just a few steps on a user-friendly interface, the approximate clinical outcome of patients can easily be estimated based on their clinical and laboratory characteristics. Therefore, nomograms have recently been developed to predict the different outcomes or even the survival rate at a specific time point for patients with different diseases. However, on the establishment and application of nomograms, there is still a lot of confusion that may mislead researchers. The objective of this paper is to provide a brief introduction on the history, definition, and application of nomograms and then to illustrate simple procedures to develop a nomogram with an example based on a multivariate logistic regression model in thoracic surgery. In addition, validation strategies and common pitfalls have been highlighted.

A Nomogram to Predict Recurrence and Survival of High-Risk Patients Undergoing Sublobar Resection for Lung Cancer: An Analysis of a Multicenter Prospective Study (ACOSOG Z4032)

BACKGROUND

Individualized prediction of outcomes may help with therapy decisions for patients with non-small cell lung cancer. We developed a nomogram by analyzing 17 clinical factors and outcomes from a randomized study of sublobar resection for non-small cell lung cancer in high-risk operable patients. The study compared sublobar resection alone with sublobar resection with brachytherapy. There were no differences in primary and secondary outcomes between the study arms, and they were therefore combined for this analysis.

METHODS

The clinical factors of interest (considered as continuous variables) were assessed in a univariate Cox proportional hazards model for significance at the 0.10 level for their impact on overall survival (OS), local recurrence-free survival (LRFS), and any recurrence-free survival (RFS). The final multivariable model was developed using a stepwise model selection.

RESULTS

Of 212 patients, 173 had complete data on all 17 risk factors. Median follow-up was 4.94 years (range, 0.04 to 6.22). The 5-year OS, LRFS, and RFS were 58.4%, 53.2%, and 47.4%, respectively. Age, baseline percent diffusing capacity of lung for carbon monoxide, and maximum tumor diameter were significant predictors for OS, LRFS, and RFS in the multivariable model. Nomograms were subsequently developed for predicting 5-year OS, LRFS, and RFS.

CONCLUSIONS

Age, baseline percent diffusing capacity of lung for carbon monoxide, and maximum tumor diameter significantly predicted outcomes after sublobar resection. Such nomograms may be helpful for treatment planning in early stage non-small cell lung cancer and to guide future studies.