Risk-stratified Approach for Never- and Ever-Smokers in Lung Cancer Screening: A Prospective Cohort Study in China.

Rationale: Over 40% of lung cancer cases occurred in never-smokers in China. However, high-risk never-smokers were precluded from benefiting from lung cancer screening as most screening guidelines did not consider them. Objectives: We sought to develop and validate prediction models for 3-year lung cancer risks for never- and ever-smokers, named the China National Cancer Center Lung Cancer models (China NCC-LCm2021 models). Methods: 425,626 never-smokers and 128,952 ever-smokers from the National Lung Cancer Screening program were used as the training cohort and analyzed using multivariable Cox models. Models were validated in two independent prospective cohorts: one included 369,650 never-smokers and 107,678 ever-smokers (841 and 421 lung cancers), and the other included 286,327 never-smokers and 78,469 ever-smokers (503 and 127 lung cancers). Measurements and Main Results: The areas under the receiver operating characteristic curves in the two validation cohorts were 0.698 and 0.673 for never-smokers and 0.728 and 0.752 for ever-smokers. Our models had higher areas under the receiver operating characteristic curves than other existing models and were well calibrated in the validation cohort. The China NCC-LCm2021 ⩾0.47% threshold was suggested for never-smokers and ⩾0.51% for ever-smokers. Moreover, we provided a range of threshold options with corresponding expected screening outcomes, screening targets, and screening efficiency. Conclusion: The construction of the China NCC-LCm2021 models can accurately reflect individual risk of lung cancer, regardless of smoking status. Our models can significantly increase the feasibility of conducting centralized lung cancer screening programs because we provide justified thresholds to define the high-risk population of lung cancer and threshold options to adapt different configurations of medical resources.

Modifiable risk-attributable and age-related burden of lung cancer in China, 1990-2019.

BACKGROUND: There were limited studies on the quantification of the modifiable and nonmodifiable lung cancer burden over time in China. Furthermore, the potential effect of risk factor reduction for lung cancer on gains in life expectancy (LE) remains unknown. METHODS: This study explored temporal trends in lung cancer deaths and disability-adjusted life years (DALY) attributable to modifiable risk factors from 1990 to 2019, based on the 2019 Global Burden of Disease Study. The abridged period life table method was used to quantify the effect of risk factors on LE. The authors used the decomposition approach to estimate contributions of aging metrics to change in the lung cancer burden. RESULTS: Nationally, the majority of lung cancer deaths and DALYs were attributable to behavioral and environmental risk clusters. Potential gains in life expectancy (PGLE) at birth would be 0.78 years for males and 0.35 years for females if the exposure to risk factors was mitigated to the theoretical minimum level. Tobacco use had the most robust impact on LE for both sexes (PGLE: 0.71 years for males and 0.19 years for females). From 1990 to 2019, risk-attributable age-standardized death and DALY rates of lung cancer showed an increasing trend in both sexes; adult population growth imposed 245.9 thousand deaths and 6.2 million DALYs for lung cancer. CONCLUSIONS: The modifiable risk-attributable lung cancer burden remains high in China. Effective tobacco control is the critical step toward addressing the lung cancer burden. Adult population growth was the foremost driver of transition in the age-related lung cancer burden. PLAIN LANGUAGE SUMMARY: We estimate the lung cancer burden attributable to modifiable and nonmodifiable contributors and the effect of risk factor reduction for lung cancer on the life expectancy in China. The findings suggest that the majority of lung cancer deaths and disability-adjusted life years were attributable to behavioral risk clusters, and the risk-attributable lung cancer burden increased nationally from 1990 to 2019. The average gains in life expectancy would be 0.78 years for males and 0.35 years for females if the exposure to risk factors for lung cancer was reduced to the theoretical minimum risk exposure level. Adult population growth was identified as the foremost driver of variation in the aging lung cancer burden.

Comprehensive analysis of the immunological implication and prognostic value of CXCR4 in non-small cell lung cancer.

CXCR4 (C-X-C chemokine receptor type 4) is the most commonly expressed of all chemokine receptors in malignant tumors. However, studies on CXCR4 in non-small cell lung cancer (NSCLC) tumor immune microenvironment, including those determining its immune efficacy and prognostic potential, are still scarce. Therefore, in this study, we determined the ability of CXCR4 to predict immunotherapy response and prognosis in NSCLC using immunohistochemical staining and RT-PCR, respectively, in two independent cohorts from the National Cancer Center of China. We analyzed transcriptome sequencing data and clinical information from multiple public databases to assess immune cell infiltration in NSCLC and constructed immune risk prognostic signatures based on CXCR4-related immunomodulators. We found that immune cell infiltration is significant differences in NSCLC tissues and is moderately correlated with CXCR4 expression. High CXCR4 expression was significantly associated with poor prognosis in NSCLC patients and a higher response rate to immunotherapy. The ROC curve showed that CXCR4 expression exhibited excellent performance in predicting the efficacy of immunotherapy in NSCLC. We identified 30 CXCR4-related immunomodulators in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) and constructed immune prognostic signatures based on CXCR4-related immunomodulators and CXCR4-related mutant genes. The signature-based prognostic risk score showed good performance in predicting patient prognosis in both LUAD and LUSC; high risk scores were significantly associated with poor prognosis (P < 0.0001) and was established as an independent prognostic factor by multivariate Cox regression. We postulate that CXCR4 is a potential predictive marker of immunotherapy efficacy in NSCLC and should be used in clinical settings. Moreover, the constructed signatures may be valuable in predicting patient prognosis in NSCLC.

Plasma extracellular vesicle long RNAs predict response to neoadjuvant immunotherapy and survival in patients with non-small cell lung cancer.

Neoadjuvant immunotherapy has brought new hope for patients with non-small cell lung cancer (NSCLC). However, limited by the lack of clinically feasible markers, it is still difficult to select NSCLC patients who respond well and to predict patients' clinical outcomes before the treatment. Before the treatment, we isolated plasma extracellular vesicles (EVs) from three cohorts (discovery, training and validation) of 78 NSCLC patients treated with neoadjuvant immunotherapy. To identify differentially-expressed EV long RNAs (exLRs), we employed RNA-seq in the discovery cohort. And we subsequently used qRT-PCR to establish and validate the predictive signature in the other two cohorts. We have identified 8 candidate exLRs from 27 top-ranked exLRs differentially expressed between responders and non-responders, and tested their expression with qRT-PCR in the training cohort. We finally identified H3C2 (P = 0.029), MALAT1 (P = 0.043) and RPS3 (P = 0.0086) significantly expressed in responders for establishing the predictive signature. Integrated with PD-L1 expression, our signature performed well in predicting immunotherapeutic responses in the training (AUC=0.892) and validation cohorts (AUC=0.747). Furthermore, our signature was proven to be a predictor for favorable prognosis of patients treated with neoadjuvant immunotherapy, which demonstrates the feasibility of our signature in clinical practices (P = 0.048). Our results demonstrate that the exLR-based signature could accurately predict responses to neoadjuvant immunotherapy and prognosis in NSCLC patients.

Single-cell RNA-sequencing data reveals the genetic source of extracellular vesicles in esophageal squamous cell carcinoma.

Esophageal squamous cell carcinoma (ESCC) is invasive cancer and the complex mechanisms underlying carcinogenesis remain unclear. Extracellular vesicles (EVs), secreted by most cell types, serve as a critical factor in tumorigenesis via intercellular communications. Our study aims to investigate the cellular origin of EVs in ESCC, and unveil the unknown molecular and cellular mechanisms underlying cell-cell communications. Six ESCC patients were enrolled and single-cell RNA sequencing (scRNA-seq) analyses were conducted to screen different cell subpopulations. The genetic origin of EVs was tracked using the supernatant from different cellular extracts. Nanoparticle tracking analysis (NTA), western blot analysis, and transmission electron microscopy (TEM) were performed for validation. Using scRNA-seq analysis, eleven cell subpopulations were identified in ESCC. Differences in gene expression in EVs between malignant and non-malignant esophageal tissues were found. Our findings demonstrated that epithelial cells releasing EVs were the most prevalent in malignant tissues, while endothelial cells and fibroblasts releasing EVs were predominant in non-malignant tissues. Furthermore, the high levels of gene expression in EVs released from these cells were correlated significantly with a worse prognosis. Our findings revealed the genetic origin of EVs in malignant and non-malignant esophageal tissues and provided a comprehensive overview of the associated cell-cell interactions in ESCC.

Letter to the Editor: An ultra-sensitive assay using cell-free DNA fragmentomics for multi-cancer early detection.

Early detection can benefit cancer patients with more effective treatments and better prognosis, but existing early screening tests are limited, especially for multi-cancer detection. This study investigated the most prevalent and lethal cancer types, including primary liver cancer (PLC), colorectal adenocarcinoma (CRC), and lung adenocarcinoma (LUAD). Leveraging the emerging cell-free DNA (cfDNA) fragmentomics, we developed a robust machine learning model for multi-cancer early detection. 1,214 participants, including 381 PLC, 298 CRC, 292 LUAD patients, and 243 healthy volunteers, were enrolled. The majority of patients (N = 971) were at early stages (stage 0, N = 34; stage I, N = 799). The participants were randomly divided into a training cohort and a test cohort in a 1:1 ratio while maintaining the ratio for the major histology subtypes. An ensemble stacked machine learning approach was developed using multiple plasma cfDNA fragmentomic features. The model was trained solely in the training cohort and then evaluated in the test cohort. Our model showed an Area Under the Curve (AUC) of 0.983 for differentiating cancer patients from healthy individuals. At 95.0% specificity, the sensitivity of detecting all cancer reached 95.5%, while 100%, 94.6%, and 90.4% for PLC, CRC, and LUAD, individually. The cancer origin model demonstrated an overall 93.1% accuracy for predicting cancer origin in the test cohort (97.4%, 94.3%, and 85.6% for PLC, CRC, and LUAD, respectively). Our model sensitivity is consistently high for early-stage and small-size tumors. Furthermore, its detection and origin classification power remained superior when reducing sequencing depth to 1× (cancer detection: ≥ 91.5% sensitivity at 95.0% specificity; cancer origin: ≥ 91.6% accuracy). In conclusion, we have incorporated plasma cfDNA fragmentomics into the ensemble stacked model and established an ultrasensitive assay for multi-cancer early detection, shedding light on developing cancer early screening in clinical practice.

PSC subtyping based on TTF-1 and p40 expression reveals distinct molecular characteristics and therapeutic strategies.

Pulmonary sarcomatoid carcinoma (PSC) is a unique form of poorly differentiated nonsmall cell lung cancer (NSCLC) and is notorious for its highly malignant nature and dismal prognosis. To introduce effective treatment for PSC patients, precise subtyping of PSC is demanding. In our study, TTF-1 and P40 immunohistochemistry (IHC) staining were applied to 56 PSC patients with multiomics data. According to IHC results, we categorized these patients into three subgroups and profiled their molecular contexture using bioinformatic skills. IHC results classified these patients into three subgroups: TTF-1 positive subgroup (n = 27), P40 positive subgroup (n = 15) and double-negative subgroup (n = 14). Spindle cell samples accounted for 35.71% (5/14) of double-negative patients, higher than others (P = .034). The three subgroups were heterogeneous in the genomic alteration spectrum, showing significant differences in the RTK/RAS pathway (P = .004) and the cell cycle pathway (P = .030). The methylation profile of the double-negative subgroup was between the other two subgroups. In similarity analysis, the TTF-1 and p40 subgroups were closely related to LUAD and LUSC, respectively. The TTF-1 positive subgroup had the highest leukocyte fraction (LF) among several cancer types, and the tumor mutation burden (TMB) of the p40 positive subgroup ranked third in the TMB list, suggesting the applicability of immunotherapy for PSC. The study established a new subtyping method of PSC based on IHC results and reveals three subgroups with distinct molecular features, providing evidence for refined stratification in the treatment of PSC.

Plasma extracellular vesicle microRNA profiling and the identification of a diagnostic signature for stage I lung adenocarcinoma.

At present, there is no effective noninvasive method for the accurate diagnosis of early-stage lung adenocarcinoma (LUAD). This study examined the profile of plasma extracellular vesicle (EV)-delivered microRNAs (miRNAs) in patients with invasive stage I LUAD. In this study, a total of 460 participants were enrolled, including 254 patients with LUAD, 76 patients with benign pulmonary nodules (BPNs), and 130 healthy control patients (HCs). miRNA sequencing was used to analyze the EV miRNA profile of the patient plasma samples (n = 150). A diagnostic signature (d-signature) was identified by applying a stepwise logistic regression algorithm, and a single-center training cohort (n = 150) was tested, followed by a multicenter validation cohort (n = 100). A d-signature comprising four EV-derived miRNAs (hsa-miR-106b-3p, hsa-miR-125a-5p, hsa-miR-3615, and hsa-miR-450b-5p) was developed for the early detection of LUAD. The d-signature had high precision with area under the curve (AUC) values of 0.917 and 0.902 in the training and test cohorts, respectively. Moreover, the d-signature could recognize patients with adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) with AUC values of 0.846 and 0.92, respectively. To sum up, our study detailed the plasma EV-derived miRNA profile in early LUAD patients and developed an EV-derived miRNA d-signature to detect early LUAD.

Comprehensive analyses of N6 -methyladenosine-related long noncoding RNA profiles with prognosis, chemotherapy response, and immune landscape in small cell lung cancer.

Small cell lung cancer (SCLC) is the most devastating subtype of lung cancer with no clinically available prognostic biomarkers. N6 -methyladenosine (m6 A) and noncoding RNAs play critical roles in cancer development and treatment response. However, little is known about m6 A-related long noncoding RNAs (lncRNAs) in SCLC. We used 206 limited-stage SCLC (LS-SCLC) samples from two cohorts to undertake the first and most comprehensive exploration of the m6 A-related lncRNA profile in SCLC and constructed a relevant prognostic signature. In total, 289 m6 A-related lncRNAs were screened out. We then built a seven-lncRNA-based signature in the training cohort with 48 RNA sequencing data using univariate and multivariate Cox regression models. The signature was well validated in an independent cohort containing 158 cases with quantitative PCR data. In both cohorts, the signature divided patients into high- and low-risk groups with significantly different survival rates (both p < 0.001). Our signature predicted chemotherapy survival benefit in patients with LS-SCLC. Receiver operating characteristic and C-index analyses indicated that the signature was better at predicting prognosis and chemotherapy benefit than other clinicopathologic features. Moreover, the signature was identified as an independent predictor of prognosis and chemotherapy response in different cohorts. Furthermore, functional analysis showed that multiple activated immune-related pathways were enriched in the low-risk group. Additionally, the signature was also closely related to various immune checkpoints and inflammatory responses. We generated the first clinically available m6 A-related lncRNA signature to predict prognosis and chemotherapy benefit in patients with LS-SCLC. Our findings could help optimize the clinical management of patients with LS-SCLC and inform future therapeutic targets for SCLC.

A novel immune checkpoints-based signature to predict prognosis and response to immunotherapy in lung adenocarcinoma.

BACKGROUND: Except for B7-CD28 family members, more novel immune checkpoints are being discovered. They are closely associated with tumor immune microenvironment and regulate the function of many immune cells. Various cancer therapeutic studies targeting these novel immune checkpoints are currently in full swing. However, studies concerning novel immune checkpoints phenotypes and clinical significance in lung adenocarcinoma (LUAD) are still limited. METHODS: We enrolled 1883 LUAD cases from nine different cohorts. The samples from The Cancer Genome Atlas (TCGA) were used as a training set, whereas seven microarray data cohorts and an independent cohort with 102 qPCR data were used for validation. The immune profiles and potential mechanism of the system were also explored. RESULTS: After univariate Cox proportional hazards regression and stepwise multivariable Cox analysis, a novel immune checkpoints-based system (LTA, CD160, and CD40LG) were identified from the training set, which significantly stratified patients into high- and low-risk groups with different survivals. Furthermore, this system has been well validated in different clinical subgroups and multiple validation cohorts. It also acted as an independent prognostic factor for patients with LAUD in different cohorts. Further exploration suggested that high-risk patients exhibited distinctive immune cells infiltration and suffered an immunosuppressive state. Additionally, this system is closely linked to various classical immunotherapy biomarkers. CONCLUSION: we constructed a novel immune checkpoints-based system for LUAD, which predicts prognosis and immunotherapeutic implications. We believe that these findings will not only aid in clinical management but will also shed some light on screening appropriate patients for immunotherapy.

Treatment-related adverse events of PD-1 and PD-L1 inhibitor-based combination therapies in clinical trials: a systematic review and meta-analysis.

BACKGROUND: Numerous ongoing trials are testing anti-PD-1-based or anti-PD-L1-based cancer treatment combinations. Understanding the toxicity profiles of treatment-related adverse events is essential. The aim of this study was to comprehensively investigate the incidences and profiles of treatment-related adverse events across different combination therapies. METHODS: We did a systematic review and meta-analysis comparing different chemotherapy, targeted therapy, immunotherapy, and radiotherapy combinations with PD-1 or PD-L1 inhibitors. We searched Pubmed, Embase, and Cochrane databases for articles published in English between Jan 1, 2000, and May 21, 2020, investigating globally approved PD-1 or PD-L1 inhibitor-based combination therapies. Only prospective trials reporting overall incidence or tabulated data of treatment-related adverse events were included. Trials investigating sequential therapies, comprising three or more classes of therapies, and enrolling less than ten patients were excluded. The primary outcomes were overall incidences and profiles for all-grade and grade 3 or higher treatment-related adverse events by random-effect models. Heterogeneity between studies was assessed with I2 statistics. The summary measures for main outcomes are incidences (95% CI). The 95% CI were calculated together with the incidence through a random-effects model with a logit transformation. The protocol is registered with PROSPERO (CRD42020189617). FINDINGS: We identified 2540 records, of which 161 studies (17 197 patients) met the inclusion criteria. The overall incidence of treatment-related adverse events in the chemotherapy combination was 97·7% (95% CI 96·4-98·5; I2=75%) for all-grade adverse events and 68·3% (60·7-75·0; I2=93%) for grade 3 or higher adverse events; in the targeted therapy combination was 94·5% (90·7-96·8; I2=86%) for all-grade adverse events and 47·3% (37·3-57·5; I2=93%) for grade 3 or higher adverse events; in the immunotherapy combination was 86·8% (80·9-91·1; I2=94%) for all-grade adverse events and 35·9% (29·5-42·9; I2=92%) for grade 3 or higher adverse events; and in the radiotherapy combination was 89·4% (69·0-96·9; I2=74%) for all-grade adverse events and 12·4% (4·4-30·6; I2=73%) for grade 3 or higher adverse events. For these four combination therapies, the most common all-grade adverse events were anaemia (45.4% [95% CI 32·4-59·1]), fatigue (34·3% [27·5-41·9]), fatigue (26·4% [19·2-35·2]), and dysphagia (30·0% [18·7-44·5]), respectively, and the most common grade 3 or higher adverse events were neutropenia (19·6% [13·5-27·7]), hypertension (9·3% [5·7-14·9]), lipase increased (7·2% [5·2-9·9]), and lymphopenia (10·3% [4·5-21·8]). All included randomised controlled trials had a low risk of bias. INTERPRETATION: Our study provides comprehensive data on treatment-related adverse events of different PD-1 or PD-L1 inhibitor-based combination therapies. Our results provide an essential reference of toxicity profiles of PD-1 or PD-L1 inhibitor-based combination therapies for clinicians in routine practice of cancer care. FUNDING: National Key Research and Development Programme, National Natural Science Foundation of China key program, National Natural Science Foundation of China general program, Chinese Academy of Medical Sciences Initiative for Innovative Medicine, Beijing Municipal Science and Technology Commission, Non-profit Central Research Institute Fund.

Comprehensive analysis of a chemokine- and chemokine receptor family-based signature for patients with lung adenocarcinoma.

The clinical significance and comprehensive features of chemokines and their receptors in lung adenocarcinoma (LUAD) have not been clarified. We aimed to characterize the expression profiles of chemokine and chemokine receptor family members and construct a chemokine- and chemokine receptor-based prognosis signature. A total of 1511 patients with LUAD from seven independent cohorts were included in the study. The training set collected from The Cancer Genome Atlas (TCGA) database containing 468 cases. The validation was performed on the basis of six different cohorts downloaded from Gene Expression Omnibus (GEO) database. A five-chemokine- and chemokine receptor-(CXCL2, CXCL13, CCL26, CCL20, CX3CR1) based prognosis signature was constructed with TCGA dataset using LASSO Cox regression and Cox proportional hazards regression analysis. A multivariate analysis verified that this signature was an independent prognostic factor. The predictive value of this signature was further verified by other six independent cohorts and multiple clinical subtypes. We performed immune cell infiltration analysis and biological pathway analysis which provided more insight into this signature-related immune and inflammatory landscape and clarified the intrinsic molecular mechanism by which this signature could be used to predict clinical prognosis. Furthermore, we explored the close relationship between this signature and tumor mutation burden (TMB), neoantigen burden, PD-1, PD-L1, CTLA4, TIDE score, T cell-inflamed score. This signature provided a robust prognostic biomarker for LUAD and could serve as a predictor for immunotherapy response, which may be used as an important supplement to immunotherapy to achieve individualized tumor treatment by optimizing the prognostic management and immunotherapy for patients with LUAD.

m6A regulator expression profile predicts the prognosis, benefit of adjuvant chemotherapy, and response to anti-PD-1 immunotherapy in patients with small-cell lung cancer.

BACKGROUND: Small cell lung cancer (SCLC) is lethal and possesses limited therapeutic options. Platinum-based chemotherapy-with or without immune checkpoint inhibitors (anti-PDs)-is the current first-line therapy for SCLCs; however, its associated outcomes are heterogeneous. N6-methyladenosine (m6A) is a novel and decisive factor in tumour progression, chemotherapy resistance, and immunotherapy response. However, m6A modification in SCLC remains poorly understood. METHODS: We systematically explored the molecular features and clinical significance of m6A regulators in SCLC. We then constructed an m6A regulator-based prognostic signature (m6A score) based on our examination of 256 cases with limited-stage SCLC (LS-SCLC) from three different cohorts-including an independent cohort that contained 150 cases with qPCR data. We additionally evaluated the relationships between the m6A score and adjuvant chemotherapy (ACT) benefits and the patients' responses to anti-PD-1 treatment. Immunohistochemical (IHC) staining and the HALO digital pathological platform were used to calculate CD8+ T cell density. RESULTS: We observed abnormal somatic mutations and expressions of m6A regulators. Using the LASSO Cox model, a five-regulator-based (G3BP1, METTL5, ALKBH5, IGF2BP3, and RBM15B) m6A score was generated from the significant regulators to classify patients into high- and low-score groups. In the training cohort, patients with high scores had shorter overall survival (HR, 5.19; 2.75-9.77; P < 0.001). The prognostic accuracy of the m6A score was well validated in two independent cohorts (HR 4.6, P = 0.006 and HR 3.07, P < 0.001). Time-dependent ROC and C-index analyses found the m6A score to possess superior predictive power than other clinicopathological parameters. A multicentre multivariate analysis revealed the m6A score to be an independent prognostic indicator. Additionally, patients with low scores received a greater survival benefit from ACT, exhibited more CD8+ T cell infiltration, and were more responsive to cancer immunotherapy. CONCLUSIONS: Our results, for the first time, affirm the significance of m6A regulators in LS-SCLC. Our multicentre analysis found that the m6A score was a reliable prognostic tool for guiding chemotherapy and immunotherapy selections for patients with SCLC.

Development and Validation of a Nomogram Prognostic Model for Resected Limited-Stage Small Cell Lung Cancer Patients.

BACKGROUND: In this study, we developed and validated nomograms for predicting the survival in surgically resected limited-stage small cell lung cancer (SCLC) patients. METHODS: The SCLC patients extracted from the Surveillance, Epidemiology, and End Results database between 2000 and 2014 were reviewed. Significant prognostic factors were identified and integrated to develop the nomogram using multivariable Cox regression. The model was then validated internally by bootstrap resampling, and externally using an independent SCLC cohort diagnosed between 2000 and 2015 at our institution. The prognostic performance was measured by the concordance index (C-index) and calibration curve. RESULTS: A total of 1006 resected limited-stage SCLC patients were included in the training cohort. Overall, 444 cases from our institution constituted the validation cohort. Seven prognostic factors were identified and entered into the nomogram construction. The C-indexes of this model in the training cohort were 0.723, 0.722, and 0.746 for predicting 1-, 3-, and 5-year overall survival (OS), respectively, and 0.816, 0.710, and 0.693, respectively, in the validation cohort. The calibration curve showed optimal agreement between nomogram-predicted survival and actual observed survival. Additionally, significant distinctions in survival curves between different risk groups stratified by prognostic scores were also observed. The proposed nomogram was then deployed into a website server for convenient application. CONCLUSIONS: We developed and validated novel nomograms for individual prediction of survival for resected limited-stage SCLC patients. These models perform better than the previously widely used staging system and may offer clinicians instructions for strategy making and the design of clinical trials.

An immune-related lncRNA signature predicts prognosis and adjuvant chemotherapeutic response in patients with small-cell lung cancer.

BACKGROUND: Patients with small-cell lung cancer (SCLC) are burdened by limited treatment options and the disease's dismal prognosis. Long non-coding RNAs (lncRNAs) are essential regulators of genetic alteration and are actively involved in tumor immunity. However, few studies have examined interactions between immune genes and lncRNAs in SCLC. METHODS: Immune-related lncRNA (irlncRNA) expression profiles and their clinical significance were explored. We enrolled 227 patients with SCLC, including 79 cases from GSE65002 and 148 cases from a validation cohort with corresponding qPCR data. The least absolute shrinkage and selection operator (LASSO) model was applied to identify prognostic irlncRNAs for an irlncRNA-based SCLC signature. We additionally investigated the potential mechanisms and immune landscape of the signature using bioinformatics methods. RESULTS: An irlncRNA signature including 8 irlncRNAs (ENOX1-AS1, AC005162, LINC00092, RPL34-AS1, AC104135, AC015971, AC126544, AP001189) was established for patients with SCLC in the training cohort. Low-risk patients were more likely to benefit from chemotherapy and achieve a favorable prognosis. The signature was also well-validated in the validation cohort and various clinical subgroups. Compared to other clinical parameters, the irlncRNA signature exhibited superior predictive performance for chemotherapy response and prognosis. The signature was as an independent prognostic factor in the training and validation cohorts. Interestingly, low-risk patients showed an activated immune phenotype. CONCLUSION: We constructed the first irlncRNA-based signature for chemotherapy efficacy and outcome prediction. The irlncRNA signature is a reliable and robust prognostic classifier that could be useful for clinical management and determination of potential chemotherapy benefit for patients with SCLC.

Associations between female lung cancer risk and sex steroid hormones: a systematic review and meta-analysis of the worldwide epidemiological evidence on endogenous and exogenous sex steroid hormones.

BACKGROUND: Published findings suggest sex differences in lung cancer risk and a potential role for sex steroid hormones. Our aim was to perform a meta-analysis to investigate the effects of sex steroid hormone exposure specifically on the risk of lung cancer in women. METHODS: The PubMed, MEDLINE, Web of Science, and EMBASE databases were searched. The pooled odds ratios (ORs) and 95% confidence intervals (95% CIs) for female lung cancer risk associated with sex steroid hormones were calculated overall and by study design, publication year, population, and smoking status. Sensitivity analysis, publication bias, and subgroup analysis were performed. RESULTS: Forty-eight studies published between 1987 and 2019 were included in the study with a total of 31,592 female lung cancer cases and 1,416,320 subjects without lung cancer. Overall, higher levels of sex steroid hormones, both endogenous (OR: 0.92, 95% CI: 0.87-0.98) and exogenous (OR: 0.86, 95% CI: 0.80-0.93), significantly decreased the risk of female lung cancer by 10% (OR: 0.90, 95% CI: 0.86-0.95). The risk of lung cancer decreased more significantly with a higher level of sex steroid hormones in non-smoking women (OR: 0.88, 95% CI: 0.78-0.99) than in smoking women (OR: 0.98, 95% CI: 0.77-1.03), especially in Asia women (OR: 0.84, 95% CI: 0.74-0.96). CONCLUSIONS: Our meta-analysis reveals an association between higher levels of sex steroid hormone exposure and the decreased risk of female lung cancer. Surveillance of sex steroid hormones might be used for identifying populations at high risk for lung cancer, especially among non-smoking women.

Utility of isocitrate dehydrogenase 1 as a serum protein biomarker for the early detection of non-small-cell lung cancer: A multicenter in vitro diagnostic clinical trial.

We aimed to verify the expression status and diagnostic significance of isocitrate dehydrogenase 1 (IDH1) in non-small-cell lung cancer (NSCLC), especially during early stages. Serum IDH1 levels were measured by ELISA. A total of 1223 participants (660 patients with NSCLC, 276 healthy controls [HCs], 95 patients with benign pulmonary conditions [BPCs], 135 patients with other cancers [OCs], and 57 samples with interfering factors) were divided into a training cohort and a validation cohort according to 3 testing centers. The IDH1 concentrations in the NSCLC group were obviously higher than those in the control groups (P < .001). Area under the receiver operating characteristic curves (AUCs) for discriminating NSCLC patients from controls (HC, BPC, and OC) were 0.870 and 0.745 (sensitivity, 63.3% and 55.0%; specificity, 86.8% and 86.3%) in the training cohort and validation cohort, respectively. The AUCs for discriminating stage 0-IA lung cancer patients from HCs were 0.907 and 0.788 (sensitivity, 58.6% and 59.1%; specificity, 92.9% and 89.3%) in 2 cohorts, respectively. Isocitrate dehydrogenase 1 showed specificity for NSCLC and had no diagnostic value for other common cancers. Furthermore, IDH1 was significantly reduced in postoperative serum. Isocitrate dehydrogenase 1 shows clinical utility as a serum protein biomarker for the early diagnosis of NSCLC.

Independent and joint associations of blood lipids and lipoproteins with lung cancer risk in Chinese males: A prospective cohort study.

To investigate the independent and joint associations of blood lipids and lipoproteins with lung cancer risk in Chinese males, a prospective cohort study was conducted. A total of 109,798 males with baseline information on total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C) and non-HDL were prospectively observed from 2006 to 2015 for cancer incidence. Cox proportional hazards models and restricted cubic spline (RCS) analysis were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs). During a 9-year follow-up, a total of 986 lung cancer cases were identified. Multivariable analyses showed that both males with low TC (HRQ1vs.Q2 = 1.27, 95%CI: 1.02-1.60) and males with high TC (HRQ5vs.Q2 = 1.30, 95%CI: 1.04-1.63) had an increased lung cancer risk, and the U-shaped association was also revealed in the RCS analysis (poverall = 0.013, pnonlinear = 0.006). Furthermore, both low TG (HRQ1vs.Q2 = 1.24, 95%CI: 0.99-1.54) and high TG (HRQ5vs.Q2 = 1.27, 95%CI: 1.01-1.59) were associated with increased lung cancer risk, while low LDL-C (HRQ1vs.Q2 = 1.38, 95%CI: 1.11-1.72) was associated with increased lung cancer risk. When TC, TG and LDL-C were considered jointly, the number of abnormal indicators was linearly associated with an increased risk of lung cancer (ptrend < 0.001), as subjects with three abnormal indicators had a twofold higher risk of developing lung cancer (HR = 2.02, 95%CI: 1.62-2.54). Notably, these associations were statistically significant among never smokers, never drinkers and overweight/obese males. These findings suggest that dyslipidemia may potentially be a modifiable risk factor that has key scientific and clinical significance for lung cancer prevention.

mTOR up-regulation of BEX4 promotes lung adenocarcinoma cell proliferation by potentiating OCT4.

Previously, BEX family members have been reported to participate in cancer development. However, little is known about the role of BEX4 in lung adenocarcinoma (LAC). Here, we found that BEX4 was over-expressed in LAC tissues compared with adjacent tissues. LAC tissues from metastatic patients exhibited higher expression of BEX4 comparing to those from non-metastatic ones. In vitro, BEX4 ectopic expression accelerated the proliferation of both A549 and H1975 cells. By contrast, knockdown of BEX4 suppressed the proliferation of A549 and H1975 cells. BEX4 positively regulated the expression of OCT4, silencing of which reduced the proliferation of A549 and H1975 cells with over-expressed BEX4. Additionally, mTOR activation, which is frequently observed in LAC, potentiated BEX4 depending on mTORC1 but not mTORC2. BEX4 abundance dictated the sensitivity of A549 and H1975 cells to rapamycin treatment. Our findings reveal that BEX4 is an oncogene in LAC and may contribute to the hyper-active mTOR-induced LAC development.

Sleep duration and the risk of cancer: a systematic review and meta-analysis including dose-response relationship.

BACKGROUND: The effect of sleep duration on cancer risk remains controversial. We aimed to quantify the available evidence on this relationship using categorical and dose-response meta-analyses. METHODS: Population-based cohort studies and case-control studies with at least three categories of sleep duration were identified by searching PubMed, EMBASE, and the Cochrane Library database up to July 2017. RESULTS: Sixty-five studies from 25 articles were included, involving 1,550,524 participants and 86,201 cancer cases. The categorical meta-analysis revealed that neither short nor long sleep duration was associated with increased cancer risk (short: odds ratio [OR] = 1.01, 95% confidence intervals [CI] = 0.97-1.05; long: OR = 1.02, 95% CI = 0.97-1.07). Subgroup analysis revealed that short sleep duration was associated with cancer risk among Asians (OR = 1.36; 95% CI: 1.02-1.80) and long sleep duration significantly increased the risk of colorectal cancer (OR = 1.21; 95% CI: 1.08-1.34). The dose-response meta-analysis showed no significant relationship between sleep duration and cancer risk. When treated as two linear piecewise functions with a cut point of 7 h, similar nonsignificant associations were found (per 1-h reduction: OR = 1.02, 95% CI = 0.98-1.07; per 1-h increment: OR = 1.003, 95% CI = 0.97-1.03). CONCLUSION: Categorical meta-analysis indicated that short sleep duration increased cancer risk in Asians and long sleep duration increased the risk of colorectal cancer, but these findings were not consistent in the dose-response meta-analysis. Long-term randomized controlled trials and well-designed prospective studies are needed to establish causality and to elucidate the mechanism underlying the association between sleep duration and cancer risk.