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Abstract Background This study aimed to establish a nomogram for predicting risk of recurrence and provide a model for decision‐making between lobectomy and sublobar resection in patients with stage IA lung adenocarcinoma. Methods Patients diagnosed with stage IA lung adenocarcinoma (LUAD) between December 2010 and October 2018 from Cancer Hospital Chinese Academy of Medical Sciences were included. Patients were randomly assigned to training and validation cohorts, accounting for 70% and 30% of the total cases, respectively. We collected laboratory variables before surgery. Univariate and multivariate analyses were performed in the training cohort to identify variables significantly associated with recurrence‐free survival (RFS) which were subsequently used to construct a nomogram. Validation was conducted in both cohorts. A receiver operating characteristic curve was used to determine the optional cutoff values of the scores calculated from the nomogram. Patients were then divided into low‐ and high‐risk groups. Survival was performed to determine if the nomogram could guide the operation method. Results A total of 543 patients were included in this study. Gender, albumin level, carcinoembryonic antigen level and cytokeratin‐19‐fragment level were included in the nomogram. In both cohorts, the nomogram stratified the patients into high‐ and low‐risk groups in terms of RFS. In particular, there was a significant difference in RFS between lobectomy and sublobar resection in the high‐risk group. Conclusions Gender, albumin level, carcinoembryonic antigen level and cytokeratin‐19‐fragment level are valuable markers in predicting recurrence and can guide surgical practice in patients with stage IA LUAD.

Abstract Multimodal epigenetic characterization of cell-free DNA (cfDNA) could improve the performance of blood-based early cancer detection. However, integrative profiling of cfDNA methylome and fragmentome has been technologically challenging. Here, we adapt an enzyme-mediated methylation sequencing method for comprehensive analysis of genome-wide cfDNA methylation, fragmentation, and copy number alteration (CNA) characteristics for enhanced cancer detection. We apply this method to plasma samples of 497 healthy controls and 780 patients of seven cancer types and develop an ensemble classifier by incorporating methylation, fragmentation, and CNA features. In the test cohort, our approach achieves an area under the curve value of 0.966 for overall cancer detection. Detection sensitivity for early-stage patients achieves 73% at 99% specificity. Finally, we demonstrate the feasibility to accurately localize the origin of cancer signals with combined methylation and fragmentation profiling of tissue-specific accessible chromatin regions. Overall, this proof-of-concept study provides a technical platform to utilize multimodal cfDNA features for improved cancer detection.

Abstract Background The ability of lung cancer screening to manage pulmonary nodules was limited because of the high false‐positive rate in the current mainstream screening method, low‐dose computed tomography (LDCT). We aimed to reduce overdiagnosis in Chinese population. Methods Lung cancer risk prediction models were constructed using data from a population‐based cohort in China. Independent clinical data from two programs performed in Beijing and Shandong, respectively, were used as the external validation set. Multivariable logistic regression models were used to estimate the probability of lung cancer incidence in the whole population and in smokers and nonsmokers. Results In our cohort, 1,016,740 participants were enrolled between 2013 and 2018. Of 79,581 who received LDCT screening, 5165 participants with suspected pulmonary nodules were allocated into the training set, of which, 149 lung cancer cases were diagnosed. In the validation set, 1815 patients were included, and 800 developed lung cancer. The ages of patients and radiologic factors of nodules (calcification, density, mean diameter, edge, and pleural involvement) were included in our model. The area under the curve (AUC) values of the model were 0.868 (95% CI: 0.839–0.894) in the training set and 0.751 (95% CI: 0.727–0.774) in the validation set. The sensitivity and specificity were 70.5% and 70.9%, respectively, which could reduce the 68.8% false‐positive rate in simulated LDCT screening. There was no substantial difference between smokers' and nonsmokers' prediction models. Conclusion Our models could facilitate the diagnosis of suspected pulmonary nodules, effectively reducing the false‐positive rate of LDCT for lung cancer screening.

Abstract Background In China, over 50% of lung cancer cases occur in nonsmokers. Thus, identifying high‐risk individuals for targeted lung cancer screening is crucial. Beyond age and smoking, determining other risk factors for lung cancer in the Asian population has become a focal point of research. Using 30,000 participants in the prospectively enrolled cohort at China's National Cancer Center (NCC) over the past 14 years, we categorized participants by risk, with an emphasis on nonsmoking females. Materials and Methods Between November 2005 and December 2019, 31,431 individuals voluntarily underwent low‐dose computed tomography (LDCT) scans for lung cancer screening at the NCC. We recorded details like smoking history, exposure to hazards, and family history of malignant tumors. Using the 2019 NCCN criteria, participants were categorized into high‐, moderate‐, and low‐risk groups. Additionally, we separated non‐high‐risk groups into female never smokers (aged over 40) exposed to second‐hand smoke (SHS) and others. Any positive results from initial scans were monitored per the I‐ELCAP protocol (2006), and suspected malignancies were addressed through collaborative decisions between patients and physicians. We analyzed and compared the detection rates of positive results, confirmed lung cancers, and cancer stages across risk, age, and gender groups. Results Out of 31,431 participants (55.9% male, 44.1% female), 3695 (11.8%) showed positive baseline LDCT scans with 197 (0.6%; 106 females, 91 males) confirmed as lung cancer cases pathologically. Malignancy rate by age was 0.1% among those aged under 40 years, 0.4% among those aged 40–49 years, 0.8% among those aged 50–59 years, and 1.2% among those aged 60 years and older. From the 25,763 participants (56.9% male, 43.1% female) who completed questionnaires, 1877 (7.3%) were categorized as high risk, 6500 (25.2%) as moderate risk, and 17,386 (67.5%) as low risk. Of the 23,886 in the non‐high‐risk category, 8041 (33.7%) were females over 40 years old exposed to SHS. The high‐risk group showed the highest lung cancer detection rate at 1.4%. However, females exposed to SHS had a notably higher detection rate than the rest of the non‐high‐risk group (1.1% vs. 0.5%; p

Abstract Objectives This study aims to characterize the specific organ metastatic rates in lung adenocarcinoma (LUAD) patients and identify the prognosis‐associated factors. Methods Using the Surveillance, Epidemiology and End Results database, 40 117 patients diagnosed with positive histology as the only primary LUAD were included. We stratified patients by diagnosed year, age, sex, race/ethnicity, marital status, insurance, location, TNM stage, organ‐specific metastases, surgery, chemotherapy, and radiation therapy. We performed multivariable logistic and Cox regression to identify the factors associated with the presence of specific organ metastases and prognosis predictors. Results For the 40 117 LUAD patients, 43.69%, 26.25%, 19.66%, 10.60%, and 17.89% had specific organ, bone, brain, liver, and lung metastases, respectively. The average survival in patients with organ metastases was 12.19 months, compared to 36.40 months in patients without metastases. In different kinds of metastatic organ cohorts, the longest average survival was 12.60 months in the lung metastases cohort, and the shortest was 8.43 months in liver metastases cohort. In total, 571 patients with metastases received surgery, which was significantly associated with decreased mortality (hazard ratio 1.82, 95% confidence interval 1.65–2.01, p

Abstract Introduction With the advancements in early diagnosis, more and more patients with multiple primary lung cancer (MPLC) have been identified. However, the progression of MPLC involves complex changes in cell composition and metabolic function, which remains largely controversial. Objective Our study aims to comprehensively reveal the cellular characteristics and inter‐cellular connections of MPLC. Methods We performed scRNA‐seq from 23 samples of six MPLC patients, combined with bulk whole‐exome sequencing. We performed trajectory analysis to investigate the transition of different cell types during the development of MPLC. Results A total of 1 67 397 cells were sequenced derived from tumour and adjacent tissues of MPLC patients, and tumour, normal, immune and stromal cells were identified. Two states of epithelial cells were identified, which were associated with immune response and cell death, respectively. Furthermore, both CD8+ naïve and memory T cells participated in the differentiation of CD8+ T cells. The terminal states of CD8+ T cells were exhausted T cells and cytotoxic T cells, which positively regulated cell death and were implicated in the regulation of cytokine production, respectively. Two main subpopulations of B cells with distinct functions were identified, which participate in the regulation of the immune response and antigen presentation, respectively. In addition, we found a specific type of endothelial cells that were abundant in tumour samples, with an increasing trend from normal to tumour samples. Conclusions Our study showed the comprehensive landscape of different cells of MPLC, which might reveal the key cellular mechanisms and, therefore, may provide new insights into the early diagnosis and treatment of MPLC.

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.

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.

Abstract Background Epigenetic alterations have been shown to contribute immensely to human carcinogenesis. Dynamic and reversible N6‐methyladenosine (m6A) RNA modification regulates gene expression and cell fate. However, the reasons for activation of KIAA1429 (also known as VIRMA, an RNA methyltransferase) and its underlying mechanism in lung adenocarcinoma (LUAD) remain largely unexplored. In this study, we aimed to clarify the oncogenic role of KIAA1429 in the tumorigenesis of LUAD. Methods Whole‐genome sequencing and transcriptome sequencing of LUAD data were used to analyze the gene amplification of RNA methyltransferase. The in vitro and in vivo functions of KIAA1429 were investigated. Transcriptome sequencing, methylated RNA immunoprecipitation sequencing (MeRIP‐seq), m6A dot blot assays and RNA immunoprecipitation (RIP) were performed to confirm the modified gene mediated by KIAA1429. RNA stability assays were used to detect the half‐life of the target gene. Results Copy number amplification drove higher expression of KIAA1429 in LUAD, which was correlated with poor overall survival. Manipulating the expression of KIAA1429 could regulate the proliferation and metastasis of LUAD. Mechanistically, the target genes of KIAA1429‐mediated m6A modification were confirmed by transcriptome sequencing and MeRIP‐seq assays. We also revealed that KIAA1429 could regulate BTG2 expression in an m6A‐dependent manner. Knockdown of KIAA1429 significantly decreased the m6A levels of BTG2 mRNA, leading to enhanced YTH m6A RNA binding protein 2 (YTHDF2, the m6A “reader”)‐dependent BTG2 mRNA stability and promoted the expression of BTG2; thus, participating in the tumorigenesis of LUAD. Conclusions Our data revealed the activation mechanism and important role of KIAA1429 in LUAD tumorigenesis, which may provide a novel view on the targeted molecular therapy of LUAD.

Abstract 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.

Abstract Background Recent studies indicated that T cell immunoreceptor with immunoglobulin and ITIM domains (TIGIT) and cluster of differentiation 47 (CD47) have emerged as new potential immunotherapy targets. However, the roles of TIGIT and CD47 in lung squamous cell carcinoma (LUSC) have not been fully illustrated. Methods The specimens and clinicopathological information from 190 LUSC patients who underwent surgeries in our center were retrospectively collected. Immunohistochemical staining for TIGIT and CD47 was conducted. Transcriptional and clinical data of 479 LUSC were downloaded from The Cancer Genome Atlas (TCGA). Results In the TCGA LUSC cohort, 142 (29.6%) cases were TIGIT/CD47 dual high expression at RNA level. The expression levels of TIGIT and CD47 were significantly correlated (p

Abstract. Background:. Non-smokers account for a large proportion of lung cancer patients, especially in Asia, but the attention paid to them is limited compared with smokers. In non-smokers, males display a risk for lung cancer incidence distinct from the females—even after excluding the influence of smoking; but the knowledge regarding the factors causing the difference is sparse. Based on a large multicenter prospective cancer screening cohort in China, we aimed to elucidate the interpretable sex differences caused by known factors and provide clues for primary and secondary prevention. Methods:. Risk factors including demographic characteristics, lifestyle factors, family history of cancer, and baseline comorbidity were obtained from 796,283 Chinese non-smoking participants by the baseline risk assessment completed in 2013 to 2018. Cox regression analysis was performed to assess the sex difference in the risk of lung cancer, and the hazard ratios (HRs) that were adjusted for different known factors were calculated and compared to determine the proportion of excess risk and to explain the existing risk factors. Results:. With a median follow-up of 4.80 years, 3351 subjects who were diagnosed with lung cancer were selected in the analysis. The lung cancer risk of males was significantly higher than that of females; the HRs in all male non-smokers were 1.29 (95% confidence interval [CI]: 1.20–1.38) after adjusting for the age and 1.38 (95% CI: 1.28–1.50) after adjusting for all factors, which suggested that known factors could not explain the sex difference in the risk of lung cancer in non-smokers. Known factors were 7% (|1.29–1.38|/1.29) more harmful in women than in men. For adenocarcinoma, women showed excess risk higher than men, contrary to squamous cell carcinoma; after adjusting for all factors, 47% ([1.30–1.16]/[1.30–1]) and 4% ([7.02–6.75]/[7.02–1])) of the excess risk was explainable in adenocarcinoma and squamous cell carcinoma. The main causes of gender differences in lung cancer risk were lifestyle factors, baseline comorbidity, and family history. Conclusions:. Significant gender differences in the risk of lung cancer were discovered in China non-smokers. Existing risk factors did not explain the excess lung cancer risk of all non-smoking men, and the internal causes for the excess risk still need to be explored; most known risk factors were more harmful to non-smoking women; further exploring the causes of the sex difference would help to improve the prevention and screening programs and protect the non-smoking males from lung cancers.

Abstract 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.

BackgroundStudying the application of neoadjuvant immunochemotherapy (NICT) in the real world and evaluating its effectiveness and safety in comparison with neoadjuvant chemotherapy (NCT) are critically important.MethodsThis study included the II-IIIB stage non-small cell lung cancer (NSCLC) patients receiving NCT with or without PD-1 inhibitors and undergoing surgery after neoadjuvant treatments between January 2019 to August 2022. The clinical characteristics and treatment outcomes were retrospectively reviewed and analyzed.ResultsA total of 66 patients receiving NICT and 101 patients receiving NCT were included in this study. As compared to NCT, NICT showed similar safety while not increasing the surgical difficulty. The ORR in the NICT and NCT groups was 74.2% and 53.5%, respectively, P = 0.009. A total of 44 patients (66.7%) in the NICT group and 21 patients (20.8%) in the NCT group showed major pathology response (MPR) (P

ObjectivesThis study aimed to evaluate the prognostic significance of the eighth edition TNM stage criteria in patients with combined small-cell lung cancer (C-SCLC) on a population level.MethodsUsing the Surveillance, Epidemiology, and End Results (SEER) database, patients diagnosed with C-SCLC (histology code 8245) between the years 2004 and 2015 were identified. We performed a Kaplan–Meier analysis and used the multivariable cox regression proportional hazards model to obtain prognostic overall survival estimates for each group of patients.ResultsA total of 477 patients diagnosed with C-SCLC were identified. The T, N, M, TNM, and combined TNM stage status of the eighth edition were all significant prognostic factors for patients’ overall survivals, with the best discrimination identified in the combined stages. Surgery was also found to be a prognostic factor (HR =1.95, 95%CI =1.49-2.56, p

Abstract Anti-programmed death-1 (PD-1)/programmed death-ligand 1 (PD-L1) immunotherapy has dramatically changed the therapeutic landscape of inoperable non-small cell lung cancer (NSCLC), and has been included in first-line treatments. Sintilimab is a domestic anti-PD-1 monoclonal antibody in China that has received approvals from the National Medical Products Administration to treat classical Hodgkin’s lymphoma, hepatocellular carcinoma, and squamous and non-squamous NSCLC. In a prospective clinical study we led, neoadjuvant sintilimab has led to major and complete pathologic responses, which are recommended as surrogate endpoints for neoadjuvant immunotherapy; however, its effect remains inconclusive in pulmonary ground glass nodules. Meanwhile, combination plans seem more likely to be satisfying therapeutic options. Specifically, sintilimab plus platinum-based chemotherapy plans conferred better anti-tumor efficacy and clinical benefits compared to chemotherapy alone, which led to their approval in China and the acceptance of a biological license application in the US. Besides, the combination with other plans, such as docetaxel, cytokine-induced killer cell immunotherapy, radiation therapy, and anlotinib have also shown promising anti-tumor efficacy, with acceptable toxicities, and are therefore worth further exploration. In addition, several clinical trials on NSCLC at our center are ongoing. In general, sintilimab and its combinatorial plans were effective and well tolerated, but the treatment requires appropriate timing; pathologic responses can be surrogate endpoints for neoadjuvant immunotherapy, while more effective biomarkers are warranted. This study provides an overview of sintilimab-based clinical trials on NSCLC, and may support further investigation of sintilimab in future clinical trials.

Abstract Background Screening with low‐dose computed tomography (LDCT) is an efficient way to detect lung cancer at an earlier stage, but has a high false‐positive rate. Several pulmonary nodules risk prediction models were developed to solve the problem. This systematic review aimed to compare the quality and accuracy of these models. Methods The keywords “lung cancer,” “lung neoplasms,” “lung tumor,” “risk,” “lung carcinoma” “risk,” “predict,” “assessment,” and “nodule” were used to identify relevant articles published before February 2021. All studies with multivariate risk models developed and validated on human LDCT data were included. Informal publications or studies with incomplete procedures were excluded. Information was extracted from each publication and assessed. Results A total of 41 articles and 43 models were included. External validation was performed for 23.2% (10/43) models. Deep learning algorithms were applied in 62.8% (27/43) models; 60.0% (15/25) deep learning based researches compared their algorithms with traditional methods, and received better discrimination. Models based on Asian and Chinese populations were usually built on single‐center or small sample retrospective studies, and the majority of the Asian models (12/15, 80.0%) were not validated using external datasets. Conclusion The existing models showed good discrimination for identifying high‐risk pulmonary nodules, but lacked external validation. Deep learning algorithms are increasingly being used with good performance. More researches are required to improve the quality of deep learning models, particularly for the Asian population.

ObjectivesThis study aimed to identify the computed tomography (CT) features associated with the new International Association for the Study of Lung Cancer (IASLC) three-tiered grading system to improve the preoperative prediction of disease-free survival of stage I lung adenocarcinoma patients.MethodsThe study included 379 patients. Ordinal logistic regression analysis was used to identify the independent predictors of IASLC grades. The first multivariate Cox regression model (Model 1) was based on the significant factors from the univariate analysis. The second multivariate model (Model 2) excluded the histologic grade and based only on preoperative factors.ResultsLarger consolidation tumor ratio (OR=2.15, P

Abstract 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.

ctDNA has been shown to identify minimal residual disease (MRD) and is thus dynamically monitored in different types of tumours. Here, the authors show that serial longitudinal ctDNA analysis can be used as a tool to detect MRD, inform the use of adjuvant therapy, and predict recurrence risk in lung cancer.

Abstract 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. N 6-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.

ABSTRACT: Familial risk of lung cancer has been widely studied but whether this association holds in non-smoking females is largely unknown. We sought to determine the relationship between a family history of cancer and lung cancer risk among Chinese non-smoking females based on a multi-center prospective population-based cohort study involving 547,218 individuals between 2013 and 2019. A total of 1620 lung cancer cases occurred during a median follow-up of 3.9 years. Multivariable Cox regression showed that a family history of lung cancer in first-degree relatives significantly increased the risk of lung cancer (HR: 1.50, 95%CI: 1.29, 1.75, P

Summary: Background: Optimal uptake rates of low-dose computed tomography (LDCT) scans are essential for lung cancer screening (LCS) to confer mortality benefits. We aimed to outline the process model of the LCS programme in China, identify the high-risk individuals with low uptake based on a prospective multi-centre population-based cohort, and further explore associated structural characteristics. Methods: A total of 221,955 individuals at high-risk for lung cancer from the National Lung Cancer Screening cohort were included. The logistic regression model was performed to identify the individual characteristics associated with the uptake of LCS, defined as whether the high-risk individual undertook LDCT scans in designated hospitals within six months following the initial risk assessment. The linear regression model was adopted to explore the structural characteristics associated with the uptake rates in 186 communities. Findings: The overall uptake rate was 33·0%. The uptake rate was negatively correlated with the incidence of advanced-stage lung cancer (Pearson's coefficient −0·88, p-value 0·0007). Multivariable regression models found that lower uptake rates were associated with males (OR 0·88, 95%CI 0·85–0·91), current smokers (OR 0·93, 95%CI 0·90–0·96), individuals with depressive symptoms (OR 0·92, 95%CI 0·90–0·94), and the structural characteristics, including longer structural delays in initiating LDCT scans (30–90 days vs. ≤14 days: β −7·17, 95%CI −12·76∼ −1·57; >90 days vs. ≤14 days: β −13·69, 95%CI −24·61∼ −2·76), no media-assisted publicity (β −6·43, 95%CI −11·26∼ −1·60), and no navigation assistance (β −5·48, 95%CI −10·52∼ −0·44). Interpretation: Multifaceted interventions are recommended, which focus on poor-uptake individuals and integrate the ‘assessment-to-timely-screening’ approach to minimise structural delays, media publicity, and a navigation assistance along the centralised screening pathway. Funding: Ministry of Finance and National Health Commission of the People's Republic of China.

Summary: Background: The tumor microenvironment (TME) serves as an important factor in tumorigenesis and metastasis. Although distinct cell subsets can be identified via single-cell RNA sequencing (scRNA-seq), the spatial composition of cells within the TME is difficult to characterise. Methods: Tissue samples were collected from three patients with esophageal squamous cell carcinoma (ESCC), and scRNA-seq was performed to identify distinct cell subsets. In addition, a microarray-based spatial transcriptomics (ST) method was used to characterise the spatial landscape of expression data via an array of spots. Using multimodal intersection analysis (MIA) to integrate scRNA-seq and ST, the exact cellular components of the tumor and stromal regions were annotated. Findings: The subpopulations of seven stromal cells were identified within the TME of ESCC, and the architecture of scRNA-seq-determined subsets was mapped in cancer and stromal regions. The distribution of various stromal cells and their subpopulations was heterogeneous. Compared with immune cells, non-immune stromal cells were significantly enriched in the TME. Most subsets of epithelial cells were enriched in the cancer regions, whereas inflammatory cancer-associated fibroblasts were correlated with the stromal regions. Furthermore, TME features were different between metastatic and non-metastatic samples and between the primary and metastatic sites of the metastatic sample. Interpretation: This study revealed the spatial landscape of various cell subsets within the TME and the potential cross-talk among diverse cells, which provides novel insights into cancer intervention. Funding: A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.

Abstract Backgroud Recent studies have indicated that programmed cell death‐ligand 1 (PD‐L1) and cluster of differentiation 47 (CD47) play an essential role in tumor immune evasion and may serve as potential targets for combined immunotherapy. The aim of our study was to evaluate the PD‐L1/CD47 expression status in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD), and explore its survival impact and relevance with the immune microenvironment. Methods The specimens from 190 LUSC and 240 LUAD patients who underwent intent‐to‐treat surgeries were retrospectively collected for immunohistochemistry assays of PD‐L1, CD47, cluster of differentiation 8 (CD8), and cluster of differentiation 68 (CD68). Results A total of 96 (22.3%) and 296 (68.8%) cases were positive for PD‐L1 and CD47 expression, respectively, and 80 (18.6%) of them demonstrated the co‐expression of PD‐L1/CD47. The rate of PD‐L1/CD47 co‐expression was 23.7% in LUSC, significantly higher than the 14.6% in LUAD (p = 0.018). The median overall survival (OS) for all patients was 55.9 months (range 2.0–146.0 months). The univariate analysis showed that patients with positive CD47 expression (LUSC p = 0.003, LUAD p = 0.036) and PD‐L1/CD47 co‐expression (LUSC p = 0.023, LUAD p = 0.004) exhibited significantly worse prognosis. The multivariate analysis demonstrated that PD‐L1/CD47 co‐expression was an independent prognostic factor for OS (LUSC hazard ratio [HR] 1.922, 95% CI 1.245–2.969, p = 0.003; LUAD HR 1.549, 95% CI 1.015–2.364, p = 0.043). PD‐L1/CD47 co‐expression was associated with high CD8‐positive T‐lymphocyte density in LUSC (p = 0.004) and LUAD (p = 0.043), and with high CD68‐positive macrophage density in LUSC (p = 0.026). Conclusions PD‐L1/CD47 co‐expression was an independent prognostic factor for LUSC and LUAD patients and may serve as a potential predictive biomarker for combined dual‐targeting immunotherapy.

Abstract 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.

Abstract Background Albumin is supposed to be associated with cancer risk. However, evidence on serum albumin and cancer risk among the Chinese population is sparse. This study was conducted to evaluate the association between pre‐diagnostic serum albumin and cancer risk among Chinese. Methods A total of 82,061 participants with baseline information on serum albumin concentration in the Kailuan cohort were recruited. Cox proportional hazards models and restricted cubic spline (RCS) analyses were used to evaluate the association between pre‐diagnostic serum albumin and cancer risk. Results Albumin levels were inversely associated with overall cancer risk (HR [95% CI]: Q2, Q3, Q4 vs. Q1: 0.91 [0.78–1.07], 0.80 [0.70–0.92], 0.73 [0.63–0.85]), and the risk of lung, colorectal, and liver cancer (HR [95% CI]: Q4 vs. Q1: lung: 0.70 [0.52–0.95], colorectal: 0.43 [0.26–0.72], liver: 0.59 [0.36–0.95]). After excluding new cancer cases within 2 years since enrollment, a more significant association was observed for liver cancer (HR [95% CI]: Q4 vs. Q1: 0.41 [0.21–0.78]), while associations converted to nonsignificant for lung and colorectal cancer. The RCS model suggested an inverse linear association between albumin and the risk of overall cancer (p‐overall

Primary small cell carcinoma of the oesophagus has a poor prognosis, and has not been fully characterised molecularly. Here, the authors study the disease using multi-omics technology and find frequent RB1 disruptions and similarities to small cell lung cancer, opening potential therapeutic avenues.

Summary: Background: Early diagnosis benefits lung cancer patients with higher survival, but most patients are diagnosed after metastasis. Although cell-free DNA (cfDNA) analysis holds promise, its sensitivity for detecting early-stage lung cancer is unsatisfying. We leveraged cfDNA fragmentomics to develop a predictive model for invasive stage I lung adenocarcinoma (LUAD). Methods: 292 stage I LUAD patients from three medical centers were included together with 230 healthy controls whose plasma cfDNA samples were profiled by whole-genome sequencing (WGS). Multiple cfDNA fragmentomic motif features and machine learning models were compared in the training cohort to select the best model. Model performance was assessed in the internal and external validation cohorts and an additional dataset. Findings: A logistic regression model using the 6bp-breakpoint-motif feature was selected. It yielded 98·0% sensitivity and 94·7% specificity in the internal validation cohort [Area Under the Curve (AUC): 0·985], while 92·5% sensitivity and 90·0% specificity were achieved in the external validation cohort (AUC: 0·954). It is sensitive for early-stage (100% sensitivity for minimally invasive adenocarcinoma, MIA) and

Background and objective Perioperative treatment has become an increasingly important aspect of the management of patients with non-small cell lung cancer (NSCLC). Small-scale clinical studies performed in recent years have shown improvements in the major pathological remission rate after neoadjuvant therapy, suggesting that it will soon become an important part of NSCLC treatment. Nevertheless, neoadjuvant immunotherapy may be accompanied by serious adverse reactions that lead to delay or cancelation of surgery, additional illness, and even death, and have therefore attracted much attention. The purpose of the clinical recommendations is to form a diagnosis and treatment plan suitable for the current domestic medical situation for the immune-related adverse event (irAE). Methods This recommendation is composed of experts in thoracic surgery, oncologists, thoracic medicine and irAE related departments (gastroenterology, respirology, cardiology, infectious medicine, hematology, endocrinology, rheumatology, neurology, dermatology, emergency section) to jointly complete the formulation. Experts make full reference to the irAE guidelines, large-scale clinical research data published by thoracic surgery, and the clinical experience of domestic doctors and publicly published cases, and repeated discussions in multiple disciplines to form this recommendation for perioperative irAE. Results This clinical recommendation covers the whole process of prevention, evaluation, examination, treatment and monitoring related to irAE, so as to guide the clinical work comprehensively and effectively. Conclusion Perioperative irAE management is an important part of immune perioperative treatment of lung cancer. With the continuous development of immune perioperative treatment, more research is needed in the future to optimize the diagnosis and treatment of perioperative irAE.

There is no cost-effective, accurate, and non-invasive method for the detection of esophageal squamous cell carcinoma (ESCC) in clinical practice. We aimed to investigate the diagnostic potential of tumor-educated platelets in ESCC. In this study, seventy-one ESCC patients and eighty healthy individuals were enrolled and divided into a training cohort (23 patients and 27 healthy individuals) and a validation cohort (48 patients and 53 healthy individuals). Next-generation RNA sequencing was performed on platelets isolated from peripheral blood of all participants, and a support vector machine/leave-one-out cross validation (SVM/LOOCV) approach was used for binary classification. A diagnostic signature composed of ARID1A, GTF2H2, and PRKRIR discriminated ESCC patients from healthy individuals with 91.3% sensitivity and 85.2% specificity in the training cohort and 87.5% sensitivity and 81.1% specificity in the validation cohort. The AUC was 0.924 (95% CI, 0.845–0.956) and 0.893 (95% CI, 0.821–0.966), respectively, in the training cohort and validation cohort. This 3-gene platelet RNA signature could effectively discriminate ESCC from healthy control. Our data highlighted the potential of tumor-educated platelets for the noninvasive diagnosis of ESCC. Moreover, we found that keratin and collagen protein families and ECM-related pathways might be involved in tumor progression and metastasis of ESCC, which might provide insights to understand ESCC pathobiology and advance novel therapeutics.

Pulmonary sarcomatoid carcinoma (PSC) is a rare subtype of lung cancer with poor prognosis. Here the authors performed multi-omics analysis of human samples to investigate the mutational landscape of PSC and show three subgroups of PSC with distinct biology, prognosis and potential therapeutic strategies.

Abstract Background Lung adenocarcinomas (LUAD) is the most common histological subtype of lung cancers. Tumor immune microenvironment (TIME) is involved in tumorigeneses, progressions, and metastases. This study is aimed to develop a robust immune‐related signature of LUAD. Methods A total of 1774 LUAD cases sourced from public databases were included in this study. Immune scores were calculated through ESTIMATE algorithm and weighted gene co‐expression network analysis (WGCNA) was applied to identify immune‐related genes. Stability selections and Lasso COX regressions were implemented to construct prognostic signatures. Validations and comparisons with other immune‐related signatures were conducted in independent Gene Expression Omnibus (GEO) cohorts. Abundant infiltrated immune cells and pathway enrichment analyses were carried out, respectively, through ImmuCellAI and gene set enrichment analysis (GSEA). Results In Cancer Genome Atlas (TCGA) LUAD cohorts, immune scores of higher levels were significantly associated with better prognoses (P

Abstract Background Low‐dose computed tomography screening has been proved to reduce lung cancer mortality, however, the issues of high false‐positive rate and overdiagnosis remain unsolved. Risk prediction models for lung cancer that could accurately identify high‐risk populations may help to increase efficiency. We thus sought to develop a risk prediction model for lung cancer incorporating epidemiological and metabolic markers in a Chinese population. Methods During 2006 and 2015, a total of 122 497 people were observed prospectively for lung cancer incidence with the total person‐years of 976 663. Stepwise multivariable‐adjusted logistic regressions with Pentry = .15 and Pstay = .20 were conducted to select the candidate variables including demographics and metabolic markers such as high‐sensitivity C‐reactive protein (hsCRP) and low‐density lipoprotein cholesterol (LDL‐C) into the prediction model. We used the C‐statistic to evaluate discrimination, and Hosmer‐Lemeshow tests for calibration. Tenfold cross‐validation was conducted for internal validation to assess the model's stability. Results A total of 984 lung cancer cases were identified during the follow‐up. The epidemiological model including age, gender, smoking status, alcohol intake status, coal dust exposure status, and body mass index generated a C‐statistic of 0.731. The full model additionally included hsCRP and LDL‐C showed significantly better discrimination (C‐statistic = 0.735, P = .033). In stratified analysis, the full model showed better predictive power in terms of C‐statistic in younger participants (

BackgroundThe early-stage lung adenocarcinoma (LUAD) rate has increased with heightened public awareness and lung cancer screening implementation. Lipid metabolism abnormalities are associated with lung cancer initiation and progression. However, the comprehensive features and clinical significance of the immunometabolism landscape and lipid metabolism-related genes (LMRGs) in cancer recurrence for early-stage LUAD remain obscure.MethodsLMRGs were extracted from Gene Set Enrichment Analysis (GSEA) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Samples from The Cancer Genome Atlas (TCGA) were used as training cohort, and samples from four Gene Expression Omnibus (GEO) datasets were used as validation cohorts. The LUAD recurrence-associated LMRG molecular pattern and signature was constructed through unsupervised consensus clustering, time-dependent receiver operating characteristic (ROC), and least absolute shrinkage and selection operator (LASSO) analyses. Kaplan-Meier, ROC, and multivariate Cox regression analyses and prognostic meta-analysis were used to test the suitability and stability of the signature. We used Gene Ontology (GO), KEGG pathway, immune cell infiltration, chemotherapy response analyses, gene set variation analysis (GSVA), and GSEA to explore molecular mechanisms and immune landscapes related to the signature and the potential of the signature to predict immunotherapy or chemotherapy response.ResultsFirst, two LMRG molecular patterns were established, which showed diverse prognoses and immune infiltration statuses. Then, a 12-gene signature was identified, and a risk model was built. The signature remained an independent prognostic parameter in multivariate Cox regression and prognostic meta-analysis. In addition, this signature stratified patients into high- and low-risk groups with significantly different recurrence rates and was well validated in different clinical subgroups and several independent validation cohorts. The results of GO and KEGG analyses and GSEA showed that there were differences in multiple lipid metabolism, immune response, and drug metabolism pathways between the high- and low-risk groups. Further analyses revealed that the signature-based risk model was related to distinct immune cell proportions, immune checkpoint parameters, and immunotherapy and chemotherapy response, consistent with the GO, KEGG, and GSEA results.ConclusionsThis is the first lipid metabolism-based signature for predicting recurrence, and it could provide vital guidance to achieve optimized antitumor for immunotherapy or chemotherapy for early-stage LUAD.

The iron-dependent cell death named ferroptosis has been implicated in the progression and therapeutic response of several tumors. However, potential role of ferroptosis in lung adenocarcinomas (LUAD) remained less well understood. In TCGA-LUAD cohort, unsupervised clustering was first conducted based on ferroptosis regulators extracted from FerrDb database. Comprehensive correlation analysis and comparisons were performed among ferroptosis subtypes. The ferroptosis-related prognostic (FRP) signature was identified based on filtered features and repeated LASSO and was validated in five independent cohorts. The clinical relevance between the risk score and therapeutic response was further explored by multiple algorithms. qPCR was implemented to verify gene expression. A total of 1,168 LUAD patients and 161 ferroptosis regulators were included in this study. Three ferroptosis subtypes were identified and patients in subtype B had the best prognosis among the three subtypes. Significant differences in immune microenvironment and biological function enrichment were illustrated in distinct subtypes. The Boruta algorithm was conducted on 308 common differentially expressed genes for dimensionality reduction. A total of 56 genes served as input for model construction and a six-gene signature with the highest frequencies of 881 was chosen as FRP. The prognostic significance of FRP was validated in five independent cohorts. High FRP risk score was also linked to increased tumor mutation burden, PD-L1 protein expression and number of neoantigens. Of the FRP genes, 83.3% was abnormally expressed in LUAD cell lines. In conclusion, ferroptosis plays a non-negligible role in LUAD. Exploration of the ferroptosis pattern will enhance the prognostic stratification of individual patients and move toward the purpose of personalized treatment.

With the increasingly early stage lung squamous cell carcinoma (LUSC) being discovered, there is an urgent need for a comprehensive analysis of the prognostic characteristics of early stage LUSC. Here, we developed an immune-related gene signature for outcome prediction of early stage LUSC based on three independent cohorts. Differentially expressed genes (DEGs) were identified using CIBERSORT and ESTMATE algorithm. Then, a 17-immune-related gene (RPRM, APOH, SSX1, MSGN1, HPR, ISM2, FGA, LBP, HAS1, CSF2, RETN, CCL2, CCL21, MMP19, PTGIS, F13A1, C1QTNF1) signature was identified using univariate Cox regression, LASSO regression and stepwise multivariable Cox analysis based on the verified DEGs from 401 cases in The Cancer Genome Atlas (TCGA) database. Subsequently, a cohort of GSE74777 containing 107 cases downloaded from Gene Expression Omnibus (GEO) database and an independent data set consisting of 36 frozen tissues collected from National Cancer Center were used to validate the predictive value of the signature. Seventeen immune-related genes were identified from TCGA cohort, which were further used to establish a classification system to construct cases into high- and low-risk groups in terms of overall survival. This classifier was still an independent prognostic factor in multivariate analysis. In addition, another two independent cohorts and different clinical subgroups validated the significant predictive value of the signature. Further mechanism research found early stage LUSC patients with high risk had special immune cell infiltration characteristics and gene mutation profiles. In conclusion, we characterized the tumor microenvironment and established a highly predictive model for evaluating the prognosis of early stage LUSC, which may provide a lead for effective immunotherapeutic options tailored for each subtype.

Background The systemic immune‐inflammation index (SII) is correlated with patient survival in various types of solid tumors. However, only a few studies have focused on the prognostic value of the SII in patients with surgically resected non‐small cell lung cancer (NSCLC). Methods This study was a single center retrospective analysis of 569 NSCLC patients who underwent curative lobectomy at the Department of Thoracic Surgery, National Cancer Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College between 2006 and 2012. A receiver operating characteristic curve was plotted to compare the discriminatory ability of the SII for overall survival (OS). A Cox proportional hazards regression model was used to perform univariate and multivariate analyses. Results The SII, neutrophil‐lymphocyte ratio (NLR), and platelet‐lymphocyte ratio (PLR) all correlated with OS in NSCLC patients, and the SII was an independent prognostic factor for OS (hazard ratio 1.256, 95% confidence interval 1.018–1.551; P = 0.034). The area under the receiver operating characteristic curve of the SII (0.547) was larger than the NLR (0.541) and PLR (0.531). Furthermore, the SII retained prognostic significance in the lung adenocarcinoma subgroup. Conclusion The SII is a promising prognostic predictor for patients with surgically resected NSCLC and retained prognostic significance in the lung adenocarcinoma subgroup. The prognostic value of the SII is superior to the NLR and PLR.

BackgroundEsophageal squamous cell carcinoma (ESCC) is one of the most common cancer types and represents a threat to global public health. N6-Methyladenosine (m6A) methylation plays a key role in the occurrence and development of many tumors, but there are still few studies investigating ESCC. This study attempts to construct a prognostic signature of ESCC based on m6A RNA methylation regulators and to explore the potential association of these regulators with the tumor immune microenvironment (TIME).MethodsThe transcriptome sequencing data and clinical information of 20 m6A RNA methylation regulators in 453 patients with ESCC (The Cancer Genome Atlas [TCGA] cohort, n = 95; Gene Expression Omnibus [GEO] cohort, n = 358) were obtained. The differing expression levels of m6A regulators between ESCC and normal tissue were evaluated. Based on the expression of these regulators, consensus clustering was performed to investigate different ESCC clusters. PD-L1 expression, immune score, immune cell infiltration and potential mechanisms among different clusters were examined. LASSO Cox regression analysis was utilized to obtain a prognostic signature based on m6A RNA methylation modulators. The relationship between the risk score based on the prognostic signature and the TIME of ESCC patients was studied in detail.ResultsSix m6A regulators (METTL3, WTAP, IGF2BP3, YTHDF1, HNRNPA2B1 and HNRNPC) were observed to be significantly highly expressed in ESCC tissues. Two molecular subtypes (clusters 1/2) were determined by consensus clustering of 20 m6A modulators. The expression level of PD-L1 in ESCC tissues increased significantly and was significantly negatively correlated with the expression levels of YTHDF2, METL14 and KIAA1429. The immune score, CD8 T cells, resting mast cells, and regulatory T cells (Tregs) in cluster 2 were significantly increased. Gene set enrichment analysis (GSEA) shows that this cluster involves multiple hallmark pathways. We constructed a five-gene prognostic signature based on m6A RNA methylation, and the risk score based on the prognostic signature was determined to be an independent prognostic indicator of ESCC. More importantly, the prognostic value of the prognostic signature was verified using another independent cohort. m6A regulators are related to TIME, and their copy-number alterations will dynamically affect the number of tumor-infiltrating immune cells.ConclusionOur study established a strong prognostic signature based on m6A RNA methylation regulators; this signature was able to accurately predict the prognosis of ESCC patients. The m6A methylation regulator may be a key mediator of PD-L1 expression and immune cell infiltration and may strongly affect the TIME of ESCC.

Ferroptosis is a newly discovered type of programmed cell death that differs from canonical apoptosis. However, the potential role of ferroptosis in lung adenocarcinoma (LUAD) has not been elaborated. In total, 1,328 samples from databases and 36 ferroptosis regulators were included in this study. By combining random survival forest and principal component analysis algorithms, a robust prognostic ferroptosis-related risk score (FRRS) was constructed, and the performance was validated in three independent datasets. Based on the median risk score, two subgroups were identified. Then, comparisons, including of mutational profiles, functional enrichment analyses and immune components, were conducted between subgroups. An immunotherapy cohort was applied to explore potential therapeutic-related biomarkers. Finally, the clinical utility of FRRS was validated in a proteomic cohort. In the TCGA-LUAD cohort, FRRS was calculated using the expression of 11 selected genes, and patients with high FRRS had a significantly (p < 0.001) worse prognosis than those with low FRRS. Multivariate regression suggested that FRRS was an independent prognostic factor. Functional enrichment analysis indicated that FRRS was mainly involved in cell cycle, metabolic and immune-related pathways. Furthermore, FRRS was shown to be significantly (p < 0.001) associated with the abundance of CD8 T cells and tumor mutation burden (TMB). The combination of TMB and FANCD2 expression, the main contributor to FRRS, substantially increased the precision of predicting the therapeutic response. In conclusion, the present study revealed the potential role of ferroptosis regulators in LUAD and identified ferroptosis-related biomarkers for prognostic and immunotherapeutic predictions.

Lung adenocarcinoma is one of the most malignant diseases worldwide. The immune checkpoint inhibitors targeting programmed cell death protein 1 (PD-1) and programmed cell death-ligand 1 (PD-L1) have changed the paradigm of lung cancer treatment; however, there are still patients who are resistant. Further exploration of the immune infiltration status of lung adenocarcinoma (LUAD) is necessary for better clinical management. In our study, the CIBERSORT method was used to calculate the infiltration status of 22 immune cells in LUAD patients from The Cancer Genome Atlas (TCGA). We clustered LUAD based on immune infiltration status by consensus clustering. The differentially expressed genes (DEGs) between cold and hot tumor group were identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Last, we constructed a Cox regression model. We found that the infiltration of M0 macrophage cells and follicular helper T cells predicted an unfavorable overall survival of patients. Consensus clustering of 22 immune cells identified 5 clusters with different patterns of immune cells infiltration, stromal cells infiltration, and tumor purity. Based on the immune scores, we classified these five clusters into hot and cold tumors, which are different in transcription profiles. Hot tumors are enriched in cytokine–cytokine receptor interaction, while cold tumors are enriched in metabolic pathways. Based on the hub genes and prognostic-related genes, we developed a Cox regression model to predict the overall survival of patients with LUAD and validated in other three datasets. In conclusion, we developed an immune-related signature that can predict the prognosis of patients, which might facilitate the clinical application of immunotherapy in LUAD.

BackgroundLung adenocarcinoma (LUAD), as the most common histological subtype of lung cancer, is a high-grade malignancy and a leading cause of cancer-related death globally. Identification of biomarkers with prognostic value is of great significance for the diagnosis and treatment of LUAD. Heterogeneous nuclear ribonucleoprotein C (HNRNPC) is an RNA-binding protein “reader” of N6-methyladenosine (m6A) methylation, and is related to the progression of various cancers; however, its role in LUAD is unclear. The aims of this study aims were to study the expression and prognostic value of HNRNPC in LUAD.MethodsThe Oncomine database and gene expression profiling interactive analysis (GEPIA) were used for preliminary exploration of HNRNPC expression and prognostic value in LUAD. LUAD cases from The Cancer Genome Atlas (TCGA) (n = 416) and the Kaplan-Meier plotter database (n = 720) were extracted to study the differential expression and prognostic value of HNRNPC. HNRNPC expression in the National Cancer Center of China (NCC) cohort was analyzed by immunohistochemical staining, and the relationship between HNRNPC expression and survival rate evaluated using the Kaplan-Meier method and log-rank test. Univariate and multivariate Cox regression analyses were used to identify independent prognostic factors. Several pathways that were significantly enriched in the HNRNPC high expression group were identified by Gene Set Enrichment Analysis (GSEA).ResultsFive data sets from the Oncomine and GEPIA databases all supported that HNRNPC expression is significantly higher in LUAD than in normal lung tissue. In TCGA cohort, HNRNPC was highly expressed in LUAD tissues and significantly related to age, sex, smoking history, ethnicity, lymph node metastasis, and TNM staging (P < 0.001). High HNRNPC expression was significantly correlated with poor prognosis in the three cohorts (NCC, TCGA, and K-M plotter) (P < 0.05). Multivariate Cox regression analysis showed that HNRNPC expression was an independent prognostic factor in both TCGA and NCC cohorts (P < 0.05). Further, 10 significantly enriched pathways were identified from TCGA data and 118 lung cancer cell lines in CCLE, respectively.ConclusionsHigh HNRNPC expression is significantly related to poor overall survival in patients with LUAD, suggesting that HNRNPC may be a cancer-promoting factor and a potential prognostic biomarker in LUAD.

Abnormal metabolism is one of the hallmarks of cancer cells. Monoacylglycerol lipase (MGLL), a key enzyme in lipid metabolism, has emerged as an important regulator of tumor progression. In this study, we aimed to characterize the role of MGLL in the development of lung adenocarcinoma (LUAD). To this end, we used tissue microarrays to evaluate the expression of MGLL in LUAD tissue and assessed whether the levels of this protein are correlated with clinicopathological characteristics of LUAD. We found that the expression of MGLL is higher in LUAD samples than that in adjacent non-tumor tissues. In addition, elevated MGLL expression was found to be associated with advanced tumor progression and poor prognosis in LUAD patients. Functional studies further demonstrated that stable short hairpin RNA (shRNA)-mediated knockdown of MGLL inhibits tumor proliferation and metastasis, both in vitro and in vivo, and mechanistically, our data indicate that MGLL regulates Cyclin D1 and Cyclin B1 in LUAD cells. Moreover, we found that knockdown of MGLL suppresses the expression of matrix metalloproteinase 14 (MMP14) in A549 and H322 cells, and in clinical samples, expression of MMP14 is significantly correlated with MGLL expression. Taken together, our results indicate that MGLL plays an oncogenic role in LUAD progression and metastasis and may serve as a potential biomarker for disease prognosis and as a target for the development of personalized therapies.

Abstract 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.

Background and objective Simultaneity multiple primary lung cancer always is a rare disease, but in recent years due to the progress of the diagnosis and treatment means detection rate increased. This study summarized the clinical data of 31 cases of synchronous multiple primary lung cancer (sMPLC) analysis, further to explore the diagnosis, treatment and prognosis of MPLC. Methods Sum up the clinical data of 31 cases of simultaneous multiple primary lung cancer, the diagnostic method, surgical methods, pathology, were retrospectively analyzed. Results All the patients are thoracoscope surgery, no deaths. Thin high resolution computed tomography (CT) in preoperative found multiple lung nodules. Lesions located in the same side of the same period surgical treatment, the operation method is given priority to with under the thoracoscope lung + the lobectomy; Lesions located in bilateral, all staged operation, the time interval for 3 months-4 months. Conclusion Thin layer distinguish CT preoperative diagnosis is the best way to simultaneous multiple primary lung cancer. Multiple primary lung cancer incidence of ipsilateral lung at the same time higher than that of bilateral lung (23:8), type, around 94%, the most common histology to adenocarcinoma, 80.6% (25/31). Primary lesions under thoracoscope lobectomy plus the lobectomy of secondary lesions is the most commonly used.

Background and objective Surgeons are the direct decision-makers and performers in the surgical treatment of patients with lung cancer. Whether the differences among doctors affect the survival of patients is unclear. This study analyzed the five-year survival rates of different thoracic surgeries in patients undergoing surgery to assess the physician's impact and impact. Methods A retrospective analysis of five years between 2002-2007 in the Department of Thoracic Surgery, Cancer Hospital, Chinese Academy of Medical Sciences, for surgical treatment of lung cancer patients. According to different surgeons grouping doctors to compare the basic information of patients, surgical methods, short-term results and long-term survival differences. Results A total of 712 patients treated by 11 experienced thoracic surgeons were included in this study. The patients have nosignificant difference with gender, age, smoking, pathological type between groups. There were significant differences in clinical staging, surgery type, operation time, blood transfusion rate, number of lymph node dissection, palliative resection rate, postoperative complications and perioperative mortality. There was a significant difference in five-year survival rates among patients treated by different doctors. This difference can be seen in all clinical stage analyzes with consistency. In the multivariate analysis, it was suggested that surgeon was an independent factor influencing the prognosis of patients. Conclusion Thoracic surgeon has a significant effect on the therapeutic effect of lung cancer patients.

Precision medicine is to developing the most appropriate individualized treatment for each patient based on the macro to the micro level of individual differences. Genomic, proteomics, metabolomics data, and other big data analysis methods are the essence of precision medicine. Precision medicine brings the hope to overcome cancer. Among all kinds of tumors, lung cancer is the biggest threat to human. This paper reviewed the development of precision medicine in the surgical treatment of lung cancer.