A risk score based on polyamine metabolism and chemotherapy-related genes predicts prognosis and immune cells infiltration of lung adenocarcinoma.

We aimed to explore whether the genes associated with both platinum-based therapy and polyamine metabolism could predict the prognosis of LUAD. We searched for the differential expression genes (DEGs) associated with platinum-based therapy, then we interacted them with polyamine metabolism-related genes to obtain hub genes. Subsequently, we analysed the main immune cell populations in LUAD using the scRNA-seq data, and evaluated the activity of polyamine metabolism of different cell subpopulations. The DEGs between high and low activity groups were screened to identify key DEGs to establish prognostic risk score model. We further elucidated the landscape of immune cells, mutation and drug sensitivity analysis in different risk groups. Finally, we got 10 hub genes associated with both platinum-based chemotherapy and polyamine metabolism, and found that these hub genes mainly affected signalling transduction pathways. B cells and mast cells with highest polyamine metabolism activity, while NK cells were found with lowest polyamine metabolism activity based on scRNA-seq data. DEGs between high and low polyamine metabolism activity groups were identified, then 6 key genes were screened out to build risk score, which showed a good predictive power. The risk score showed a universal negative correlation with immunotherapy checkpoint genes and the cytotoxic T cells infiltration. The mutation rates of EGFR in low-risk group was significantly higher than that of high-risk group. In conclusion, we developed a risk score based on key genes associated with platinum-based therapy and polyamine metabolism, which provide a new perspective for prognosis prediction of LUAD.

miR-185-5p May Modulate the Chemosensitivity of LUSC to Cisplatin via Targeting PCDHA11: Multi-omics Analysis and Experimental Validation.

Drug resistance is the major difficulty in treatment of lung squamous cell carcinoma (LUSC). This study aims to explore drug response-related miRNAs (DRmiRNAs) based on multi-omics research. We identified DRmiRNAs of LUSC with a multi-omics integrated system that combines expression data of microRNA, lncRNA, mRNA, methylation levels, somatic mutations. After identifying DRmiRNAs, we screened and validated of the target mRNAs of DRmiRNAs through Targetscan and the miRDB database. Then, Real-time PCR and Western blot assays were used to estimate the expression of DRmiRNAs and target protein, and the dual-luciferase assays were used to confirm the interaction of DRmiRNAs and target mRNA. Furthermore, CCK-8 (Cell Counting Kit-8) assays were used to evaluate cell proliferation and drug sensitivity. After integrated analysis, hsa-miR-185-5p was identified as DRmiRNA based on multi-omics data. Through Targetscan and miRDB database, the possible target mRNAs were obtained and PCDHA11 was validated as a target mRNA of miR-185-5p by real-time PCR, Western blot assays and dual-luciferase assays. CCK-8 assays and clone formation assays showed that the proliferation of miR-185-5p mimics was significantly slower than that of miR-185-5p inhibitors, which means overexpression of miR-185-5p enhanced the anticancer effects of cisplatin, whereas the downregulation of miR-185-5p reduced the effects. Furthermore, the proliferation of silencing PCDHA11 was significantly slower than that of overexpression of PCDHA11, which means PCDHA11 overexpression weakened the anticancer effects of cisplatin, and silencing PCDHA11 enhanced the effects. This study demonstrated that miR-185-5p was involved in chemoresistance of LUSC cells to cisplatin partly via down-regulating PCDHA11, which may promote understanding the underlying molecular mechanisms of drug response.

FAK-LINC01089 negative regulatory loop controls chemoresistance and progression of small cell lung cancer.

The focal adhesion kinase (FAK) tyrosine kinase is activated and upregulated in multiple cancer types including small cell lung cancer (SCLC). However, FAK inhibitors have shown limited efficacy in clinical trials for cancer treatment. With the aim of identifying potential therapeutic strategies to inhibit FAK for cancer treatment, we investigated long non-coding RNAs (lncRNAs) that potentially regulate FAK in SCLC. In this study, we identified a long non-coding RNA LINC01089 that binds and inhibits FAK phosphorylation (activation). Expression analysis revealed that LINC01089 was downregulated in SCLC tissues and negatively correlated with chemoresistance and survival in SCLC patients. Functionally, LINC01089 inhibited chemoresistance and progression of SCLC in vitro and in vivo. Mechanistically, LINC01089 inhibits FAK activation by blocking binding with Src and talin kinases, while FAK negatively regulates LINC01089 transcription by activating the ERK signaling pathway to recruit the REST transcription factor. Furthermore, LINC01089-FAK axis mediates the expression of drug resist-related genes by modulating YBX1 phosphorylation, leading to drug resistance in SCLC. Intriguingly, the FAK-LINC01089 interaction depends on the co-occurrence of the novel FAK variant and the non-conserved region of LINC01089 in primates. In Conclusion, our results indicated that LINC01089 may serve as a novel high-efficiency FAK inhibitor and the FAK-LINC01089 axis represents a valuable prognostic biomarker and potential therapeutic target in SCLC.

FSTL3 is associated with prognosis and immune cell infiltration in lung adenocarcinoma.

PURPOSE: FSTL3 expression is altered in various types of cancer. However, the role and mechanism of action of FSTL3 in lung adenocarcinoma development and tumor immunity are unknown. We investigated the association between FSTL3 expression and clinical characteristics and immune cell infiltration in lung adenocarcinoma samples from The Cancer Genome Atlas (TCGA) and a separate validation set from our hospital. METHODS: Data on immune system infiltration, gene expression, and relevant clinical information were obtained by analyzing lung adenocarcinoma sample data from TCGA database. Using online tools like GEPIA, the correlations between FSTL3 expression and prognosis, clinical stage, survival status, and tumor-infiltrating immune cells were examined. In a validation dataset, immunohistochemistry was performed to analyze FSTL3 expression and its related clinical characteristics. RESULTS: FSTL3 expression was markedly reduced in patients with lung adenocarcinoma. N stage, pathological stage, and overall survival were significantly correlated with FSTL3 expression. According to GSEA, FSTL3 is strongly linked to signaling pathways such as DNA replication and those involved in cell cycle regulation. Examination of TCGA database and TIMER online revealed a correlation between FSTL3 and B cell, T cell, NK cell, and neutrophil levels. The prognosis of patients with lung adenocarcinoma was significantly affected by six genes (KRT6A, VEGFC, KRT14, KRT17, SNORA12, and KRT81) related to FSTL3. CONCLUSION: FSTL3 is significantly associated with the prognosis and progression of lung adenocarcinoma and the infiltration of immune cells. Thus, targeting FSTL3 and its associated genes in immunotherapy could be potentially beneficial for the treatment of lung adenocarcinoma.

A risk score model based on lipid metabolism-related genes could predict response to immunotherapy and prognosis of lung adenocarcinoma: a multi-dataset study and cytological validation.

BACKGROUND: Lipid metabolism is a key factor in tumorigenesis and drug resistance, and models related to lipid metabolism have shown potential to predict survival and curative effects of adjuvant therapy in various cancers. However, the relationship between lipid metabolism and prognosis and treatment response of lung adenocarcinoma (LUAD) are still unclear. METHODS: We enrolled seven bulk RNA-sequence datasets (GSE37745, GSE19188, GSE30219, GSE31547, GSE41271, GSE42127, and GSE72094) from the GEO database and one single-cell RNA-sequencing dataset (GSE117570) from the TISCH2 database. Non-negative matrix factorization (NMF) was utilized to construct the risk score model based on lipid score calculated by GSVA algorithm. Phs000452.v3, PMID: 26359337, PMID: 32472114, PRJEB23709 datasets were used to test the response to immunotherapy. Drug sensitivity analysis was assessed according to the GDSC database, and immunotherapy response was evaluated using the Wilcoxon test. Cellular function assays including clone formation, EDU assays and flow cytometry were implemented to explore the phenotype alteration caused by the knockdown of PTDSS1, which is one of key gene in risk score model. RESULTS: We analyzed both bulk and single-cell RNA sequencing data to establish and validate a risk score model based on 18 lipid metabolism-related genes with significant impact on prognosis. After divided the patients into two groups according to risk score, we identified differences in lipid-related metabolic processes and a detailed portrait of the immune landscapes of high- and low-risk groups. Moreover, we investigated the potentials of our risk score in predicting response to immunotherapy and drug sensitivity. In addition, we silenced PTDSS1 in LUAD cell lines, and found that the proliferation of the cells was weakened, and the apoptosis of the cells was increased. CONCLUSION: Our study highlights the crucial roles of lipid metabolism in LUAD and provides a reliable risk score model, which can aid in predicting prognosis and response to immunotherapy. Furthermore, we investigated the roles of PTDSS1 in LUAD carcinogenesis, which showed that PTDSS1 regulated proliferation and apoptosis of LUAD cells.

Prognostic and immune correlation analysis of mitochondrial autophagy and aging-related genes in lung adenocarcinoma.

PURPOSE: Mitophagy and aging (MiAg) are very important pathophysiological mechanisms contributing to tumorigenesis. MiAg-related genes have prognostic value in lung adenocarcinoma (LUAD). However, prognostic, and immune correlation studies of MiAg-related genes in LUAD are lacking. METHODS: MiAg differentially expressed genes (DEGs) in LUAD were obtained from public sequencing datasets. A prognostic model including MiAg DEGs was constructed according to patients divided into low- and high-risk groups. Gene Ontology, gene set enrichment analysis, gene set variation analysis, CIBERSORT immune infiltration analysis, and clinical characteristic correlation analyses were performed for functional annotation and correlation of MiAgs with prognosis in patients with LUAD. RESULTS: Seven MiAg DEGs of LUAD were identified: CAV1, DSG2, DSP, MYH11, NME1, PAICS, PLOD2, and the expression levels of these genes were significantly correlated (P < 0.05). The RiskScore of the MiAg DEG prognostic model demonstrated high predictive ability of overall survival of patients diagnosed with LUAD. Patients with high and low MiAg phenotypic scores exhibited significant differences in the infiltration levels of eight types of immune cells (P < 0.05). The multi-factor DEG regression model showed higher efficacy in predicting 5-year survival than 3- and 1-year survival of patients with LUAD. CONCLUSIONS: Seven MiAg-related genes were identified to be significantly associated with the prognosis of patients diagnosed with LUAD. Moreover, the identified MiAg DEGs might affect the immunotherapy strategy of patients with LUAD.

A tricarboxylic acid cycle-based machine learning model to select effective drug targets for the treatment of esophageal squamous cell carcinoma.

Background: The tricarboxylic acid cycle (TCA cycle) is an important metabolic pathway and closely related to tumor development. However, its role in the development of esophageal squamous cell carcinoma (ESCC) has not been fully investigated. Methods: The RNA expression profiles of ESCC samples were retrieved from the TCGA database, and the GSE53624 dataset was additionally downloaded from the GEO database as the validation cohort. Furthermore, the single cell sequencing dataset GSE160269 was downloaded. TCA cycle-related genes were obtained from the MSigDB database. A risk score model for ESCC based on the key genes of the TCA cycle was built, and its predictive performance was evaluated. The association of the model with immune infiltration and chemoresistance were analyzed using the TIMER database, the R package "oncoPredict" score, TIDE score and so on. Finally, the role of the key gene CTTN was validated through gene knockdown and functional assays. Results: A total of 38 clusters of 8 cell types were identified using the single-cell sequencing data. The cells were divided into two groups according to the TCA cycle score, and 617 genes were identified that were most likely to influence the TCA cycle. By intersecting 976 key genes of the TCA cycle with the results of WGCNA, 57 genes significantly associated with the TCA cycle were further identified, of which 8 were screened through Cox regression and Lasso regression to construct the risk score model. The risk score was a good predictor of prognosis across subgroups of age, N, M classification and TNM stage. Furthermore, BI-2536, camptothecin and NU7441 were identified as possible drug candidates in the high-risk group. The high-risk score was associated with decreased immune infiltration in ESCC, and the low-risk group had better immunogenicity. In addition, we also evaluated the relationship between risk scores and immunotherapy response rates. Functional assays showed that CTTN may affect the proliferation and invasion of ESCC cells through the EMT pathway. Conclusion: We constructed a predictive model for ESCC based on TCA cycle-associated genes, which achieved good prognostic stratification. The model are likely associated with the regulation of tumor immunity in ESCC.

Outcomes and prognosis of non-small cell lung cancer patients who underwent curable surgery: a protocol for a real-world, retrospective, population-based and nationwide Chinese National Lung Cancer Cohort (CNLCC) study.

INTRODUCTION: Surgery is one of the main approaches for the comprehensive treatment of early and locally advanced non-small cell lung cancer (NSCLC). This study conducts a nationwide multicentre study to explore factors that could influence the outcomes of patients with I-IIIA NSCLC who underwent curable surgery in real-world scenarios. METHODS AND ANALYSIS: All patients diagnosed with NSCLC between January 2013 and December 2020 will be identified from 30 large public medical services centres in mainland China. The algorithm of natural language processing and artificial intelligence techniques were used to extract data from electronic health records of enrolled patients who fulfil the inclusion criteria. Six categories of parameters are collected and stored from the electronic records, then the parameters will be structured as a high-quality structured case report form. The code book will be compiled and each parameter will be classified and designated a code. In addition, the study retrieves the survival status and causes of death of patients from the Chinese Centre for Disease Control and Prevention. The primary endpoints are overall survival and the secondary endpoint is disease-free survival. Finally, an online platform is formed for data queries and the original records will be stored as secure electronic documents. ETHICS AND DISSEMINATION: The study has been approved by the Ethical Committee of the Chinese Academy of Medical Sciences. Study findings will be disseminated via presentations at conferences and publications in open-access journals. This study has been registered in the Chinese Trial Register (ChiCTR2100052773) on 11 May 2021, http://www.chictr.org.cn/showproj.aspx?proj=136659. TRIAL REGISTRATION NUMBER: ChiCTR2100052773.

The c-Src/LIST Positive Feedback Loop Sustains Tumor Progression and Chemoresistance.

Chemotherapy resistance and treatment failure hinder clinical cancer treatment. Src, the first mammalian proto-oncogene to be discovered, is a valuable anti-cancer therapeutic target. Although several c-Src inhibitors have reached the clinical stage, drug resistance remains a challenge during treatment. Herein, a positive feedback loop between a previously uncharacterized long non-coding RNA (lncRNA), which the authors renamed lncRNA-inducing c-Src tumor-promoting function (LIST), and c-Src is uncovered. LIST directly binds to and regulates the Y530 phosphorylation activity of c-Src. As a c-Src agonist, LIST promotes tumor chemoresistance and progression in vitro and in vivo in multiple cancer types. c-Src can positively regulate LIST transcription by activating the NF-κB signaling pathway and then recruiting the P65 transcription factor to the LIST promoter. Interestingly, the LIST/c-Src interaction is associated with evolutionary new variations of c-Src. It is proposed that the human-specific LIST/c-Src axis renders an extra layer of control over c-Src activity. Additionally, the LIST/c-Src axis is of high physiological relevance in cancer and may be a valuable prognostic biomarker and potential therapeutic target.

Plasma metabolomics study in screening and differential diagnosis of multiple primary lung cancer.

BACKGROUND: Multiple primary lung cancer (MPLC) is becoming increasingly common in clinical practice. Imaging examination is sometimes difficult to differentiate from intrapulmonary metastasis (IM) or single primary lung cancer (SPLC) before surgery. There is a lack of effective blood biomarkers as an auxiliary diagnostic method. PARTICIPANTS AND METHODS: A total of 179 patients who were hospitalized and operated in our department from January to June 2019 were collected, and they were divided into SPLC with 136 patients, MPLC with 24 patients, and IM with 19 patients. In total, 96 healthy people without lung cancer were enrolled. Medical history, imaging, and pathology data were assembled from all participants. Plasma metabolomics analysis was performed by quadrupole time-of-flight tandem mass spectrometry, and data were analyzed using SPSS19.0/Simca 14.1/MetaboAnalyst5.0 software. Significant metabolites were selected by variable importance in projection, P value, and fold change. The area under the receiver operating characteristic curve was used to evaluate their diagnostic ability. RESULTS: There were significant differences in plasma metabolite profiles between IM and MPLC. Seven metabolites were screened out. Two metabolites had higher levels in IM, and five metabolites had higher levels in MPLC. All had favorable discriminating capacity. Phosphatidyl ethanolamine (38:5) showed the highest sensitivity (0.95) and specificity (0.92). It was followed by l -histidine with sensitivity 0.92 and specificity 0.84. l -tyrosine can be used to identify SPLC and MPLC. The panel composed of related metabolites exhibited higher diagnostic ability. Eight principal metabolites caused remarkable differences between healthy people and MPLC, and five of them had area under the curves greater than 0.85, showing good discriminating power. CONCLUSION: Through the study of plasma metabolomics, it was found that there were obvious differences in the metabolite profiles of MPLC, IM, SPLC, and the healthy population. Some discovered metabolites possessed excellent diagnostic competence with high sensitivity and specificity. They had the potential to act as biomarkers for the screening and differential diagnosis of MPLCs.

Efficacy, Safety, and Biomarker Analysis of Neoadjuvant Camrelizumab and Apatinib in Patients With Resectable NSCLC: A Phase 2 Clinical Trial.

INTRODUCTION: Camrelizumab (an anti-programmed cell death protein-1 antibody) combined with apatinib (an antiangiogenic agent) has conferred benefits for advanced NSCLC. We aimed to assess the activity and safety of neoadjuvant camrelizumab plus apatinib in patients with resectable NSCLC. METHODS: In this phase 2 trial, patients with histologically confirmed resectable stages IIA to IIIB NSCLC (stage IIIB, T3N2 only) received intravenous camrelizumab (200 mg) every 2 weeks for three cycles and oral apatinib (250 mg) once daily for 5 days followed by 2 days off for 6 weeks. Surgery was planned 3 to 4 weeks after apatinib discontinuation. The primary end point was major pathologic response (MPR) rate, assessed in patients who received at least one dose of neoadjuvant treatment and underwent surgery. RESULTS: Between November 9, 2020, and February 16, 2022, 78 patients were treated and 65 (83%) underwent surgery. All 65 patients achieved an R0 surgical resection. Among the 65 patients, 37 (57%, 95% confidence interval [CI]: 44%-69%) had an MPR, of whom 15 (23%, 95% CI: 14%-35%) had a pathologic complete response (pCR). Pathologic responses observed in squamous cell NSCLC were superior to adenocarcinoma (MPR: 64% versus 25%; pCR: 28% versus 0%). The radiographic objective response rate was 52% (95% CI: 40%-65%). Among all the 78 enrolled patients, 37 (47%, 95% CI: 36%-59%) had an MPR, of whom 15 (19%, 95% CI: 11%-30%) had a pCR. Four (5%) of 78 patients had grade 3 neoadjuvant treatment-related adverse events. No grade 4 or 5 treatment-related adverse events occurred. Receiver operating characteristic analysis revealed a significant correlation between the maximum reduction of standard uptake values and pathologic response (R = 0.619, p < 0.0001). In addition, baseline programmed death-ligand 1 expression, HOXA9 and SEPT9 methylation levels, and circulating tumor DNA status before surgery were associated with pathologic responses. CONCLUSIONS: Neoadjuvant camrelizumab plus apatinib was found to have promising activity and manageable toxicity in patients with resectable stages IIA to IIIB NSCLC, which might be a potential therapeutic option in neoadjuvant setting.

Enhanced CT-based radiomics model to predict natural killer cell infiltration and clinical prognosis in non-small cell lung cancer.

Background: Natural killer (NK) cells are crucial for tumor prognosis; however, their role in non-small-cell lung cancer (NSCLC) remains unclear. The current detection methods for NSCLC are inefficient and costly. Therefore, radiomics represent a promising alternative. Methods: We analyzed the radiogenomics datasets to extract clinical, radiological, and transcriptome data. The effect of NK cells on the prognosis of NSCLC was assessed. Tumors were delineated using a 3D Slicer, and features were extracted using pyradiomics. A radiomics model was developed and validated using five-fold cross-validation. A nomogram model was constructed using the selected clinical variables and a radiomic score (RS). The CIBERSORTx database and gene set enrichment analysis were used to explore the correlations of NK cell infiltration and molecular mechanisms. Results: Higher infiltration of NK cells was correlated with better overall survival (OS) (P = 0.002). The radiomic model showed an area under the curve of 0.731, with 0.726 post-validation. The RS differed significantly between high and low infiltration of NK cells (P < 0.01). The nomogram, using RS and clinical variables, effectively predicted 3-year OS. NK cell infiltration was correlated with the ICOS and BTLA genes (P < 0.001) and macrophage M0/M2 levels. The key pathways included TNF-α signaling via NF-κB and Wnt/ß-catenin signaling. Conclusions: Our radiomic model accurately predicted NK cell infiltration in NSCLC. Combined with clinical characteristics, it can predict the prognosis of patients with NSCLC. Bioinformatic analysis revealed the gene expression and pathways underlying NK cell infiltration in NSCLC.

RASAL3 predicts overall survival and CD8+ T lymphocyte infiltration in lung adenocarcinoma.

RAS-activating protein-like 3 (RASAL3) is a synaptic Ras GTPase-activating protein (SynGAP) and a potential novel biomarker of CD8+ T cell infiltration in lung adenocarcinoma (LUAD). This study explored RASAL3 expression in LUAD, the prognostic impact of RASAL3 and the relationship with immune cell infiltration. RASAL3 expression in LUAD tissues was considerably low, with high RASAL3 expression associated with better overall survival, whereas the low expression was linked to advanced T, N, M classifications, TNM stage and lower grade. Furthermore, RASAL3 expression positively correlated with CD8+ T lymphocyte infiltration. In conclusion, RASAL3 expression is a potential prognostic and immunological biomarker of LUAD.

Clinical characteristics and prognostic factors of pulmonary sarcomatoid carcinoma.

Background: Pulmonary sarcomatoid carcinoma (PSC) is a kind of rare lung cancer. We aim to analyze the clinical characteristics and prognostic factors of patients with PSC. Methods: From January 1, 2006 to December 31, 2015, 119 patients in the Cancer Hospital Chinese Academy of Medical Sciences were diagnosed with PSC, and they received treatment. We retrospectively collected information on gender, age, body mass index (BMI), symptoms, family history, smoking history, tumor size, tumor location, tumor diameter, tumor-node-metastasis (TNM), pathological type, and other factors to analyze the relationship between these factors and 1-, 3-, 5-year, and overall survival (OS). Results: Male patients who had a smoking history (n=76) comprised the main group of PSC. Median patient age was 60.67±10.50 years (range, 26-89 years). The majority of these patients (n=82) presented with respiratory symptoms. The median survival of patients who died of PSC was 11.87 months (6.38-21.48 months). The 1-, 3-, and 5-year survival rates were 61.3%, 34.5%, and 31.9%, respectively. Patients with a lower T stage and without lymph node metastasis and distant metastasis had a better OS (P<0.05). Other clinical characteristics and the difference in treatments did not influence the prognosis significantly (P>0.05). Conclusions: PSC is a rare malignant neoplasm of the lung with poor prognosis. Surgery is a major therapeutic method for this disease entity. TNM-stage is the main factors affecting prognosis.

A Single Center Analysis of Thymic Neuroendocrine Tumors.

PURPOSE: Thymic neuroendocrine tumors (TNETs) are a collection of slow-progressing neoplasms located in the anterior mediastinum. Relatively few previously published studies have focused on thymic carcinomas. This study investigated the basic clinical characteristics, treatment, and prognosis of TNETs. METHODS: Patients were enrolled in the study from January 2003 to December 2017 who had been diagnosed with TNETs through pathological screening and treated at our institution. Demographic data from each patient, the Masaoka stage, histology and size of the tumor, tumor invasion characteristics, and therapeutic strategies were gathered. The Kaplan-Meier method was used to assess patient survival. In addition, the log-rank test was used to carry out univariate analyses. RESULTS: Twenty-six patients were eligible for inclusion in the study. The median age of the patients was 46.5 (25-69) years. The tumor median maximum diameter was 7.9 cm (from 3 to 19 cm). Twenty-four patients were treated surgically. Nineteen patients completed radiation therapy, and sixteen patients underwent chemotherapy. A median follow-up time of 54.95 months was observed. The survival rate for three years was 75.0% and 70.6% for five years. The corresponding progression-free survival rates for three and five years were 55.7% and 37.7%, respectively. The local, regional recurrence-free survival (LRFS) rates were 87.2% and 81.7%, and the distant metastasis-free survival (DMFS) rates were 55.7% and 37.7%, at three and five years, respectively. Local recurrence (six patients) and bone metastasis (six patients) were observed as the most frequent failures. CONCLUSION: TNET was observed to be an aggressive but rare malignant lesion. While the predominant treatment was complete resection, chemotherapy and radiotherapy were also required due to the high recurrence rate.

Plasm Metabolomics Study in Pulmonary Metastatic Carcinoma.

Background: The lung is one of the most common metastatic sites of malignant tumors. Early detection of pulmonary metastatic carcinoma can effectively reduce relative cancer mortality. Human metabolomics is a qualitative and quantitative study of low-molecular metabolites in the body. By studying the plasm metabolomics of patients with pulmonary metastatic carcinoma or other lung diseases, we can find the difference in plasm levels of low-molecular metabolites among them. These metabolites have the potential to become biomarkers of lung metastases. Methods: Patients with pulmonary nodules admitted to our department from February 1, 2019, to May 31, 2019, were collected. According to the postoperative pathological results, they were divided into three groups: pulmonary metastatic carcinoma (PMC), benign pulmonary nodules (BPN), and primary lung cancer (PLC). Moreover, healthy people who underwent physical examination were enrolled as the healthy population group (HPG) during the same period. On the one hand, to study lung metastases screening in healthy people, PMC was compared with HPG. The multivariate statistical analysis method was used to find the significant low-molecular metabolites between the two groups, and their discriminating ability was verified by the ROC curve. On the other hand, from the perspective of differential diagnosis of lung metastases, three groups with different pulmonary lesions (PMC, BPN, and PLC) were compared as a whole, and then the other two groups were compared with PMC, respectively. The main low-molecular metabolites were selected, and their discriminating ability was verified. Results: In terms of lung metastases screening for healthy people, four significant low-molecular metabolites were found by comparison of PMC and HPG. They were O-arachidonoyl ethanolamine, adrenoyl ethanolamide, tricin 7-diglucuronoside, and p-coumaroyl vitisin A. In terms of the differential diagnosis of pulmonary nodules, the significant low-molecular metabolites selected by the comparison of the three groups as a whole were anabasine, octanoylcarnitine, 2-methoxyestrone, retinol, decanoylcarnitine, calcitroic acid, glycogen, and austalide L. For the comparison of PMC and BPN, L-tyrosine, indoleacrylic acid, and lysoPC (16 : 0) were selected, while L-octanoylcarnitine, retinol, and decanoylcarnitine were selected for the comparison of PMC and PLC. Their AUCs of ROC are all greater than 0.80. It indicates that these substances have a strong ability to differentiate between pulmonary metastatic carcinoma and other pulmonary nodule lesions. Conclusion: Through the research of plasm metabolomics, it is possible to effectively detect the changes in some low-molecular metabolites among primary lung cancer, pulmonary metastatic carcinoma, and benign pulmonary nodule patients and healthy people. These significant metabolites have the potential to be biomarkers for screening and differential diagnosis of lung metastases.

External validation of the eighth edition of the TNM classification for lung cancer in small cell lung cancer.

OBJECTIVES: The newly released eighth edition of the American Joint Committee on Cancer TNM staging system for lung cancer seeks to improve prognostic accuracy but lacks external validation for small cell lung cancer (SCLC). Moreover, previous studies posed a few questions concerning survival differences for patients with specific site N3 node involvement or single-site metastasis (SSM) in different distant organs. The aim of this study was to validate the eighth edition of the TNM classification for SCLC in an independent multi-institutional cohort from China and answer the questions raised by the previous research. METHODS: Patients with SCLC from four Chinese cancer centers between 2009 and 2019 were reclassified according to the seventh and eighth edition of the TNM classification. Survival was estimated using the Kaplan-Meier method. Comparisons between adjacent categories and stage groups were performed using Cox proportional hazard regression. R2 statistics were calculated to evaluate the discriminating performance of editions. RESULTS: Of 3384 enrolled cases, 3358 had clinical stage, 537 had pathological stage, and 511 had both. Progressive deterioration of survival was observed with advancing of TNM categories and stages both in the seventh and the eighth edition. The eighth edition stages had a higher R2 statistic than the seventh edition (0.207 versus 0.197). Newly defined categories M1b and M1c and stages IIIC, IVA and IVB in the eighth edition discriminated groups with significantly different prognosis. Patients with N3 contralateral supraclavicular nodes had a significantly worse prognosis than those without (p = 0.032). For patients with single-site metastasis, liver involvement showed a worse prognosis compared to brain involvement (p = 0.030). CONCLUSIONS: Our study provided an external validation of the eighth edition of the TNM classification for lung cancer in Chinese patients with SCLC, and confirmed its improved prognostic accuracy compared with the seventh edition. Patients with N3 and M1b might represent heterogeneous populations that warrant further research.