Despite its rapid growth and early metastasis, small cell lung cancer (SCLC) is more chemosensitive than other lung cancers. However, some patients with extensive-stage SCLC (ES-SCLC) do not respond to first-line chemotherapy, resulting in poorer prognoses due to inter- and intratumoral heterogeneity. In this study, we conducted single-cell RNA sequencing of 9 treatment-naive ES-SCLC samples. Based on comprehensive imaging evidence collected before and after two cycles of first-line chemotherapy and sample types, the 9 samples were categorized into three groups: progressive disease with the pleural effusion sample (PD_PE group, n = 1), progressive disease with the primary tumor samples (PD_TU group, n = 2), and partial response with the primary tumor samples (PR_TU group, n = 6). Based on transcriptomic landscape and cell type composition, the PD samples represent a multicellular ecosystem distinct from PR samples. The immune response, along with the elevated expression of immune-related genes such as LTF, SLPI, SPARC and IGLV1-51, might correlate with a poor first-line chemotherapy response in ES-SCLC. We also observed that T cells, particularly effector T cells, were more abundant in PD_TU group, with TNFA signaling via NFκB being significantly enriched. The PD_TU group was strongly enriched with macrophages and tumor-associated macrophages (TAMs), and angiogenesis in TAMs was highly enriched. Immunomodulatory fibroblasts were highly abundant in PD_TU group, and the pathways of epithelial-mesenchymal transition and angiogenesis were upregulated. This study offers the first comprehensive insights into the cellular and molecular heterogeneity in treatment-naive patients with ES-SCLC with different chemotherapy responses.

BACKGROUND:Lung cancer is a prevalent form of cancer worldwide. A possible link between lung cancer and chronic obstructive pulmonary disease (COPD) has been suggested by recent studies. The objective of our research was to analyze the mRNA expression patterns in both situations, with a specific emphasis on their biological functions and the pathways they are linked to. METHOD:Data on COPD mRNA expression was collected from the NCBI-GEO database, while information regarding lung cancer mRNA was acquired from The Cancer Genome Atlas database. To examine the association of COPD-related scores in lung cancer patients, we utilized the ssGSEA algorithm for single sample gene set enrichment analysis. The possible routes were examined through the utilization of Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis. Risk models were developed using Cox and least absolute shrinkage and selection operator (LASSO) regression analyses. Moreover, a GSEA was performed to investigate significant pathways among various risk groups. RESULT:After identifying 17 genes that were differentially expressed and linked to COPD, we found that they met the criteria of having a false discovery rate < 0.05 and an absolute log2 fold change > 0.585. By utilizing the ssGSEA algorithm, it became possible to classify individuals with lung cancer into two distinct groups based on their COPD status. Consequently, a seven-gene risk model was developed specifically for these patients. The risk score was determined by applying the given formula: risk score = AC022784.1 × 0.0423737993775888 + CRISP3 × 0.0415322046890524 + MELTF × 0.0661848418476596 + MT2P1 × 0.111843227536117 + FAM83A-AS1 × 0.045295939710361 + ZNF506 × -0.309489953363417 + ITGA6 × 0.01813978449589. The risk model associated with COPD showed a notable connection with different immune cells found in the lung cancer sample, including macrophages of M0/M1/M2 types, hematopoietic stem cells, mast cells, NK T cells and regulatory T cells. Overexpression of crucial genes was seen to enhance cell proliferation and invasive potential in the lung cancer sample. In the lung cancer sample, it was observed that an increase in ZNF506 expression enhanced both cell proliferation and invasion. CONCLUSION:In conclusion, this study effectively examines the potential correlation between COPD and lung cancer. A prognostic model based on seven COPD-associated genes demonstrated robust predictive potential in the lung cancer sample. Our analysis offers comprehensive insights for lung cancer patients.

Brain metastases (BM) of lung adenocarcinoma (LUAD) are the most common intracranial malignancy leading to death. However, the cellular origins and drivers of BM from LUAD have not been clarified. Cellular composition was characterized by single-cell sequencing analysis of primary lung adenocarcinoma (pLUAD), BM and lymph node metastasis (LNM) samples in GSE131907. Our study briefly analyzed the tumor microenvironment (TME), focusing on the role of epithelial cells (ECs) in BM. We have discovered a population of brain metastasis-associated epithelial cells (BMAECs) expressing SPP1, SAA1, and CDKN2A, and it has been observed that this population is mainly composed of aneuploid cells from pLUAD, playing a crucial role in brain metastasis. Our study concluded that both LNM and BM in LUAD originated from pLUAD lesions, but there is currently insufficient evidence to prove a direct association between BM lesions and LNM lesions, which provides inspiration for further investigation of the TME in BM.

Background Chronic obstructive pulmonary disease (COPD) is closely linked to lung cancer (LC) development. The aim of this study is to identify the genetic and clinical risk factors for LC risk in COPD, according to which the prediction model for LC in COPD was constructed. Methods This is a case-control study in which patientis with COPD + LC as the case group, patientis with only COPD as the control group, and patientis with only LC as the second control group. A panel of clinical variables including demographic, environmental and lifestyle factors were collected. A total of 20 single nucleotide polymorphisms (SNPs) were genotyped. The univariate analysis, candidate gene study and multivariate analysis were applied to identify the independent risk factors, as well as the prediction model was constructed. The ROC analyses were used to evaluate the predictive ability of the model. Results A total of 503 patients were finally enrolled in this study, with 188 patients for COPD + LC group, 162 patients for COPD group and 153 patients for LC group. The univariate analysis of clincial data showed compared with the patients with COPD, the patients with COPD + LC tended to have significantly lower BMI, higher smoking pack-years, and higher prevalence of emphysema. The results of the candidate gene study showed the rs1489759 in HHIP and rs56113850 in CYP2A6 demonstrated significant differences between COPD and COPD + LC groups. By using multivariate logistic regression analysis, four variables including BMI, pack-years, emphysema and rs56113850 were identified as independent risk factors for LC in COPD and the prediction model integrating genetic and clinical data was constructed. The AUC of the prediction model for LC in COPD reached 0.712, and the AUC of the model for predicting LC in serious COPD reached up to 0.836. Conclusion The rs56113850 (risk allele C) in CYP2A6, decrease in BMI, increase in pack-years and emphysema presence were independent risk factors for LC in COPD. Integrating genetic and clinical data for predicting LC in COPD demonstrated favorable predictive performance.

In the context of cancer expansion, epithelial-mesenchymal transition (EMT) plays an essential role in driving invasion and metastasis potential of cancer cells. Tumor-associated macrophages (TAMs)-derived factors involved in the initiation and progression of EMT. We assess the role of M2 macrophage in suppressing lung tumors of a natural compound (-)-Guaiol by using macrophage depleted model. Bone marrow-derived monocytes (BMDMs) were extracted and induced to M2-like phenotype in vitro. The co-culture of M2 macrophage and lung cancer cells was established to observe that inhibition of lung tumor growth by (-)-Guaiol requires presence of macrophages. This suppressed effect of (-)-Guaiol was alleviated when mice macrophage was depleted. The expression of M2-like macrophages was strongly reduced by (-)-Guaiol treated mice, but not the changes of M1-like macrophages. In vitro studies, we demonstrated that (-)-Guaiol suppressed M2 polarization of BMDMs, as well as migration, invasion, and EMT of lung cancer cells in co-culture. M2 macrophage-derived interleukin 10 (IL-10) was investigated as a critical signaling molecule between M2 macrophage and lung cancer cells. We have also verified that the mechanism of (-)-Guaiol inhibiting the EMT process of lung cancer is related to the activation of IL-10-mediated signal transducer and activator of transcription 3 (STAT3). These results suggested that the suppressive effect role of (-)-Guaiol in M2 macrophage promoting EMT of lung cancer, which was associated with inhibition of IL-10 mediated STAT3 signaling pathway.

Background: With the rapid spread of COVID-19 worldwide, the clinical practice in cancer treatment of complementary and alternative medicine (CAM) becomes significantly different. This study was designed to explore the changes of China’s cancer patients’ perception, attitude and clinical practice in CAM after the pandemic of COVID-19. Methods: A multicenter before-after cross-sectional study was conducted, including 448 cancer patients by a survey. Results: For perception, half of the cancer patients (51.2%) reported they had received adequate information in CAM cancer treatment and had a great expectation in medical appointment pattern (40.2%). For attitudes, most of the cancer patients (79.4%) had full of confidence in CAM cancer treatment and showed a strong desire to learn more about CAM (70.2%). Nearly half of the cancer patients (44.0%) presented their oncology clinics had been greatly affected by COVID-19, however, most (82.8%) refused to the online medical service pattern. For clinical practice, Chinese herbal medicine was widely used before (89.5%) / after (90.3%) the epidemic of COVID-19 and the main purpose of receiving CAM therapy was to improve immune system (71.8%). The frequency of patients’ visiting the clinic decreased significantly (P < 0.001), and most (90.0%) didn’t change their follow-up pattern. The majority of the cancer patients (83.3%) were identified as CAM users and the only predictive factor was national health insurance in the multivariable logistic regression model. Conclusion: China’s cancer patients show more interest and confidence in treating cancer by CAM, and the new medical pattern such as medical appointment, has been gradually accepted during the COVID-19 pandemic. However, the cancer patients’ perception in CAM and online medical service pattern still need to be improved.

Interferon-gamma (IFN-γ) plays a complex role in modulating tumor microenvironment during lung adenocarcinoma (LUAD) development. In order to define the role of IFN-γ response genes in LUAD progression, we characterized the gene expression, mutation profile, protein-protein interaction of 24 IFN-γ response genes, which exhibited significant hazard ratio in overall survival. Two subgroups of LUAD from the TCGA cohort, which showed significant difference in the survival rate, were identified based on the expression of these genes. Furthermore, LASSO penalized cox regression model was used to derive a risk signature comprising seven IFN-γ response genes, including CD74, CSF2RB, PTPN6, MT2A, NMI, LATS2, and PFKP, which can serve as an independent prognostic predictor of LUAD. The risk signature was validated in an independent LUAD cohort. The high risk group is enriched with genes regulating cell cycle and DNA replication, as well as a high level of pro-tumor immune cells. In addition, the risk score is negatively correlated with the expression of immune metagenes, but positively correlated with DNA damage repair genes. Our findings reveal that seven-gene risk signature can be a valuable prognostic predictor for LUAD, and they are crucial participants in tumor microenvironment of LUAD.

Metabolomics is expected to identify potential metabolites and related pathways, and further reveal the underlying mechanisms of the transition from prediabetes to diabetes. In this study, a metabolomics-based gas chromatography-mass spectrometry (GC-MS) technique was used for demonstrating the serum metabolic profiles among healthy, prediabetes, and diabetes at fasting state and 2h oral glucose tolerance test (2h OGTT) state. With Ingenuity Pathway Analysis (IPA) tool, the comparative analysis showed no significant differences in the pathway analysis (P > 0.05) between prediabetes and diabetes at either fasting state or 2h OGTT state. The self-comparative analysis demonstrated the glycine/glutathione biosynthesis in diabetes were more inhibited than that in prediabetes or healthy control at 2h OGTT state compared with fasting state (P<0.05). In addition, the protein kinase A signaling pathway in prediabetes or diabetes was significantly inhibited more than that in healthy control (P<0.05). Therefore, the glycine/glutathione biosynthesis and protein kinase A signaling could differentiate the diabetic subjects from the prediabetic and healthy control subjects, and may involve in prediabetes transition to diabetes. This study provided more metabolomics information for the transition from prediabetes to diabetes.