Translational lung cancer research最新文献

Background: A definitive optimal oncologic care regimen for recurrent multiple brain metastases (BMs) has yet to be established, and the accrual of high-quality evidence pertaining to helical tomotherapy-based stereotactic radiotherapy (HT-SRT) in patients with BMs is needed.

Case description: We treated a 64-year-old male smoker initially diagnosed with non-small cell lung cancer (NSCLC) with BMs, and the initial schedule involved administering linear accelerator-based hypofractionated stereotactic radiotherapy (Linac-HSRT) targeting 6 intracranial lesions. Further chemotherapy was declined due to intolerance after one cycle of paclitaxel-albumin/carboplatin. Distant brain failure (DBF) and extracranial progression emerged 3 months subsequent to the initial SRT, and helical tomotherapy-based hypofractionated stereotactic radiotherapy (HT-HSRT) was replanned to 4 BMs, while helical tomotherapy-based intensity-modulated radiotherapy was employed for the extracranial lesions. Nevertheless, reirradiation with hippocampal-sparing HT-HSRT and simultaneous memantine approach were imminently delivered for confirmed DBF, as 19 newly identified intact intracranial lesions were observed at 5 months posttreatment. As assessed by the Hopkins Verbal Learning Test Revised Total Recall test, neither severe symptomatic radionecrosis (RN) nor neurocognitive dysfunction has manifested thus far, representing a survival period of 20.5 months. In the literature review, SRT delivery schedule to BMs, strategies for managing recurrent BMs and addressing RN, along with 6 summarized published studies of HT-SRT for BM were discussed.

Conclusions: We posit that the administration of repeated SRT for recurrent BMs in a short-term interval may be viable, yet randomized, robust analyses are imperative to ascertain the potential benefits of HT-SRT in preserving neurocognition and confirm the efficacy of memantine and hippocampal avoidance during SRT.

Background: Identifying small pulmonary nodules during sublobar resection via video-assisted thoracoscopic surgery (VATS) poses certain challenges. Although preoperative computed tomography (CT)-guided localization is common, it is invasive and may lead to complications. This study aims to develop a novel, non-invasive technique for improving the accuracy of pulmonary nodule localization during VATS, with the goal of reducing complications associated with traditional methods.

Methods: We developed the longitude-latitude-depth (LLD) localization method, a novel intraoperative approach for localizing small pulmonary nodules. The LLD method uses anatomical reference points derived from the lung's natural structure to guide nodule localization during surgery. This retrospective study compared patients with small pulmonary nodules (≤2 cm in diameter, consolidation tumor ratio ≤0.5, and outer one-third of the pulmonary parenchyma) undergoing either intraoperative LLD localization or preoperative CT-guided hook-and-wire localization followed by VATS at Qilu Hospital of Shandong University from March 2020 to November 2023. Propensity score matching (PSM) analysis was used to the compare clinical information and perioperative outcomes, with 176 patients in each group after matching was performed.

Results: Compared to the CT-guided localization, the LLD method achieved higher accuracy (96.59%) during surgery and had a significantly reduced localization duration (5 vs. 18 min), needle-carrying time (0 vs. 81. min), localization complications (pain: 0% vs. 4.55%; hemothorax: 0% vs. 3.41%; pneumothorax: 0% vs. 4.55%; hemoptysis: 0% vs. 6.82%), estimated blood loss (37.5 vs. 55 mL), chest tube removal time (3 vs. 4 days), postoperative pain score (3 vs. 4 score), postoperative day (5 vs. 6 days), hospitalization cost (CNY ¥39764.25 vs. CNY ¥48458.41), and failure rate (3.41% vs. 8.52%).

Conclusions: LLD localization is noninvasive, time-saving, and cost-effective and may be a feasible, safe, and effective technique for localizing small pulmonary nodules during surgery.

Background: Brain metastasis (BrM) has been a challenge for lung cancer treatment, but the mechanisms underlying lung cancer BrM remain elusive. This study aims to dissect cellular components and their spatial distribution in human BrM tumors of lung adenocarcinoma (LUAD) and identify potential therapeutic targets.

Methods: We performed single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) on three LUAD BrMs, and validated our findings using public scRNA-seq data of 10 LUAD BrMs. Western blotting, quantitative real-time polymerase chain reaction (qRT-PCR) and functional experiments were employed for experimental studies.

Results: By combining scRNA-seq and ST, our analysis revealed the inter- and intra-tumoral heterogeneity of cellular components and their spatial localization within LUAD BrMs. Through RNA velocity and transcription factor (TF) regulatory activity analyses, we identified ATF3 as a potential regulator of the mesenchymal-epithelial transition (MET) program, which plays crucial roles in the colonization of tumor cells at metastatic sites. Furthermore, we demonstrated that knockdown of ATF3 significantly inhibited cancer cell proliferation while promoting cancer cell migration. Mechanistically, ATF3 knockdown could reverse the MET program. Additionally, we revealed that LGALS3/ANXA2-mediated cell-cell interaction between macrophage and tumor cells may also promote the MET program.

Conclusions: Our study provides a single-cell atlas of the cellular composition in BrM of LUAD and identifies ATF3 as a potential therapeutic target for BrM treatment.

Background: Despite the recent advancements in the treatment of cancer, the 5-year survival of patients with non-small cell lung cancer (NSCLC) remains unsatisfactory. Lung adenocarcinoma (LUAD) is NSCLC's most common subtype, and metastasis is the major cause of death in patients with cancer. Therefore, identifying novel targets associated with metastasis in NSCLC is crucial to improving treatment. This study aimed to characterize the expression of GNGT1 in LUAD and to clarify the mechanism underlying the association between the higher expression level of GNGT1 and worse prognosis in patients.

Methods: The transcriptome datasets and clinical information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database. Bioinformatics analyses were performed in 515 patients who were stratified into two groups (high- and low-GNGT1 expression group) according to the GNGT1 level. Overall survival, DNA promotor methylation, immune cell infiltration, gene set enrichment analysis (GSEA), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed to elucidate the functions of GNGT1 and to identify the related hub genes in LUAD. Their expression and functions in LUAD were verified using tissues from patients and transgenic mice overexpressing GNGT1 under the control of a lung-specific promoter (Scgb1a1-Cre).

Results: GNGT1 was overexpressed in patients with LUAD and was associated with poor prognosis. GNGT1 expression was significantly correlated with gene alteration and hypomethylated promoter status. High GNGT1 expression in patients with LUAD was associated with advanced lymph node metastasis and the degree of immune cell infiltration. Functional enrichment analyses indicated that differentially expressed genes (DEGs) in the high-GNGT1 group participated in DNA replication, DNA replication preinitiation, and M phase, while cell adhesion molecules, apoptosis, and natural killer cell-mediated cytotoxicity were all downregulated. Messenger RNA and protein levels were correspondingly regulated in human LUAD tissues and the Scgb1a1-Cre; LSL-GNGT1 mouse model (GNGT1^fl/+ mice).

Conclusions: GNGT1 was associated with tumor cell proliferation via the enhancement of tumor cell stemness and interaction with driver genes. Elevated GNGT1 expression promoted epithelial-mesenchymal transformation, remodeled the tumor microenvironment, and led to tumor metastasis, ultimately worsening the survival-related prognosis of patients with LUAD.

Background: Reliable mediastinal restaging after neoadjuvant treatment to rule out persistent nodal disease is essential to select patients for resection. Main endpoints of this study are: to analyse the accuracy of video-assisted mediastinoscopic lymphadenectomy (VAMLA) and to determine the rate of persistent N2-3 in patients with clinical N2-3 (cN2-3) non-small cell lung cancer (NSCLC) after neoadjuvant treatment.

Methods: Prospective observational single-centre study of patients with NSCLC and histologically proven mediastinal involvement (cN2-3), treated with neoadjuvant therapy who underwent VAMLA for restaging. Patients with negative VAMLA underwent lung resection. Systematic nodal dissection (SND) was considered the reference test to confirm negative VAMLAs. Staging values were calculated based on pathologic findings using the standard formulas.

Results: From 2017 to 2023, 41 patients with cN2-3 NSCLC received neoadjuvant therapy and thereafter underwent VAMLA for restaging. Neoadjuvant treatments: concomitant cisplatin-based chemotherapy and radical radiotherapy (n=33), chemoradiotherapy and immunotherapy (n=2), chemotherapy (n=2), chemotherapy and immunotherapy (n=2), tyrosine kinase inhibitor and immunotherapy (n=1) and immunotherapy (n=1). VAMLA was feasible in all patients. Restaging values with VAMLA were: sensitivity, 1 [95% confidence interval (CI): 0.72-1]; negative predictive value (NPV), 1 (95% CI: 0.89-1); and diagnostic accuracy, 1 (95% CI: 0.91-1). Rate of persistent N2 of the whole series: 29% (12/41). Complication rate was 9.7%.

Conclusions: This preliminary series of patients with cN2-3 NSCLC treated with neoadjuvant treatment and restaged by VAMLA demonstrated high accuracy and high rate of persistent N2. VAMLA should be included in restaging algorithms to select patients with potentially resectable cN2-3 NSCLC.

Background: The benefits of spontaneous ventilation (SV)-video-assisted thoracoscopic surgery (VATS) in octogenarian patients with non-small-cell lung cancer (NSCLC) have rarely been reported. This retrospective study was conducted to evaluate the safety and feasibility of SV-VATS in octogenarian patients with NSCLC.

Methods: Patients with NSCLC aged >80 years who underwent SV-VATS or mechanical ventilation (MV)-VATS between 2017 and 2022 were included in this study. The baseline characteristics of the two groups were balanced by a 1:2 propensity score matching (PSM). Intraoperative and postoperative outcomes were compared. Overall survival (OS) and disease-free survival (DFS) were analyzed by Kaplan-Meier survival analysis and Cox regression.

Results: A total of 251 patients were initially included, and after applying selection criteria and PSM, 22 patients were in the SV-VATS group and 44 in the MV-VATS group. Baseline characteristics were well balanced between the two groups. Compared with the MV-VATS group, the SV-VATS group had shorter post-anesthesia care unit (PACU) stay (88.8±22.3 vs. 111±38.8, P=0.01) and shorter resuscitation time (88.8±22.7 vs. 112±40.4, P=0.02). No statistically significant differences were observed in the surgical and anaesthesia times, chest tube duration, total volume of chest drainage, intraoperative blood loss, postoperative hospital stay, or complications in the PACU. The OS and DFS of patients who underwent SV-VATS were comparable to those of patients who underwent MV-VATS.

Conclusions: SV-VATS appears to be a safe and feasible option for octogenarian patients with NSCLC, providing a new approach to surgical treatment. Large-scale prospective studies are required to further validate its feasibility.

Background: Lung adenocarcinoma (LUAD) is associated with high morbidity and mortality rates. Increasing evidence indicates that neutrophil extracellular traps (NETs) play a critical role in tumor progression, metastasis and immunosuppression in the LUAD tumor microenvironment (TME). Nevertheless, the use of NET formation-related genes (NFRGs) to predict LUAD patient survival and response to immunotherapy has not been explored. Therefore, this study aimed to construct a NFRGs-based prognostic signature for stratifying LUAD patients and informing individualized management strategies.

Methods: The cell composition of the LUAD TME was investigated using the single-cell sequencing data in Single-Cell Lung Cancer Atlas (LuCA). NFRGs were identified to construct a prognostic signature based on The Cancer Genome Atlas (TCGA) cohort which was validated in the Gene Expression Omnibus (GEO) dataset. The univariate Cox and least absolute shrinkage and selection operator (LASSO) Cox regression models, receiver operating characteristic (ROC) and Brier Score were applied to assess the prognostic model. A nomogram was established to facilitate the clinical application of the risk score. The Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATE) and Tumor Immune Dysfunction and Exclusion (TIDE) algorithm were utilized to assess the TME and predict immunotherapy response. Reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was applied to quantify the expression levels of four NFRGs in LUAD paired tissue samples.

Results: Single‑cell RNA sequence analysis showed the importance of neutrophils in LUAD TME. We developed and validated a 4-NFRG (CAT, CTSG, ENO1, TLR2) prognostic signature based on TCGA and GEO cohorts, which stratified patients into high-risk and low-risk groups. Univariate and multivariate analyses showed that our risk model could independently predict the survival of LUAD patients. Patients in the low-risk group exhibited a more active immune microenvironment, lower TIDE scores, lower half-maximal inhibitory concentration (IC50) values and higher immune checkpoint molecule expression. Our risk signature could serve as a biomarker for predicting immunotherapeutic benefits.

Conclusions: We developed a novel prognostic signature for LUAD patients based on NFRGs and emphasized the critical role of this signature in predicting LUAD patient survival and immunotherapy response.

Background: Prognosis prediction is crucial for non-small cell lung cancer (NSCLC) treatment planning. While tumor hypoxia significantly impacts patient outcomes, identifying hypoxic genomic markers remains challenging. This study sought to identify hypoxic computed tomography (CT) radiomic features and create an artificial intelligence (AI) model for NSCLC through the integration of multi-modal data.

Methods: In total, 452 NSCLC patients were enrolled in this study, including patients from The Second Affiliated Hospital of Soochow University (SC, n=112), The Cancer Genome Atlas (TCGA)-NSCLC dataset (n=74), the radiogenomics dataset (n=130), and the Gene Expression Omnibus (GEO) datasets (GSE19188: n=82, and GSE87340: n=54). Hypoxia status was classified using optimized cut-off values of hypoxia enrichment scores, which were calculated through single-sample gene set enrichment analysis (ssGSEA) of hypoxic genes. Radiomic features were extracted using three-dimensional (3D)-Slicer software. The least absolute shrinkage and selection operator (LASSO) algorithm was used to identify hypoxic CT radiomic features. A model named ssuBERT (semantic structured unit embedded in Bidirectional Encoder Representations from Transformers) was developed to analyze electronic health records (EHRs). An AI model for overall survival prediction was constructed by integrating CT radiomic features, ssuBERT features, and clinical data, and evaluated using five-fold cross-validation.

Results: Higher hypoxia levels were correlated with worse survival outcomes. Twenty-eight radiomic features showed significant discriminatory power in detecting hypoxia status with an area under the curve (AUC) of 0.8295. The ssuBERT model achieved a weighted accuracy of 0.945 in recognizing semantic structured units in EHRs. The EHR model exhibited superior predictive performance among the single-modal models with an AUC of 0.7662. However, the multi-modal AI model had the highest average AUC of 0.8449 and an F1 score of 0.7557.

Conclusions: The AI model demonstrated potential in predicting NSCLC patient prognosis through multi-modal data integration, warranting further validation.

Background and objective: Pulmonary carcinoids (PCs) represent a rare subset of neuroendocrine tumors (NETs) within the respiratory tract that exhibit unique characteristics and clinical behaviors. These tumors are currently staged according to the tumor-nodules-metastases (TNM) classification of non-small cell lung cancer (NSCLC), which brings their reliability into question. The aim of this study was to assess reliability of the current TNM staging of PCs and explore other relevant prognostic factors of patient outcomes.

Methods: From January 2023 to October 2023, the PubMed and Embase databases were searched according to predefined keywords. Studies focusing on PCs, TNM classification, surgical management, and lymph node involvement were included. The search included meta-analyses, retrospective studies, and case reports. Pediatric cases and articles written in languages other than English were excluded.

Key content and findings: This review identified several retrospective cohort studies investigating the correlation between TNM staging, lymph node involvement, and survival outcomes in PC patients. Inconsistencies in survival rates across TNM stages were observed, highlighting the limitations of the current TNM classification as a main predictor of patient outcomes. Lymph node involvement emerged as a significant predictor of survival, with higher nodal stages associated with a poorer prognosis, especially for patients with atypical carcinoid tumors.

Conclusions: Excluding PCs from TNM staging of NSCLC and implementing new staging methods based on histological subtype and lymph node involvement may provide a better classification of this type of tumor, which could lead to more effective care for patients in the future.

Background and objective: While bibliometric studies of single journals have been conducted, bibliometric mapping has not yet been used to analyze the literature published by the Translational Lung Cancer Research (TLCR). This study aimed to comprehensively review all publications of TLCR from its inception to 2024 and provide a detailed overview of its main publication characteristics.

Methods: This study analyzed publications from TLCR spanning 2012 to 2024 using CiteSpace, VOSviewer, and the 'Bibliometrix' package in R. Descriptive bibliometric methods were employed to examine the trends and dynamics in TLCR literature, identifying leading authors, institutions, and countries in terms of publication output. Furthermore, bibliometric maps were generated to visualize key research topics and terms, highlighting their evolution over time.

Key content and findings: The analysis included 2,032 publications in TLCR from 2012 to 2023 and 121 publications in 2024. The study revealed a positive trend in literature production, although there has been a slight recent decline in the number of articles published in the TLCR. China emerged as the most productive country (n=587), with Shanghai Jiao Tong University being the most productive institution (n=127). Jianxing He from the First Affiliated Hospital of Guangzhou Medical University was identified as the most prolific author (n=75). The top ten most cited articles primarily address treatment strategies, recurrence, immune-related toxicities, global trends in mortality, and mechanisms of resistance, reflecting the broad scope and critical importance of ongoing research in lung cancer. Research published in TLCR predominantly targeted old adults with non-small cell lung cancer (n=879), with significant emphasis on overall survival (n=507), cancer staging (n=406), and cancer immunotherapy.

Conclusions: This study reviewed TLCR publications from 2012 to 2024, identifying key trends, top contributors, and research focuses. Future research directions in TLCR might focus on first-line treatment, ensartinib, and advanced data analysis methods such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) to revolutionize lung cancer research and practice. In conclusion, this study underscores TLCR's significant contributions to lung cancer research and provides valuable insights into its evolution and future directions.