Invasive Lung Adenocarcinoma Research Articles

A deep learning approach for predicting visceral pleural invasion in cT1 lung adenocarcinoma.

Currently, patients with cT1 stage non-small cell lung cancer measuring less than 2 cm are recommended to undergo sublobar resection. However, it should be noted that there is tumor heterogeneity within these lung nodules. Potential visceral pleural invasion (VPI) is regarded as a significant factor that contributes to local recurrence and poorer prognosis after sublobar resection and postoperative upstaging of the T-stage. Currently, there are no effective techniques for preoperative or intraoperative prediction of the status of VPI in lung nodules. The primary objective of this study is to develop a machine learning model for the non-invasive prediction of VPI, thereby providing surgical decision-making support for surgeons during operations. A total of 983 patients with nodules located within 5 mm distance from the pleura were included. Machine learning models were developed utilizing preprocessed 2D, 2.5D, and 3D computed tomography (CT) imaging data. These models were employed to predict the status of VPI, leveraging radiomics and deep learning techniques. The aforementioned three groups of data were further categorized into region of interest (ROI)-only (exclusively focused on the ROI) and ROI-rect (the minimum bounding rectangle or cuboid of the ROI) groups, based on whether they included images outside the ROI. Receiver operating characteristic (ROC) curves were created for the assessment of predictive accuracy across different models. Employing the Youden's index, patients were categorized into high-risk and low-risk groups based on the model's criteria, which was then followed by an in-depth analysis of overall survival rates among the distinct patient cohorts. This study included a training cohort of 786 patients and a validation cohort of 197 patients. In comparison to radiomic and radiological models, deep learning models, especially the 2D-rect model, demonstrated better predictive performance. Although the 3D-ROI-only model exhibited the highest areas under the curve (AUC) value (0.952), its predictive performance for the status of VPI was found to be inferior according to the decision curve, calibration curve, and survival analysis. The developed deep learning signature offers a robust instrument for the precise prognostication of vascular invasion in stage cT1 lung adenocarcinoma, thereby enhancing stratification for prognostic evaluation. Moreover, the application of this advanced computational model aids in the refinement of therapeutic approach formulation for individuals diagnosed with cT1 lung adenocarcinoma.

Predicting the invasiveness of ground-glass opacity predominant lung adenocarcinoma with clinical stage Ia: a CT-based semantic and radiomics analysis

Predicting the invasiveness of ground-glass opacity predominant lung adenocarcinoma with clinical stage Ia: a CT-based semantic and radiomics analysis

Ladinin 1 Shortens Survival via Promoting Proliferation and Enhancing Invasiveness in Lung Adenocarcinoma.

Lung cancer is one of the deadliest cancers worldwide, including in Taiwan. The poor prognosis of the advanced lung cancer lies in delayed diagnosis and non-druggable targets. It is worth paying more attention to these ongoing issues. Public databases and an in-house cohort were used for validation. The KM plotter was utilized to discover the clinical significance. GSEA and GSVA were adopted for a functional pathway survey. Molecular biological methods, including proliferation, migration, and the EMT methods, were used for verification. Based on public databases, the increased expression of Ladinin 1 (LAD1) was presented in tumor and metastatic sites. Furthermore, an in-house cohort revealed a higher intensity of LAD1 in tumor rather than in normal parts. The greater the expression of LAD1 was, the shorter the duration of lung adenocarcinoma (LUAD) patient survival. Moreover, the association of B3GNT3 with LAD1 affected the survival of LUAD patients. Functional analyses using GSEA and GSVA revealed the associations with survival, migration, invasion, and EMT. Biologic functions supported the roles of LAD1 in proliferation via the cell cycle and migration in EMT. This study reveals that LAD1 plays a major role in regulating proliferation and migration in lung cancer and impacts survival in LUAD. It is worth investing in further studies and in the development of drugs targeting LAD1.

A CT-Based Radiomics Nomogram Combined with Clinic-Radiological Characteristics for Preoperative Prediction of the Novel IASLC Grading of Invasive Pulmonary Adenocarcinoma

The novel International Association for the Study of Lung Cancer (IASLC) grading system of invasive lung adenocarcinoma (ADC) demonstrated a remarkable prognostic effect and enabled numerous patients to benefit from adjuvant chemotherapy. We sought to build a CT-based nomogram for preoperative prediction of the IASLC grading. This work retrospectively analyzed the CT images and clinical data of 303 patients with pathologically confirmed invasive ADC. The histological subtypes and radiological characteristics of the patients were re-evaluated. Radiomics features were extracted, and the optimal subset of features was established by ANOVA, spearman correlation analysis, and the least absolute shrinkage and selection operator (LASSO). Univariate and multivariate analyses identified the independent clinical and radiological variables. Finally, multivariate logistic regression analysis incorporated clinical, radiological, and optimal radiomics features into the nomogram. Receiver operating characteristic (ROC) curve, and accuracy were applied to assess the model's performance. Decision curve analysis (DCA), and calibration curve were applied to assess the clinical usefulness. Nine selected CT image features were used to develop the radiomics model. The accuracy, precision, sensitivity, and specificity of the radiomics model outperformed the clinic-radiological model in the training and testing sets. Integrating Radscore with independent radiological characteristics showed higher prediction performance than clinic-radiological characteristics alone in the training (AUC, 0.915 vs. 0.882; DeLong, p < 0.05) and testing (AUC, 0.838 vs. 0.782; DeLong, p < 0.05) sets. Good calibration and decision curve analysis demonstrated the clinical usefulness of the nomogram. Radiomics features effectively predict high-grade ADC. The combined nomogram may facilitate selecting patients who benefit from adjuvant treatment.

DNASE1L3 regulation by transcription factor FOXP2 affects the proliferation, migration, invasion and tube formation of lung adenocarcinoma.

Lung adenocarcinoma (LUAD) is prone to bone metastasis, resulting in poor prognosis. The present study aimed to detect the expression of deoxyribonuclease 1-like 3 (DNASE1L3) and forkhead-box P2 (FOXP2) in LUAD cells to investigate the role of DNASE1L3 in the regulation of proliferation, migration, invasion and tube formation of LUAD cells and how FOXP2 affects DNASE1L3 expression. The expression of DNASE1L3 and FOXP2 in LUAD cells was analyzed by reverse transcription-quantitative PCR (RT-qPCR) and western blotting. The transfection efficiency of DNASE1L3 overexpression plasmids, FOXP2 overexpression or interference plasmids into A549 cells was also confirmed by RT-qPCR and western blotting. The viability, proliferation, migration and invasion and tube formation of LUAD cells following transfection was in turn detected by MTT, EdU staining, wound healing, Transwell and tube formation assay. The expression of proteins associated with epithelial-mesenchymal transformation and tube formation was detected by western blotting. Binding between DNASE1L3 and FOXP2 was confirmed by dual-luciferase reporter assay and chromatin immunoprecipitation. Gene Expression Profiling Interactive Analysis database predicted that underexpression of DNASE1L3 in LUAD was associated with poor prognosis. DNASE1L3 expression was decreased in LUAD cells and overexpression of DNASE1L3 inhibited the proliferation, migration, invasion and tube formation of LUAD cells. Transcription factor FOXP2 positively regulated DNASE1L3 transcription in LUAD cells. FOXP2 was also underexpressed in LUAD cells and downregulation of FOXP2 promoted proliferation, migration, invasion and tube formation of LUAD cells, which was reversed by overexpression of DNASE1L3. In conclusion, DNASE1L3 was positively regulated by transcription factor FOXP2 and overexpression inhibited proliferation, migration, invasion and tube formation of LUAD cells.

Validation of the novel International Association for the Study of Lung Cancer grading system and prognostic value of filigree micropapillary and discohesive growth pattern in invasive pulmonary adenocarcinoma

IntroductionThe Pathology Committee of the International Association for the Study of Lung Cancer (IASLC) proposed a new histological grading system based on the combination of predominant and high-grade patterns in 2020. Materials and MethodsPathological sections from 631 patients with stage I-III invasive lung adenocarcinoma were reviewed. We then determined the histological grade according to the new grading system and confirmed the pathological features that included the filigree micropapillary and discohesive growth pattern. Applying of the novel IASLC grading system in prognosis stratification was verified and the clinical significance of the pathological characteristics was explored. ResultsCox multivariable analysis revealed that in the stage I-III invasive lung adenocarcinoma, the IASLC grading system was significantly associated with disease-free survival (DFS) [hazard ratio (HR) = 1.419; 95 % confidence interval (CI): 1.040–1.937; P = 0.027] and overall survival (OS) (HR = 1.899; 95 % CI: 1.168–3.087; P = 0.010). In patients with IASLC Grades 1 and 2, the simultaneous presence of filigree micropapillary and discohesive growth pattern was significantly correlated with DFS (HR = 1.899; 95 % CI:1.168–3.087; P = 0.010). However, the filigree micropapillary and discohesive growth pattern did not affect the OS (HR = 2.786; P = 0.317). The competitive risk model revealed that in the stage I cohort, the simultaneous presence of filigree micropapillary and discohesive growth pattern was a risk factor for recurrence and metastasis [sub- distribution HR (SHR) = 1.987; 95 %CI: 1.122–3.518; P = 0.019]. ConclusionOur study verified that the new prognostic stratification system was an effective stratification tool. Filigree micropapillary and discohesive growth pattern may also be risk factors for DFS, postoperative recurrence and metastasis.

Establishment and Validation of a Novel Nomogram for Predicting Distant Metastasis in Patients with Invasive Lung Adenocarcinoma.

To establish and verify a nomogram for predicting distant metastasis in invasive lung adenocarcinoma (IAC). Observational study. Department of Thoracic Surgery, Jinan Central Hospital, Jinan, China, from December 2021 to May 2022. To create a nomogram, univariate and multivariate logistic regression analyses wereused to identify the independent predictors of distant metastasis. The calibration, discrimination, and clinical performance of the nomogram were tested by calibration plots, area under receiver operating characteristic curve (AUC), and decision curve analysis (DCA). Age at diagnosis (<70 years), histological type (invasive mucinous adenocarcinoma), T stage, N stage, surgical approach, and lymph node dissection were independent predictors for the development of nomogram. Compared with the American Joint Committee on Cancer-8th edition staging system, AUC showed that this prediction model has a higher predictive performance (training set: 0.922 vs. 0.790; verification set: 0.919 vs. 0.779). In addition, the overall survival time (OS) of IAC patients was meaningfully different among the three groups of different risks stratified based on model score (p <0.001). The prediction model constructed according to factors such as histological type and surgical approach in this study can accurately predict distant metastasis in IAC patients and define high-risk patients according to nomogram score. Invasive adenocarcinoma IAC, Distant metastasis, Nomogram, Surveillance, Epidemiology and end results SEER.

Analysis of primary synchronous breast invasive ductal carcinoma and lung adenocarcinoma with next-generation sequencing: A case report.

Multiple primary cancers (MPCs) have an increasing incidence rate due to the detection of early stages of cancer and the development of effective therapeutic strategies. MPCs are less common compared with metachronous cancers. Therefore, distinguishing synchronous primary tumors from metastasis and developing an individualized treatment strategy can be challenging. In the present study, the case of a 70-year-old female who was referred to The First Hospital of Jilin University (Changchun, China) with an enlarged left cervical lymph node and no other clinical manifestations is reported. Radiography revealed distinct lesions in the left breast, left cervical lymph node and bilateral lungs. Subsequently, a biopsy was performed in all three lesions and then each specimen was subjected to immunohistochemistry, fluorescence in situ hybridization, amplification refractory mutation system-PCR and next-generation sequencing (NGS). Disease-related enrichment of lymph node mutant genes and Gene Ontology Biological Process enrichment of breast, as well as lung, mutant genes were performed using the Database for Annotation, Visualization and Integrated Discovery. Based on the molecular assessment, the patient was finally diagnosed with breast invasive ductal carcinoma, primary lung adenocarcinoma and cervical lymph node metastatic lung adenocarcinoma. Since primary synchronous breast and lung cancer (SBLC) is rare, a molecular assessment, particularly using NGS, could provide important information for both the diagnosis and treatment of SBLC.

M2 macrophage-derived exosomal miR-1911-5p promotes cell migration and invasion in lung adenocarcinoma by down-regulating CELF2-activated ZBTB4 expression.

Lung adenocarcinoma (LUAD) is one of the most aggressive, lethal cancers, comprising around 40% of lung cancer cases. Metastases are the primary cause of LUAD deaths. The mechanism underlying metastatic LUAD and tumor microenvironment remain largely unknown. To explore the effect of M2 macrophage-derived exosomes on LUAD progression. Quantitative-PCR (q-PCR) and western blot were used to measure the expression of RNAs and proteins separately. Co-culture experiments wound healing and Transwell invasion assays were performed to evaluate the effect of M2 macrophage-derived exosomes on LUAD cell migration and invasion. RNA pulldown and luciferase reporter, RNA-binding immunoprecipitation (RIP), and mRNA stability assays were conducted to explore the downstream mechanism of exosomal microRNA-1911-5p (miR-1911-5p). M2 macrophage-derived exosomes accelerated the migration and invasion of LUAD cells. M2 macrophages-secreted exosomal miR-1911-5p enhanced cell migration and invasion in LUAD. Mechanically, miR-1911-5p targeted CUGBP- and ETR-3-like family 2 (CELF2) to downregulate zinc finger and BTB domain containing 4 (ZBTB4) in LUAD. Additionally, miR-1911-5p promoted LUAD progression via ZBTB4. The present study demonstrated that M2 macrophage-derived exosomal miR-1911-5p facilitates the migration and invasion of LUAD cells by inhibiting CELF2-activated ZBTB4, which might offer insight into LUAD treatment.

A delta-radiomics model for preoperative prediction of invasive lung adenocarcinomas manifesting as radiological part-solid nodules.

This study aims to explore the value of the delta-radiomics (DelRADx) model in predicting the invasiveness of lung adenocarcinoma manifesting as radiological part-solid nodules (PSNs). A total of 299 PSNs histopathologically confirmed as lung adenocarcinoma (training set, n = 209; validation set, n = 90) in our hospital were retrospectively analyzed from January 2017 to December 2021. All patients underwent diagnostic noncontrast-enhanced CT (NCECT) and contrast-enhanced CT (CECT) before surgery. After image preprocessing and ROI segmentation, 740 radiomic features were extracted from NCECT and CECT, respectively, resulting in 740 DelRADx. A DelRADx model was constructed using the least absolute shrinkage and selection operator logistic (LASSO-logistic) algorithm based on the training cohort. The conventional radiomics model based on NCECT was also constructed following the same process for comparison purposes. The prediction performance was assessed using area under the ROC curve (AUC). To provide an easy-to-use tool, a radiomics-based integrated nomogram was constructed and evaluated by integrated discrimination increment (IDI), calibration curves, decision curve analysis (DCA), and clinical impact plot. The DelRADx signature, which consisted of nine robust selected features, showed significant differences between the AIS/MIA group and IAC group (p < 0.05) in both training and validation sets. The DelRADx signature showed a significantly higher AUC (0.902) compared to the conventional radiomics model based on NCECT (AUC = 0.856) in the validation set. The IDI was significant at 0.0769 for the integrated nomogram compared with the DelRADx signature. The calibration curve of the integrated nomogram demonstrated favorable agreement both in the training set and validation set with a mean absolute error of 0.001 and 0.019, respectively. Decision curve analysis and clinical impact plot indicated that if the threshold probability was within 90%, the integrated nomogram showed a high clinical application value. The DelRADx method has the potential to assist doctors in predicting the invasiveness for patients with PSNs. The integrated nomogram incorporating the DelRADx signature with the radiographic features could facilitate the performance and serve as an alternative way for determining management.

Non-contrast and contrast enhanced computed tomography radiomics in preoperative discrimination of lung invasive and non-invasive adenocarcinoma.

This study aimed to assess the value of radiomics based on non-contrast computed tomography (NCCT) and contrast-enhanced computed tomography (CECT) images in the preoperative discrimination between lung invasive adenocarcinomas (IAC) and non-invasive adenocarcinomas (non-IAC). We enrolled 1,185 pulmonary nodules (478 non-IACs and 707 IACs) to build and validate radiomics models. An external testing set comprising 63 pulmonary nodules was collected to verify the generalization of the models. Radiomic features were extracted from both NCCT and CECT images. The predictive performance of radiomics models in the validation and external testing sets were evaluated and compared with radiologists' evaluations. The predictive performances of the radiomics models were also compared between three subgroups in the validation set (Group 1: solid nodules, Group 2: part-solid nodules, and Group 3: pure ground-glass nodules). The NCCT, CECT, and combined models showed good ability to discriminate between IAC and non-IAC [respective areas under the curve (AUCs): validation set = 0.91, 0.90, and 0.91; Group 1 = 0.82, 0.79, and 0.81; Group 2 = 0.93, 0.92, and 0.93; and Group 3 = 0.90, 0.90, and 0.89]. In the external testing set, the AUC of the three models were 0.89, 0.91, and 0.89, respectively. The accuracies of these three models were comparable to those of the senior radiologist and better those that of the junior radiologist. Radiomic models based on CT images showed good predictive performance in discriminating between lung IAC and non-IAC, especially in part solid nodule group. However, radiomics based on CECT images provided no additional value compared to NCCT images.

Delineating the dynamic evolution from preneoplasia to invasive lung adenocarcinoma by integrating single-cell RNA sequencing and spatial transcriptomics

The cell ecology and spatial niche implicated in the dynamic and sequential process of lung adenocarcinoma (LUAD) from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and subsequent invasive adenocarcinoma (IAC) have not yet been elucidated. Here, we performed an integrative analysis of single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics (ST) to characterize the cell atlas of the invasion trajectory of LUAD. We found that the UBE2C + cancer cell subpopulation constantly increased during the invasive process of LUAD with remarkable elevation in IAC, and its spatial distribution was in the peripheral cancer region of the IAC, representing a more malignant phenotype. Furthermore, analysis of the TME cell type subpopulation showed a constant decrease in mast cells, monocytes, and lymphatic endothelial cells, which were implicated in the whole process of invasive LUAD, accompanied by an increase in NK cells and MALT B cells from AIS to MIA and an increase in Tregs and secretory B cells from MIA to IAC. Notably, for AIS, cancer cells, NK cells, and mast cells were colocalized in the cancer region; however, for IAC, Tregs colocalized with cancer cells. Finally, communication and interaction between cancer cells and TME cell-induced constitutive activation of TGF-β signaling were involved in the invasion of IAC. Therefore, our results reveal the specific cellular information and spatial architecture of cancer cells and TME subpopulations, as well as the cellular interaction between them, which will facilitate the identification and development of precision medicine in the invasive process of LUAD from AIS to IAC.

Measuring pure ground-glass nodules on computed tomography: assessing agreement between a commercially available deep learning algorithm and radiologists' readings.

Deep learning algorithms (DLAs) could enable automatic measurements of solid portions of mixed ground-glass nodules (mGGNs) in agreement with the invasive component sizes measured during pathologic examinations. However, the measurement of pure ground-glass nodules (pGGNs) based on DLAs has rarely been reported in the literature. To evaluate the use of a commercially available DLA for the automatic measurement of pGGNs on computed tomography (CT). In this retrospective study, we included 68 patients with 81 pGGNs. The maximum diameter of the nodules was manually measured by senior radiologists and automatically segmented and measured by the DLA. Agreement between the measurements by the radiologist and DLA was assessed using Bland-Altman plots, and correlations were analyzed using Pearson correlation. Finally, we evaluated the association between the radiologist and DLA measurements and the invasiveness of lung adenocarcinoma in patients with pGGNs on preoperative CT. The radiologist and DLA measurements exhibited good agreement with a Bland-Altman bias of 3.0%, which were clinically acceptable. The correlation between both sets of maximum diameters was also strong, with a Pearson correlation coefficient of 0.968 (P < 0.001). In addition, both sets of maximum diameters were larger in the invasive adenocarcinoma group than in the non-invasive adenocarcinoma group (P < 0.001). Automatic pGGNs measurements by the DLA were comparable with those measured manually and were closely associated with the invasiveness of lung adenocarcinoma.

3D deep learning versus the current methods for predicting tumor invasiveness of lung adenocarcinoma based on high-resolution computed tomography images.

Different pathological subtypes of lung adenocarcinoma lead to different treatment decisions and prognoses, and it is clinically important to distinguish invasive lung adenocarcinoma from preinvasive adenocarcinoma (adenocarcinoma in situ and minimally invasive adenocarcinoma). This study aims to investigate the performance of the deep learning approach based on high-resolution computed tomography (HRCT) images in the classification of tumor invasiveness and compare it with the performances of currently available approaches. In this study, we used a deep learning approach based on 3D conventional networks to automatically predict the invasiveness of pulmonary nodules. A total of 901 early-stage non-small cell lung cancer patients who underwent surgical treatment at Shanghai Chest Hospital between November 2015 and March 2017 were retrospectively included and randomly assigned to a training set (n=814) or testing set 1 (n=87). We subsequently included 116 patients who underwent surgical treatment and intraoperative frozen section between April 2019 and January 2020 to form testing set 2. We compared the performance of our deep learning approach in predicting tumor invasiveness with that of intraoperative frozen section analysis and human experts (radiologists and surgeons). The deep learning approach yielded an area under the receiver operating characteristic curve (AUC) of 0.946 for distinguishing preinvasive adenocarcinoma from invasive lung adenocarcinoma in the testing set 1, which is significantly higher than the AUCs of human experts (P<0.05). In testing set 2, the deep learning approach distinguished invasive adenocarcinoma from preinvasive adenocarcinoma with an AUC of 0.862, which is higher than that of frozen section analysis (0.755, P=0.043), senior thoracic surgeons (0.720, P=0.006), radiologists (0.766, P>0.05) and junior thoracic surgeons (0.768, P>0.05). We developed a deep learning model that achieved comparable performance to intraoperative frozen section analysis in determining tumor invasiveness. The proposed method may contribute to clinical decisions related to the extent of surgical resection.

PPP1R14D promotes the proliferation, migration and invasion of lung adenocarcinoma via the PKCα/BRAF/MEK/ERK signaling pathway.

Protein phosphatase 1 (PP1) inhibitors play a role in tumor progression through different mechanisms. Protein phosphatase 1 regulatory subunit 14D (PPP1R14D) is an inhibitor of PP1. However, the role of PPP1R14D in tumors and its mechanism of action are largely unknown. The purpose of the present study was to investigate the expression, function and mechanism of PPP1R14D in lung adenocarcinoma (LUAD). In the present study, GEPIA database analysis and immunohistochemistry demonstrated that PPP1R14D was highly expressed in LUAD tissues and that the expression of PPP1R14D in LUAD was negatively correlated with the age of patients and positively correlated with the 8th American Joint Committee on Cancer staging among patients. In addition, Kaplan‑Meier Plotter database analysis showed that PPP1R14D expression was associated with lower survival rates in patients with LUAD. PPP1R14D knockdown significantly inhibited LUAD cell proliferation, migration and invasion and induced LUAD cell arrest at the G1 phase of the cell cycle. Mechanistic analyses revealed that PPP1R14D knockdown may inhibit cell proliferation, migration and invasion by inactivating PKCα/BRAF/MEK/ERK pathway signaling and its downstream key proteins c‑Myc/Cyclin E1‑CDK2 and MMP2/MMP9/Vimentin. Moreover, knockdown of PPP1R14D suppressed tumor growth invivo. All these results showed that PPP1R14D plays an important role in LUAD tumorigenesis and may serve as a potential prognostic factor and therapeutic target in LUAD.

ARNTL2 is an indicator of poor prognosis, promotes epithelial-to-mesenchymal transition and inhibits ferroptosis in lung adenocarcinoma

ObjectivesARNTL2, as a circadian transcription factor, has been recently proposed to play an important role in a variety of tumors. however, the role of ARNTL2 in lung carcinogenesis and progression remains unclear. The purpose of this study was to investigate the effect of ARNTL2 on the clinical characteristics and prognosis of lung adenocarcinoma and to explore the relationship between ARNTL2 and EMT, ferroptosis in lung adenocarcinoma. MethodsThe Cancer Genome Atlas (TCGA) database's multi-omics data were downloaded using the Xena browser. Based on the expression levels of ARNTL2, patients with lung adenocarcinoma from TCGA were divided into two groups: those with high ARNTL2 expression and those with low ARNTL2 expression. ARNTL2 was studied for its effects on lung adenocarcinoma's clinicopathological, genomic, and immunological characteristics. Furthermore, in vivo and in vitro assays were used to confirm the impact of ARNLT2 knockdown on lung adenocarcinoma cells. ResultsWe found ARNTL2 is highly expressed in lung adenocarcinoma and was an independent predictor of a poor prognosis in patients with lung adenocarcinoma. In addition, we demonstrated that knockdown of ARNTL2 promoted ferroptosis, inhibited EMT, cell proliferation, migration and invasion in lung adenocarcinoma. In contrast, overexpressing ARNTL2 yielded the opposite results. ConclusionsARNTL2 is an independent unfavorable prognostic factor for lung adenocarcinoma. It plays a facilitating role in the development of lung adenocarcinoma, especially in promoting EMT and inhibiting ferroptosis, revealing that ARNTL2 may be a potential biomarker for lung adenocarcinoma.

Sublobectomy for stage IA1-2 invasive lung adenocarcinoma with consolidation tumor ratio ≤ 0.25.

Sublobectomy for early-stage non-small cell lung cancer (NSCLC) remains a matter of debate. This study aimed to discuss the feasibility of sublobectomy in patients with pathological-stage IA1-2 confirmed as pathologically invasive but radiologically noninvasive adenocarcinoma. From 2011 to 2019, we screened clinical stage IA1-IA2 lung cancer patients who underwent surgery at the Guangdong Provincial People's Hospital (GDPH). Inclusion criteria were maximum tumor diameter of 2.0cm or less, consolidation tumor ratio (CTR) ≤ 0.25, and pathologically confirmed invasive adenocarcinoma. Sublobectomy (segmentectomy and wedge resection) and lobectomy groups were created, and propensity scores were computed. The primary endpoints were lung cancer-specific overall survival (LCSS) and LCS- relapse-free survival (LCS-RFS) after adjusting propensity scores. A total of 1731 patients were screened, and 100 patients were enrolled. The lobectomy group had 51 patients and the limited resection group had 49. No cases relapsed, and two patients died from nontumor causes. For the entire cohort, the 5-year LCSS and 5-year LCS-RFS were 100% in the lobectomy and limited resection groups. When propensity scores matched, there were no differences in LCSS and LCS-RFS between the two groups (LCSS:100%, LCS-RFS 100% in lobectomy and limited resection, respectively). Sublobectomy may be curative for pathologically invasive but radiologically noninvasive adenocarcinoma at pathological stage IA1-2.

Opposing roles of ZEB1 in the cytoplasm and nucleus control cytoskeletal assembly and YAP1 activity.

Epithelial-mesenchymal transition (EMT) facilitates cancer invasion and is initiated by mesenchyme-driving transcription factors and actin cytoskeletal assembly. We show a cytoplasmic-to-nuclear transport gradient of the EMT transcription factor Zeb1 toward sites of invasion in lung adenocarcinoma (LUAD), driven by the EMT inducer Tgfb, which is expressed in M2 polarized macrophages. We show that Zeb1 binds free actin monomers and RhoA in the cytoplasm to inhibit actin polymerization, blocking cell migration and Yap1 nuclear transport. Tgfb causes turnover of the scaffold protein Rassf1a, which targets RhoA. Release of this RhoA inhibition in response to Tgfb overcomes Zeb1's block of cytoskeleton assembly and frees it for nuclear transport. A ZEB1 nuclear transport signature highlights EMT progression, identifies dedifferentiated invasive/metastatic human LUADs, and predicts survival. Blocking Zeb1 nuclear transport with a small molecule identified in this study inhibits cytoskeleton assembly, cell migration, Yap1 nuclear transport, EMT, and precancerous-to-malignant transition.

Distinct Prognostic Factors of Ground Glass Opacity and Pure-Solid Lesion in Pathological Stage I Invasive Lung Adenocarcinoma.

Ground glass opacity (GGO) is associated with favorable survival in lung cancer. However, the relevant evidence of the difference in prognostic factors between GGO and pure-solid nodules for pathological stage I invasive adenocarcinoma (IAC) is limited. We aimed to identify the impact of GGO on survival and find prognostic factor for part-GGO and pure-solid patients. Between December 2007 and August 2018, patients with pathological stage I IAC were retrospectively reviewed and categorized into the pure-GGO, part-GGO, and pure-solid groups. Survival curves were analyzed by the Kaplan-Meier method and compared by log-rank tests. Least absolute shrinkage and selection operator and Cox regression models were used to obtained prognostic factors for disease-free survival (DFS) and overall survival (OS). The number of patients with pure-GGO, part-GGO, and pure-solid was 134, 540, and 396, respectively. Part-GGO patients with consolidation-tumor-ratio (CTR) > 0.75 had similar outcome to those with pure-solid nodules. In part-GGO patients, CTR was negatively associated with OS (P = 0.007) and solid tumor size (STS) was negatively associated with DFS (P < 0.001). Visceral pleural invasion (VPI) was negatively associated with OS (P = 0.040) and DFS (P = 0.002). Sublobectomy was negatively associated with OS (P = 0.008) and DFS (P = 0.005), while extended N1 stations examination was associated with improved DFS (P = 0.005) in pure-solid patients. Though GGO component is a positively prognostic factors of patients with pathological stage I IAC, a small proportion of GGO components is not associated with favorable survival. VPI, STS and CTR are the significant predictors for part-GGO patients. Sublobectomy, especially wedge resection should be used cautiously in pure-solid patients.

Comprehensive analysis of clinical prognosis and CLIC1 immune invasion in lung adenocarcinoma.

Background:Chloride intracellular channel 1 (CLIC1) plays an important role in the process of cell epithelial transport, and is also involved in tumor invasion and metastasis. Due to its aberrant expression in cancer, the mechanism of action of CLIC1 in cancer has been carefully studied. In this study, we tried to investigate the relationship between CLIC1 and lung adenocarcinoma (LUAD).Methods:The RNA-sequencing data and clinical information of CLIC1 in lung adenocarcinoma were collected from the the cancer genome altas (TCGA) database and analyzed with R software. Paired t test and Mann–Whitney U test were used to detect differences between LUAD tissue and adjacent normal tissue, and the pROC software package performed reactive oxygen species (ROC) curves to detect cutoff values for CLIC1. The expression of CLIC1 in normal human tissues was extracted from the human protein altas (HPA) database, and analyzed clinical proteomic tumor analysis consortium by using UALCAN programme. The relationship between CLIC1 and LUAD was explored by enrichment analysis using gene oncology and Kyoto encyclopedia of genes and genomes. The tumor immunity estimation resource (TIMER) and integrated repository portal for tumor-immune system interactions (TISIDB) databases were used to analyze the correlation between CLIC1 and LUAD immune cell infiltration. Survival analysis of CLIC1 in LUAD was assessed by the PrognoScan database.Results:Compared with normal tissues, both mRNA (messenger Ribose Nucleic Acid) and protein of CLIC1 were overexpressed in LUAD, which was associated with shorter overall survial (OS). In addition, CLIC1 expression was in connection with some clinical-pathological characteristics like tumor node metatasis stages and lymph node metastases. What’s more, CLIC1 may play a role in the immune infiltration of LUAD.Conclusion:In summary, CLIC1 is up-regulated in LUAD and is associated with tumor metastasis, tumor staging, and OS. It may be regarded as a novel marker for prognostic judgement in LUAD.