Lung Adenocarcinoma Staging Using the 2011 IASLC/ATS/ERS Classification: A Pooled Analysis of Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma

Fusing Diverse Decision Rules in 3D-Radiomics for Assisting Diagnosis of Lung Adenocarcinoma

This study aimed to develop an interpretable diagnostic model for subtyping of pulmonary adenocarcinoma, including minimally invasive adenocarcinoma (MIA), adenocarcinoma in situ (AIS), and invasive adenocarcinoma (IAC), by integrating 3D-radiomic features and clinical data. Data from multiple hospitals were collected, and 10 key features were selected from 1600 3D radiomic signatures and 11 radiological features. Diverse decision rules were extracted using ensemble learning methods (gradient boosting, random forest, and AdaBoost), fused, ranked, and selected via RuleFit and SHAP to construct a rule-based diagnostic model. The model's performance was evaluated using AUC, precision, accuracy, recall, and F1-score and compared with other models. The rule-based diagnostic model exhibited excellent performance in the training, testing, and validation cohorts, with AUC values of 0.9621, 0.9529, and 0.8953, respectively. This model outperformed counterparts relying solely on selected features and previous research models. Specifically, the AUC values for the previous research models in the three cohorts were 0.851, 0.893, and 0.836. It is noteworthy that individual models employing GBDT, random forest, and AdaBoost demonstrated AUC values of 0.9391, 0.8681, and 0.9449 in the training cohort, 0.9093, 0.8722, and 0.9363 in the testing cohort, and 0.8440, 0.8640, and 0.8750 in the validation cohort, respectively. These results highlight the superiority of the rule-based diagnostic model in the assessment of lung adenocarcinoma subtypes, while also providing insights into the performance of individual models. Integrating diverse decision rules enhanced the accuracy and interpretability of the diagnostic model for lung adenocarcinoma subtypes. This approach bridges the gap between complex predictive models and clinical utility, offering valuable support to healthcare professionals and patients.

Artificial intelligence-based plasma exosome label-free SERS profiling strategy for early lung cancer detection

As a lung cancer biomarker, exosomes were utilized for in vitro diagnosis to overcome the lack of sensitivity of conventional imaging and the potential harm caused by tissue biopsy. However, given the inherent heterogeneity of exosomes, the challenge of accurately and reliably recognizing subtle differences in the composition of exosomes from clinical samples remains significant. Herein, we report an artificial intelligence-assisted surface-enhanced Raman spectroscopy (SERS) strategy for label-free profiling of plasma exosomes for accurate diagnosis of early-stage lung cancer. Specifically, we build a deep learning model using exosome spectral data from lung cancer cell lines and normal cell lines. Then, we extracted the features of cellular exosomes by training a convolutional neural network (CNN) model on the spectral data of cellular exosomes and used them as inputs to a support vector machine (SVM) model. Eventually, the spectral features of plasma exosomes were combined to effectively distinguish adenocarcinoma in situ (AIS) from healthy controls (HC). Notably, the approach demonstrated significant performance in distinguishing AIS from HC samples, with an area under the curve (AUC) of 0.84, sensitivity of 83.3%, and specificity of 83.3%. Together, the results demonstrate the utility of exosomes as a biomarker for the early diagnosis of lung cancer and provide a new approach to prescreening techniques for lung cancer.

A Radiological-Radiomics model for differentiation between minimally invasive adenocarcinoma and invasive adenocarcinoma less than or equal to 3 cm: A two-center retrospective study

OBJECTIVE

To develop a Radiological-Radiomics (R-R) combined model for differentiation between minimal invasive adenocarcinoma (MIA) and invasive adenocarcinoma (IA) of lung adenocarcinoma (LUAD) and evaluate its predictive performance.

METHODS

The clinical, pathological, and imaging data of a total of 509 patients (522 lesions) with LUAD diagnosed by surgical pathology from 2 medical centres were retrospectively collected, with 392 patients (402 lesions) from center 1 trained and validated using a five-fold cross-validation method, and 117 patients (120 lesions) from center 2 serving as an independent external test set. The least absolute shrinkage and selection operator (LASSO) method was utilized to filter features. Logistic regression was used to construct three models for predicting IA, namely, Radiological model, Radiomics model, and R-R model. Also, receiver operating curve curves (ROCs) were plotted, generating corresponding area under the curve (AUC), sensitivity, specificity, and accuracy.

RESULTS

The R-R model for IA prediction achieved an AUC of 0.918 (95 % CI: 0.889-0.947), a sensitivity of 80.3 %, a specificity of 88.2 %, and an accuracy of 82.1 % in the training set. In the validation set, this model exhibited an AUC of 0.906 (95 % CI: 0.842-0.970), a sensitivity of 79.9 %, a specificity of 88.1 %, and an accuracy of 81.8 %. In the external test set, the AUC was 0.894 (95 % CI: 0.824-0.964), a sensitivity of 84.8 %, a specificity of 78.6 %, and an accuracy of 83.3 %.

CONCLUSION

The R-R model showed excellent diagnostic performance in differentiating MIA and IA, which can provide a certain reference for clinical diagnosis and surgical treatment plans.

The diagnostic potential role of thioredoxin reductase and TXNRD1 in early lung adenocarcinoma: A cohort study

BACKGROUND

Lung adenocarcinoma (LUAD) is the primary form of lung cancer, yet the reliable biomarkers for early diagnosis remain insufficient. Thioredoxin reductase (TrxR) is strongly linked to the occurrence, development, and drug resistance of lung cancer, making it a potential biomarker. However, further research is required to assess its diagnostic value in LUAD.

METHODS

A retrospective analysis was performed on patients who underwent pulmonary nodule resection at our center from 2018 to 2022. Clinical data, including preoperative TrxR levels, imaging, and laboratory characteristics, were identified as study variables. Two prediction models were constructed using multiple logistic regression, and their prediction performance was evaluated comprehensively. Besides, bioinformatics analyses of TrxR coding genes including differential expression, functional enrichment, immune infiltration, drug sensitivity, and single-cell landscape were performed based on TCGA database, which were subsequently validated by Human Protein Atlas.

RESULTS

A total of 506 eligible patients (72 benign lesions, 77 AISs, 185 MIAs and 172 IACs) were identified in the clinical cohort. Two TrxR-based models were developed, which were able to distinguish between benign and malignant pulmonary nodules, as well as pathological subtypes of LUAD, respectively. The models exhibited good predictive ability with all AUC values ranging from 0.7 to 0.9. Based on calibration curves and clinical decision analysis, the nomogram models showed high reliability. Functional analysis indicated that TXNRD1 primarily participated in cell cycle and lipid metabolism. Immune infiltration analysis showed that TXNRD1 has a strong association with immune cells and could impact immunotherapy. Then, we identified small molecular compounds that inhibit TXNRD1 and confirmed TXNRD1 expression by single-cell landscape and immunohistochemistry.

CONCLUSION

This study validated the diagnostic value of TrxR and TXNRD1 in clinical cohorts and transcriptional data, respectively. TrxR and TXNRD1 could be used in the risk diagnosis of early LUAD and facilitate personalized treatment strategies.

Invasion and Grading of Pulmonary Non-Mucinous Adenocarcinoma

Lung adenocarcinoma staging and grading were recently updated to reflect the link between histologic growth patterns and outcomes. The lepidic growth pattern is regarded as "in-situ," whereas all other patterns are regarded as invasive, though with stratification. Solid, micropapillary, and complex glandular patterns are associated with worse prognosis than papillary and acinar patterns. These recent changes have improved prognostic stratification. However, multiple pitfalls exist in measuring invasive size and in classifying lung adenocarcinoma growth patterns. Awareness of these limitations and recommended practices will help the pathology community achieve consistent prognostic performance and potentially contribute to improved patient management.

Up-regulated ORC1 promotes lung adenocarcinoma by inhibiting ferroptosis via SLC7A11 dependent pathway

Background

Lung adenocarcinoma (LUAD) is a pulmonary malignant disease that poses a high risk of mortality and morbidity. Previous study indicated that ORC1 plays an oncogenic function. However, the precise regulatory function that ORC1 serves in the progression of LUAD is still not clearly known.

Methods

Bioinformatics analyses were performed using TCGA and GEO datasets. The human LUAD cell line NCIH1355, NCIH1568 as well as BEAS-2B cell line (human normal lung epithelial cell) were utilized for in vitro study. LUAD cell proliferation were determined via CCK-8 assays and RT-qPCR for ki-67. The relation of ORC1 and SLC7A11 was detected by Western blot and qPCR with or without sh-RNA. The expression level ACSL4, the biomarker of ferroptosis, were detected using RT-qPCR.

Results

ORC1 and SLC7A11 exhibit high expression levels in both LUAD patients and cell lines, and are strongly associated with poor prognosis. In vitro experiments demonstrate that ORC1 and SLC7A11 promote proliferation of LUAD cell lines while inhibiting gefitinib-induced ferroptosis. Additionally, the function of ORC1 in LUAD cells is dependent on SLC7A11.

Conclusion

ORC1 promotes LUAD cell proliferation and inhibits ferroptosis in a SLC7A11-dependent manner. This implies that ORC1 could potentially serve as a useful diagnosis biomarker and treatment target.

Identification of a prognosis-related gene signature and ceRNA regulatory networks in lung adenocarcinoma

The ceRNA network, consisting of both noncoding RNA and protein-coding RNA, governs the occurrence, progression, metastasis, and infiltration of lung adenocarcinoma. Signatures comprising multiple genes can effectively determine survival stratification and prognosis of patients with lung adenocarcinoma. To explore the mechanisms of lung adenocarcinoma progression and identify potential biological targets, we carried out systematic bioinformatics analyses of the genetic profiles of lung adenocarcinoma, such as weighted gene co-expression network analysis (WGCNA), differential expression (DE) assessment, univariate and multivariate Cox proportional hazard regression models, ceRNA modulatory networks generated using the ENCORI and miRcode databases, nomogram models, ROC curve assessment, and Kaplan-Meier survival curve analysis. The ceRNA network encompassed 37 nodes, comprising 12 mRNAs, 22 lncRNAs, and three miRNAs. Simultaneously, we performed integration analysis using the 12 genes from the ceRNA network. Our findings revealed that the signature established by these 12 genes serves as an adverse element in lung adenocarcinoma, contributing to unfavorable patient prognosis. To ensure the credibility of our results, we used in vitro experiments for further verification. In conclusion, our study delved into the potential mechanisms underlying lung adenocarcinoma via the ceRNA regulatory network, specifically focusing on the PIF1 and has-miR-125a-5p axis. Additionally, a signature comprising 12 genes was identified as a biomarker related to the prognosis of lung adenocarcinoma.

The self-distillation trained multitask dense-attention network for diagnosing lung cancers based on CT scans

BACKGROUND

The latest international multidisciplinary histopathological classification of lung cancer indicates that a deeper study of the lung adenocarcinoma requires a comprehensive multidisciplinary platform. However, in the traditional pathological examination or previous computer-vision-based research, the entire lung is not considered in a comprehensive manner.

PURPOSE

The study aims to develop a deep learning model proposed for diagnosing the lung adenocarcinoma histopathologically based on CT scans. Instead of just classifying the lung adenocarcinoma, the pathological report should be inferred based on both the invasiveness and growth pattern of the tumors.

METHODS

A self-distillation trained multitask dense-attention network (SD-MdaNet) is proposed and validated based on 2412 labeled CT scans from 476 patients and 845 unlabeled scans. Inferring the pathological report is divided into two tasks, predicting the invasiveness of the lung tumor and inferring growth patterns of tumor cells in a comprehensive histopathological subtyping manner with excellent accuracy. In the proposed method, the dense-attention module is introduced to better extract features from a small dataset in the main branch of the MdaNet. Next, task-specific attention modules are utilized in different branches and finally integrated as a multitask model. The second task is a blend of classification and regression tasks. Thus, a specialized loss function is developed. In the proposed knowledge distillation (KD) process, the MdaNet as well as its main branch trained for solving two single tasks, respectively, are treated as multiple teachers to produce a student model. A novel KD loss function is developed to take the advantage of all the models as well as data with labels and without labels.

RESULTS

SD-MdaNet achieves an AUC of98.7 ± 0.4 % $98.7\pm 0.4\%$ on invasiveness prediction, and91.6 ± 1.0 % $91.6\pm 1.0\%$ on predominant growth pattern prediction on our dataset. Moreover, the average mean squared error in inferring growth pattern proportion reaches0.0217 ± 0.0019 $0.0217\pm 0.0019$ , and the AUC for predominant growth pattern proportion reaches91.6 ± 1.0 % $91.6\pm 1.0\%$ . The proposed SD-MdaNet is significantly better than all other benchmarking methods (F D R < 0.05 $FDR<0.05$ ).

CONCLUSIONS

Experimental results demonstrate that the proposed SD-MdaNet can significantly improve the performance of the lung adenocarcinoma pathological diagnosis using only CT scans. Analyses and discussions are conducted to interpret the advantages of the SD-MdaNet.

CDC6 may serve as an indicator of lung adenocarcinoma prognosis and progression based on TCGA and GEO data mining and experimental analyses

Lung adenocarcinoma (LUAD) is one of the most lethal types of cancer worldwide, and accurately predicting patient prognosis is an important challenge. Gene prediction models, which are known for their simplicity and efficiency, have the potential to be used for prognostic predictions. However, the availability of models with true clinical value is limited. The present study integrated tissue sequencing and the clinical information of patients with LUAD from The Cancer Genome Atlas and Gene Expression Omnibus databases using bioinformatics. This comprehensive approach enabled the identification of 252 differentially expressed genes. Subsequently, univariate and multivariate Cox analyses were performed using these genes, and 14 and 3 genes [including cell division cycle 6 (CDC6), hyaluronan mediated motility receptor and STIL centriolar assembly protein] were selected for the construction of two prognostic models. Notably, the 3‑gene prognostic model exhibited a comparable predictive ability to that of the 14‑gene model. Functionally, pathway enrichment analysis revealed that CDC6 played a role in regulating the cell cycle and promoting tumor staging. To further investigate the relevance of CDC6, in vitro experiments involving the downregulation of CDC6 expression were conducted, which resulted in significant inhibition of tumor cell migration, invasion and proliferation. Moreover, in vivo experiments demonstrated that downregulating CDC6 expression significantly reduced the burden and metastasis of in situ lung tumors in mice. These findings suggested that CDC6 may be a critical gene involved in the development and prognosis of LUAD. In summary, the present study successfully constructed a simple yet accurate prognostic prediction model consisting of 3 genes. Additionally, the functional importance of CDC6 as a key gene in the model was identified. These findings lay a crucial foundation for further exploration of prognostic prediction models and a deeper understanding of the functional mechanisms of CDC6. Notably, these results have potential clinical implications for improving personalized treatment and prognosis evaluation for patients with LUAD.

Integrative Multianalytical Model Based on Novel Plasma Protein Biomarkers for Distinguishing Lung Adenocarcinoma and Benign Pulmonary Nodules

Given the pressing clinical problem of making a decision in diagnosis for subjects with pulmonary nodules, we aimed to discover novel plasma protein biomarkers for lung adenocarcinoma (LUAD) and benign pulmonary nodules (BPNs) and then develop an integrative multianalytical model to guide the clinical management of LUAD and BPN patients. Through label-free quantitative plasma proteomic analysis (data are available via ProteomeXchange with identifier PXD046731), 12 differentially expressed proteins (DEPs) in LUAD and BPN were screened. The diagnostic abilities of DEPs were validated in two independent validation cohorts. The results showed that the levels of three candidate proteins (PRDX2, PON1, and APOC3) were lower in the plasma of LUAD than in BPN. The three candidate proteins were combined with three promising computed tomography indicators (spiculation, vascular notch sign, and lobulation) and three traditional markers (CEA, CA125, and CYFRA21-1) to construct an integrative multianalytical model, which was effective in distinguishing LUAD from BPN, with an AUC of 0.904, a sensitivity of 81.44%, and a specificity of 90.14%. Moreover, the model possessed impressive diagnostic performance between early LUADs and BPNs, with the AUC, sensitivity, specificity, and accuracy of 0.868, 65.63%, 90.14%, and 82.52%, respectively. This model may be a useful auxiliary diagnostic tool for LUAD and BPN by achieving a better balance of sensitivity and specificity.

Updates on lung adenocarcinoma: invasive size, grading and STAS

Advancements in the classification of lung adenocarcinoma have resulted in significant changes in pathological reporting. The eighth edition of the tumour-node-metastasis (TNM) staging guidelines calls for the use of invasive size in staging in place of total tumour size. This shift improves prognostic stratification and requires a more nuanced approach to tumour measurements in challenging situations. Similarly, the adoption of new grading criteria based on the predominant and highest-grade pattern proposed by the International Association for the Study of Lung Cancer (IASLC) shows improved prognostication, and therefore clinical utility, relative to previous grading systems. Spread through airspaces (STAS) is a form of tumour invasion involving tumour cells spreading through the airspaces, which has been highly researched in recent years. This review discusses updates in pathological T staging, adenocarcinoma grading and STAS and illustrates the utility and limitations of current concepts in lung adenocarcinoma.

Drug resistance related genes in lung adenocarcinoma predict patient prognosis and influence the tumor microenvironment

Lung adenocarcinoma (LUAD) is the predominant type of non-small lung cancer (NSCLC) with strong invasive ability and poor prognosis. The drug resistance related genes are potentially associated with prognosis of LUAD. Our research aimed to identify the drug resistance related genes and explore their potential prognostic value in LUAD patients. The data used in this study were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Firstly, we screened drug resistance related genes in LUAD by differential gene analysis, univariate Cox regression and drug sensitivity analyses. Subsequently, we constructed a risk score model using LASSO Cox regression analysis, and verified whether the risk score can predict the survival of LUAD patients independent of other factors. Moreover, we explored the immune infiltration of 22 immune cells between high-risk and low-risk patients. Totally 10 drug-resistance positively related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) were identified in LUAD. The risk score model of LUAD constructed with these 10 genes could reliably predict the prognosis of LUAD patients. 18 pathways were significantly activated in high-risk group compared with low-risk group. In addition, the infiltration proportion of multiple immune cells was significantly different between high-risk and low-risk groups, and the proportion of M1 phagocytes was significantly higher in the high-risk group compared with the low-risk group. The drug resistance related genes (PLEK2, TFAP2A, KIF20A, S100P, GDF15, HSPB8, SASH1, WASF3, LAMA3 and TCN1) could predict the prognosis of LUAD patients. Clarifying the roles and mechanisms of these 10 genes in regulating drug resistance in LUAD will help to improve individualized clinical treatment protocols and predict patient sensitivity to treatment.

Transcriptomic analysis reveals the potential biological mechanism of AIS and lung adenocarcinoma

Introduction

Acute ischemic stroke (AIS) and lung adenocarcinoma (LUAD) are associated with some of the highest morbidity and mortality rates worldwide. Despite reports on their strong correlation, the causal relationship is not fully understood. The study aimed to identify and annotate the biological functions of hub genes with clinical diagnostic efficacy in AIS and LUAD.

Methods

Transcriptome and single-cell datasets were obtained from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). We identified the differentially expressed genes (DEGs) upregulated in AIS and LUAD and found 372 genes intersecting both datasets. Hub genes were identified using protein-protein interaction (PPI) networks, and the diagnostic and prognostic utility of these hub genes was then investigated using receiver operating characteristic (ROC) curves, survival analysis, and univariable Cox proportional hazard regression. Single-cell analysis was used to detect whether the hub genes were expressed in tumor epithelial cells. The immune microenvironment of AIS and LUAD was assessed using the CIBERSORT algorithm. The protein expression of these hub genes was tracked using the Human Protein Atlas (HPA). We calculated the number of positive cells using the digital pathology software QuPath. Finally, we performed molecular docking after using the Enrichr database to predict possible medicines.

Results

We identified the molecular mechanisms underlying hub genes in AIS and LUAD and found that CCNA2, CCNB1, CDKN2A, and CDK1 were highly expressed in AIS and LUAD tissue samples compared to controls. The hub genes were mainly involved in the following pathways: the cell cycle, cellular senescence, and the HIF-1 signaling pathway. Using immunohistochemical slices from the HPA database, we confirmed that these hub genes have a high diagnostic capability for AIS and LUAD. Further, their high expression is associated with poor prognosis. Finally, curcumin was tested as a potential medication using molecular docking modeling.

Discussion

Our findings suggest that the hub genes we found in this study contribute to the development and progression of AIS and LUAD by altering the cellular senescence pathway. Thus, they may be promising markers for diagnosis and prognosis.

Evolution of lung adenocarcinoma from preneoplasia to invasive adenocarcinoma

OBJECTIVE

Mutations in driver genes contribute to the development and progression of lung adenocarcinoma (LUAD). However, in the dynamic evolutionary process from adenocarcinoma in situ (AIS) to minimally invasive adenocarcinoma (MIA) and eventually to invasive adenocarcinoma (IAC), the role of driver genes is currently unclear. This study aimed to analyse the role of driver gene status in the progression of LUAD from preneoplasia to IAC.

METHODS

Patients with LUAD who underwent surgery in our centre from March 2015 to December 2019 were retrospectively analysed, and LUAD patients with tumour sizes ≤3.0 cm and pN0 were included in the final analysis. The mutation status of common driver genes, including EGFR, ALK and ROS1, was detected. According to the pathological characteristics, the patients were divided into three stages: AIS, MIA and IAC. We analysed the distribution of driver gene mutation frequencies across three stages of LUAD. In addition, we performed univariate and multivariate analyses of IAC patients to screen for relevant variables (driver genes and clinicopathological features) affecting their prognosis.

RESULTS

Ultimately, 759 patients with LUAD were enrolled, including 135, 130, and 494 cases of AIS, MIA, and IAC, respectively. EGFR mutations were identified in 359 (61.8%) patients, and with the transition from AIS to MIA, the frequency of EGFR mutations increased from 33.3% to 50.8%, p = 0.004, whereas the frequency of EGFR mutations was comparable for MIA and IAC (50.8% vs. 50.2%, p = 0.922). Moreover, ALK and ROS1 gene fusions were identified in 17 cases (2.2%) and 2 cases (3.0‰) respectively. For AIS, neither ALK gene nor ROS1 gene fusions were observed. When the tumour progressed to MIA, the ALK fusion frequency was 2.3% (3/130), which was basically consistent with the ALK fusion frequency of 2.8% in IAC, p = 0.143. For IAC, fusions of ROS1 fell into this category. In addition, we found that 40 patients (5.3%) developed metastasis/recurrence, and 14 patients (1.8%) died of cancer-specific related diseases. Notably, for AIS, there were no recurrences and no deaths, and for MIA, only 1 patient died with LUAD. Finally, survival analysis was performed in patients with stage IA invasive adenocarcinoma, and EGFR-mutant patients showed better DFS than EGFR-wild-type patients (p = 0.036). Conversely, patients with ALK fusions showed worse DFS than those with ALK wild-type (p = 0.004), and the same results were found in OS analysis.

CONCLUSIONS

The accumulation of EGFR driver gene mutation frequencies mediates the progression of LUAD from AIS to MIA. When the tumour progresses to stage IA invasive adenocarcinoma, multivariate analysis based on driver gene status can be used as a pivotal prognostic factor.

Systemic analysis of the DNA replication regulator origin recognition complex in lung adenocarcinomas identifies prognostic and expression significance

BACKGROUND

DNA replication alteration is a hallmark of patients with lung adenocarcinoma (LUAD) and is frequently observed in LUAD progression. Origin recognition complex (ORC) 1, ORC2, ORC3, ORC4, ORC5, and ORC6 form a replication-initiator complex to mediate DNA replication, which plays a key role in carcinogenesis, while their roles in LUAD remain poorly understood.

METHODS

The mRNA and protein expression of ORCs was confirmed by the GEPIA, HPA, CPTAC, and TCGA databases. The protein-protein interaction network was analyzed by the GeneMANIA database. Functional enrichment was confirmed by the Metascape database. The effects of ORCs on immune infiltration were validated by the TIMER database. The prognostic significance of ORCs in LUAD was confirmed by the KM-plot and GENT2 databases. DNA alteration and protein structure were determined in the cBioProtal and PDB databases. Moreover, the protein expression and prognostic value of ORCs were confirmed in our LUAD data sets by immunohistochemistry (IHC) staining.

RESULTS

ORC mRNA and protein were significantly increased in patients with LUAD compared with corresponding normal tissue samples. The results of IHC staining analysis were similar result to those of the above bioinformatics analysis. Furthermore, ORC1 and ORC6 had significant prognostic values for LUAD patients. Furthermore, the ORC cooperatively promoted LUAD development by driving DNA replication, cellular senescence, and metabolic processes.

CONCLUSION

The ORC, especially ORC1/6, has important prognostic and expression significance for LUAD patients.

Construction and Validation of a Novel Immune-Related Gene Pairs-Based Prognostic Model in Lung Adenocarcinoma

OBJECT

Focus on immune-related gene pairs (IRGPs) and develop a prognostic model to predict the prognosis of patients with lung adenocarcinoma (LUAD).

METHODS

First, the LUAD patient dataset was downloaded from The Cancer Genome Atlas database, and paired analysis of immune-related genes was subsequently conducted. Then, LASSO regression was used to screen prognostic IRGPs for building a risk prediction model. Meanwhile, the Gene Expression Omnibus database was used for external validation of the model. Next, the clinical predictive power of IRGPs features was assessed by uni-multivariate Cox regression analysis, the infiltration of key immune cells in high and low IRGPs risk groups was analyzed with CIBERSORT, quanTIseq, and Timer, and the key pathways enriched for IRGPs were assessed using the Kyoto Encyclopedia of Genes and Genomes. Finally, the expression and related functions of key immune cells and genes were verified by immunofluorescence and cell experiments of tissue samples.

RESULTS

It was revealed that the risk score of 19 IRGPs could be used as accurate indicators to evaluate the prognosis of LUAD patients, and the risk score was mainly related to T cell infiltration based on CIBERSORT analysis. Two genes of IRGPs, IL6, and CCL2, were found to be closely associated with the expression of PD-1/PD-L1 and the function of T-cells. Depending on the results of tissue immunofluorescence, IL6, CCL2, and T cells were highly expressed in the LUAD tissues of patients. Furthermore, IL6 and CCL2 were positively correlated with the expression of T cells. Besides, qRT-PCR assay in four different LUAD cells proved that IL6 and CCL2 were positively correlated with the expression of PD-L1 (P < .001).

CONCLUSIONS

Based on 19 IRGPs, an effective prognosis model was established to predict the prognosis of LUAD patients. In addition, IL6 and CCL2 are closely related to the function of T-cells.

Challenges of the eighth edition of the American Joint Committee on Cancer staging system for pathologists focusing on early stage lung adenocarcinoma

BACKGROUND

The eighth edition of the American Joint Committee on Cancer (AJCC) staging system for lung cancer adopts new criteria for tumor size, and for determining pTis, pT1a(mi), and pT1a. The latter is based on the size of stromal invasion. It is quite challenging for lung pathologists.

METHODS

All patients who had undergone surgical resection for pulmonary adenocarcinoma (ADC) at Chung Shan Medical University Hospital between January 2014 and April 2018 were reviewed, and restaged according to the eighth AJCC staging system. The clinical characteristics and survival of patients with tumor stage 0 (pTis), I or II were analyzed.

RESULTS

In total, 376 patients were analyzed. None of the pTis, pT1a(mi), or pT1a tumors recurred during the follow-up period up to 5 years, but pT1b, pT1c, pT2a, and pT2b tumors all had a few tumor recurrences (p < 0.0001). In addition, 95.2%, 100%, and 77.5% of pTis, pT1a(mi), and pT1a tumors, respectively, had tumor sizes ≤1.0 cm by gross examination. All pTis, pT1a(mi), and pT1a tumors exhibited only lepidic, acinar, or papillary patterns histologically.

CONCLUSIONS

This study demonstrated excellent survival for lung ADC patients with pTis, pT1a(mi), and pT1a tumors when completely excised. To reduce the inconsistencies between pathologists, staging lung ADC with tumors of ≤1 cm in size grossly as pTis, pT1a(mi), or pT1a may not be necessary when the tumors exhibit only lepidic, acinar, or papillary histological patterns. A larger cohort study with sufficient follow-up data is necessary to support this proposal.

The key role of microRNA-766 in the cancer development

Cancer is caused by defects in coding and non-coding RNAs. In addition, duplicated biological pathways diminish the efficacy of mono target cancer drugs. MicroRNAs (miRNAs) are short, endogenous, non-coding RNAs that regulate many target genes and play a crucial role in physiological processes such as cell division, differentiation, cell cycle, proliferation, and apoptosis, which are frequently disrupted in diseases such as cancer. MiR-766, one of the most adaptable and highly conserved microRNAs, is notably overexpressed in several diseases, including malignant tumors. Variations in miR-766 expression are linked to various pathological and physiological processes. Additionally, miR-766 promotes therapeutic resistance pathways in various types of tumors. Here, we present and discuss evidence implicating miR-766 in the development of cancer and treatment resistance. In addition, we discuss the potential applications of miR-766 as a therapeutic cancer target, diagnostic biomarker, and prognostic indicator. This may shed light on the development of novel therapeutic strategies for cancer therapy.

[Consensus on Postoperative Recurrence Prediction of Non-small Cell Lung Cancer Based on Molecular Markers]

Significant progress has been made in lung cancer screening, surgery, chemoradiation, targeted therapy, and immunotherapy recently. Surgical resection is the most important treatment for localized non-small cell lung cancer (NSCLC) so far, but there are still many patients who develop local recurrence or distant metastases within 5 years of surgery. Currently, the risk factors of recurrence in patients with NSCLC are mainly based on clinical and pathological features, which hardly identify patients at high risk of recurrence accurately. With the development of new detection technologies, a number of molecular markers that may have a predictive risk of recurrence in NSCLC have been discovered over the years. In order to summarize the molecular markers related to postoperative recurrence in NSCLC patients, we have formulated a consensus on the prediction of postoperative recurrence of NSCLC based on molecular markers. This consensus mainly focuses on the early stage NSCLC patients, discusses and summarizes the risk factors of disease recurrence from the molecular level. It is hoped that more and more valuable information can be provided for the management of patients, so as to provide more guidance for the perioperative management of the patients with early stage NSCLC in the future.
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Folate receptor-positive circulating tumor cells in the preoperative diagnosis of indeterminate pulmonary nodules

BACKGROUND

The use of FR + CTC to distinguish lung cancer from benign lung disease has been well studied. However, the effective method to differentiate precursor glandular lesions from benign/malignant pulmonary diseases is rare.

METHODS

380 patients with indeterminate pulmonary nodules were prospectively recruited. Peripheral blood samples were collected from all participants before surgery for analyzing FR + CTC levels. The performance of FR + CTC to identify lung precursor lesions were analyzed by receiver operating characteristic (ROC) curve.

RESULTS

FR + CTC can effectively differentiate precursor from benign pulmonary diseases in all included patients (cutoff: 9.22 FU/3 ml, AUC = 0.807, (p < 0.0001, sensitivity: 69.17%, specificity: 82.46%) and patients with single pulmonary lesion (cutoff: 9.03 FU/3 ml, AUC = 0.842, p = 0.0001, sensitivity: 75.20%, specificity: 83.00%). However, FR + CTC cannot differentiate precursor from benign pulmonary diseases in multiple lesions patients (p = 0.110). FR + CTC neither differentiate precursor from malignant pulmonary lesions in all included patients (p = 0.715), single nor multiple lesions patients (p = 0.867, p = 0.692, respectively). Total number of pulmonary nodules, MTD, location (lower vs upper) were independent risk factors for malignancy (AOR, 95% CI: 3.104 (1.525, 6.316), 3.148 (1.722, 5.754), 2.098 (1.132, 3.888), respectively.

CONCLUSION

Preoperative FR + CTC can be identified in precursor glandular lesions and utilized to differentiate from benign pulmonary diseases. Total number of pulmonary nodules, MTD, location (lower vs upper) were independent risk factors for malignancy.

Assessing the Prognostic Capability of Immune-Related Gene Scoring Systems in Lung Adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the commonest of the subtypes of lung cancer histologically. For this study, we intended to analyze the expression profiling of the immune-related genes (IRGs) from an independently available public database and developed a potent signature predictive of patients' prognosis.

Methods

Gene expression profiles and the clinical data of lung adenocarcinoma were gathered from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA), and the obtained data were split into a training set (n = 226), test set (n = 83), and validation set (n = 400). IRGs were then gathered from the ImmPort database. A prognostic model was constructed by analyzing the training set. Then the GO and KEGG analysis was performed, and a gene correlation prognostic nomogram was constructed. Finally, external validation, such as immune infiltration and immunohistochemistry, was performed.

Results

The 110 genes were significant by univariate Cox regression analysis and randomized survival forest algorithm for the training set and showed a good distinction between the low-risk-score and high-risk-score groups in the training set (P < 0.0001) by screening for four prognosis-related genes (HMOX1, ARRB1, ADM, PDIA3) and validated by the test set GSE30219 (P=0.0025) and TCGA dataset (P=0.00059). Multivariate Cox showed that the four gene signatures were an individual risk factor for LUAD. In addition, the genes in the signatures were externally verified using an online database. In particular, PDIA3 and HMOX1 are essential genes in the prognostic nomogram and play an important role in the model of immune-related genes.

Conclusion

Four immune-related genetic signatures are reliable prognostic indicators for patients with LUAD, providing a relevant theoretical basis and therapeutic rationale for immunotherapy.

Combined Radiomic and Visual Assessment for Improved Detection of Lung Adenocarcinoma Invasiveness on Computed Tomography Scans: A Multi-Institutional Study

Objective

The timing and nature of surgical intervention for semisolid abnormalities are dependent upon distinguishing between adenocarcinoma-in-situ (AIS), minimally invasive adenocarcinoma (MIA), and invasive adenocarcinoma (INV). We sought to develop and evaluate a quantitative imaging method to determine invasiveness of small, ground-glass lesions on computed tomography (CT) chest scans.

Methods

The study comprised 268 patients from 4 institutions with resected (<=3 cm) semisolid lesions with confirmed histopathological diagnosis of MIA/AIS or INV. A total of 248 radiomic texture features from within the tumor nodule (intratumoral) and adjacent to the nodule (peritumoral) were extracted from manually annotated lung nodules of chest CT scans. The datasets were randomly divided, with 40% of patients used for training and 60% used for testing the machine classifier (Training D_Train, N=106; Testing, D_Test, N=162).

Results

The top five radiomic stable features included four intratumoral (Laws and Haralick feature families) and one peritumoral feature within 3 to 6 mm of the nodule (CoLlAGe feature family), which successfully differentiated INV from MIA/AIS nodules with an AUC of 0.917 [0.867-0.967] on D_Train and 0.863 [0.79-0.931] on D_Test. The radiomics model successfully differentiated INV from MIA cases (<1 cm AUC: 0.76 [0.53-0.98], 1-2 cm AUC: 0.92 [0.85-0.98], 2-3 cm AUC: 0.95 [0.88-1]). The final integrated model combining the classifier with the radiologists’ score gave the best AUC on D_Test (AUC=0.909, p<0.001).

Conclusions

Addition of advanced image analysis via radiomics to the routine visual assessment of CT scans help better differentiate adenocarcinoma subtypes and can aid in clinical decision making. Further prospective validation in this direction is warranted.

Comparison of outcomes following lobectomy, segmentectomy, and wedge resection based on pathological subtyping in patients with pN0 invasive lung adenocarcinoma ≤1 cm

PURPOSE

We sought to analyze the prognostic significance of lung adenocarcinoma classification for patients with pathological N0 (pN0) lung invasive adenocarcinomas ≤1 cm who underwent surgical resection and investigate the optimal surgical procedure according to lung adenocarcinoma classification.

METHODS

A total of 1409 consecutive patients with resected pN0 invasive lung adenocarcinoma ≤1 cm were retrospectively reviewed. Comprehensive histologic subtyping was determined according to IASLC/ATS/ERS lung adenocarcinoma classification. Recurrence-free survival (RFS) and overall survival (OS) were compared between patients receiving lobectomy, segmentectomy, and wedge resection.

RESULTS

RFS and OS favored lobectomy and segmentectomy compared with wedge resection in the entire cohort. Five-year RFS rates were 100%, 98.2%, 97.3%, 77.8%, and 82.8% (p < 0.001) for lepidic, acinar, papillary, micropapillary, and solid predominant subtypes, while 5-year OS rates were 100%, 98.4%, 98.1%, 88.9%, and 96.5% (p < 0.001), respectively. Multivariate analysis showed that adenocarcinoma predominant pathological subtype and CT appearance were independent prognostic factors for RFS, and surgical procedure was independent factor for both RFS and OS. Specifically, wedge resection showed worse survival compared with anatomical resection in patients with papillary, micropapillary, or solid predominant subtypes, whereas in patients with lepidic predominant and acinar predominant subtypes, wedge resection showed comparable RFS with anatomical resection.

CONCLUSIONS

Anatomical resection showed better survival for patients with pN0 invasive lung adenocarcinoma ≤1 cm. For patients with invasive adenocarcinoma ≤1 cm in whom anatomical resection is not feasible, wedge resection could provide similar oncological effect when tumor is lepidic predominant or acinar predominant.

Identification and evaluation of circulating small extracellular vesicle microRNAs as diagnostic biomarkers for patients with indeterminate pulmonary nodules

Background

The identification of indeterminate pulmonary nodules (IPNs) following a low-dose computed tomography (LDCT) is a major challenge for early diagnosis of lung cancer. The inadequate assessment of IPNs’ malignancy risk results in a large number of unnecessary surgeries or an increased risk of cancer metastases. However, limited studies on non-invasive diagnosis of IPNs have been reported.

Methods

In this study, we identified and evaluated the diagnostic value of circulating small extracellular vesicle (sEV) microRNAs (miRNAs) in patients with IPNs that had been newly detected using LDCT scanning and were scheduled for surgery. Out of 459 recruited patients, 109 eligible patients with IPNs were enrolled in the training cohort (n = 47) and the test cohort (n = 62). An external cohort (n = 99) was used for validation. MiRNAs were extracted from plasma sEVs, and assessed using Small RNA sequencing. 490 lung adenocarcinoma samples and follow-up data were used to investigate the role of miRNAs in overall survival.

Results

A circulating sEV miRNA (CirsEV-miR) model was constructed from five differentially expressed miRNAs (DEMs), showing 0.920 AUC in the training cohort (n = 47), and further identified in the test cohort (n = 62) and in an external validation cohort (n = 99). Among five DEMs of the CirsEV-miR model, miR-101-3p and miR-150-5p were significantly associated with better overall survival (p = 0.0001 and p = 0.0069). The CirsEV-miR scores were calculated, which significantly correlated with IPNs diameters (p < 0.05), and were able to discriminate between benign and malignant PNs (diameter ≤ 1 cm). The expression patterns of sEV miRNAs in the benign, adenocarcinoma in situ/minimally invasive adenocarcinoma, and invasive adenocarcinoma subgroups were found to gradually change with the increase in aggressiveness for the first time. Among all DEMs of the three subgroups, five miRNAs (miR-30c-5p, miR-30e-5p, miR-500a-3p, miR-125a-5p, and miR-99a-5p) were also significantly associated with overall survival of lung adenocarcinoma patients.

Conclusions

Our results indicate that the CirsEV-miR model could help distinguish between benign and malignant PNs, providing insights into the feasibility of circulating sEV miRNAs in diagnostic biomarker development.

Trial registration: Chinese Clinical Trials: ChiCTR1800019877. Registered 05 December 2018, https://www.chictr.org.cn/showproj.aspx?proj=31346.

Graphical Abstract

Supplementary Information

The online version contains supplementary material available at 10.1186/s12951-022-01366-0.

Machine vision-assisted identification of the lung adenocarcinoma category and high-risk tumor area based on CT images

Computed tomography (CT) is a widely used medical imaging technique. It is important to determine the relationship between CT images and pathological examination results of lung adenocarcinoma to better support its diagnosis. In this study, a bilateral-branch network with a knowledge distillation procedure (KDBBN) was developed for the auxiliary diagnosis of lung adenocarcinoma. KDBBN can automatically identify adenocarcinoma categories and detect the lesion area that most likely contributes to the identification of specific types of adenocarcinoma based on lung CT images. In addition, a knowledge distillation process was established for the proposed framework to ensure that the developed models can be applied to different datasets. The results of our comprehensive computational study confirmed that our method provides a reliable basis for adenocarcinoma diagnosis supplementary to the pathological examination. Meanwhile, the high-risk area labeled by KDBBN highly coincides with the related lesion area labeled by doctors in clinical diagnosis.

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We study machine vision-assisted lung adenocarcinoma classification using CT images

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We design a holistic machine vision framework, improving classification performance

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Our method outperforms famous deep CNNs and medical imaging classification methods

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Our method better explains relations between CT patterns and pathological diagnoses

Lung adenocarcinoma is the most common type of lung cancer; therefore, its early diagnosis is crucial. In this study, we develop a holistic machine vision framework to automatically analyze CT images and identify the lung adenocarcinoma category with impressive performance. Our developed method can provide a reliable supplementary basis for adenocarcinoma diagnosis in clinical settings and can be used to label high-risk areas in CT images so that the relationship between CT characteristics and pathological diagnosis can be determined. Our method can potentially be used as an artificial intelligence (AI) system for adenocarcinoma identification using CT images, which will upgrade adenocarcinoma identification from the traditional expert-based evidence investigation to an automated AI-assisted paradigm.

A Diagnostic Model With IgM Autoantibodies and Carcinoembryonic Antigen for Early Detection of Lung Adenocarcinoma

Immunoglobulin M (IgM) autoantibodies, as the early appearing antibodies in humoral immunity when stimulated by antigens, might be excellent biomarkers for the early detection of lung cancer (LC). We aimed to develop a multi-analyte integrative model combining IgM autoantibodies and a traditional tumor biomarker that could be a valuable and powerful auxiliary diagnostic tool and might improve the accuracy of early detection of lung adenocarcinoma (LUAD). A customized protein array based on cancer driver genes was constructed and applied in the discovery cohort consisting of 68 LUAD patients and 68 normal controls (NCs); 31 differentially expressed IgM autoantibodies were identified. The top 5 candidate IgM autoantibodies [based on the area under the receiver operating characteristic curve (AUC) ranking], namely, TSHR, ERBB2, survivin, PIK3CA, and JAK2, were validated in the validation cohort using enzyme-linked immunosorbent assay (ELISA), which included 147 LUAD samples, 72 lung squamous cell carcinoma (LUSC) samples, 44 small cell lung carcinoma (SCLC) samples, and 147 NCs. These indicators presented diagnostic capacity for LUAD, with AUCs of 0.599, 0.613, 0.579, 0.601, and 0.633, respectively (p < 0.05). However, none of them showed a significant difference between the SCLC and NC groups, and only the IgM autoantibody against JAK2 showed a higher expression in LUSC than in NC (p = 0.046). Through logistic regression analysis, with the five IgM autoantibodies and carcinoembryonic antigen (CEA), one diagnostic model was constructed for LUAD. The model yielded an AUC of 0.827 (sensitivity = 56.63%, specificity = 93.98%). The diagnostic efficiency was superior to that of either CEA (AUC = 0.692) or IgM autoantibodies alone (AUC = 0.698). Notably, the accuracy of this model in early-stage LUAD reached 83.02%. In conclusion, we discovered and identified five novel IgM indicators and developed a multi-analyte model combining IgM autoantibodies and CEA, which could be a valuable and powerful auxiliary diagnostic tool and might improve the accuracy of early detection of LUAD.

Comprehensive Analysis Uncovers Prognostic and Immunogenic Characteristics of Cellular Senescence for Lung Adenocarcinoma

Cellular senescence plays a crucial role in tumorigenesis, development and immune modulation in cancers. However, to date, a robust and reliable cellular senescence-related signature and its value in clinical outcomes and immunotherapy response remain unexplored in lung adenocarcinoma (LUAD) patients. Through exploring the expression profiles of 278 cellular senescence-related genes in 936 LUAD patients, a cellular senescence-related signature (SRS) was constructed and validated as an independent prognostic predictor for LUAD patients. Notably, patients with high SRS scores exhibited upregulation of senescence-associated secretory phenotype (SASP) and an immunosuppressive phenotype. Further analysis showed that SRS combined with immune checkpoint expression or TMB served as a good predictor for patients’ clinical outcomes, and patients with low SRS scores might benefit from immunotherapy. Collectively, our findings demonstrated that SRS involved in the regulation of the tumor immune microenvironment through SASP was a robust biomarker for the immunotherapeutic response and prognosis in LUAD.

Discriminating invasive adenocarcinoma among lung pure ground-glass nodules: a multi-parameter prediction model

Background

Patients with consistent lung pure ground-glass nodules (pGGNs) have a high incidence of lung adenocarcinoma that can be classified as adenocarcinoma in situ (AIS), minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IAC). Regular follow-up is recommended for AIS and MIA, while surgical resection should be considered for IAC. This study sought to develop a multi-parameter prediction model to increase the diagnostic accuracy in discriminating between IAC and AIS or MIA.

Methods

The training data set comprised consecutive patients with lung pGGNs who underwent resection from January to December 2017 at the Zhongshan Hospital. Of the 370 resected pGGNs, 344 were pathologically confirmed to be AIS, MIA, or IAC and were included in the study. The 26 benign pGGNs were excluded. We compared differences in the clinical features (e.g., age and gender), the content of serum tumor biomarkers, the computed tomography (CT) parameters (e.g., nodule size and the maximal CT value), and the morphologic characteristics of nodules (e.g., lobulation, spiculation, pleura indentation, vacuole sign, and normal vessel penetration or abnormal vessel) between the pathological subtypes of AIS, MIA, and IAC. An abnormal vessel was defined as “vessel curve” or “vessel enlargement”. Statistical analyses were performed using the chi-square test, analysis of variance (ANOVA), and rank test. The IAC prediction model was constructed via a multivariate logistical regression. Our prediction model for lung pGGNs was further validated in a data set comprising consecutive patients from multiple medical centers in China from July to December 2018. In total, 345 resected pGGNs were pathologically diagnosed as lung adenocarcinoma in the validation data set.

Results

In the training data set, patients with pGGNs ≥10 mm in size had a high incidence (74.5%) of IAC. The maximal CT value of IAC [–416.1±121.2 Hounsfield unit (HU)] was much higher than that of MIA (–507.7±138.0 HU) and AIS (–602.6±93.3 HU) (P<0.001). IAC was more common in pGGNs that displayed any of the following CT manifestations: lobulation, spiculation, pleura indentation, vacuole sign, and vessel abnormality. The IAC prediction model was constructed using the parameters that were assessed as risk factors (i.e., the nodule size, maximal CT value, and CT signs). The receiver operating characteristic (ROC) analysis showed that the area under the curve (AUC) of this model for diagnosing IAC was 0.910, which was higher than that of the AUC for nodule size alone (0.891) or the AUC for the maximal CT value alone (0.807) (P<0.05, respectively). A multicenter validation data set was used to validate the performance of our prediction model in diagnosing IAC, and our model was found to have an AUC of 0.883, which was higher than that of the AUC of 0.827 for the module size alone model or the AUC of 0.791 for the maximal CT value alone model (P<0.05, respectively).

Conclusions

Our multi-parameter prediction model was more accurate at diagnosing IAC than models that used only nodule size or the maximal CT value alone. Thus, it is an efficient tool for identifying the IAC of malignant pGGNs and deciding if surgery is needed.

Exploration of the prognostic signature reflecting tumor microenvironment of lung adenocarcinoma based on immunologically relevant genes

Lung adenocarcinoma (LUAD) represents the major histological type of lung cancer with high mortality globally. Due to the heterogeneous nature, the same treatment strategy to various patients may result in different therapeutic responses. Hence, we aimed to elaborate an effective signature for predicting patient survival outcomes. The TCGA-LUAD cohort from the TCGA portal was used as a training dataset. The GSE26939 and GSE68465 cohorts from the GEO database were taken as validation datasets. All immunologically relevant genes were extracted from the ImmPort. The ESTIMATE algorithm was employed to explore LUAD microenvironment in the training dataset. Further, the DEGs were picked out based on the immune-associated genes reflecting different statuses in the immune context of TME. Univariate/multivariate Cox regression was performed to determine six prognosis- specific genes (PIK3CG, BTK, VEGFD, INHA, INSL4, and PTPRC) and established a risk predictive signature. The time-dependent ROC indicated that AUC values were all greater than 0.70 at 1-, 3-, and 5- year intervals. Corresponding RiskScore of each LUAD patient was calculated from the signature, and they were stratified into the high- and low-risk groups by the median value of RiskScore. K-M curves and Log-rank test demonstrated significant survival differences between the two groups (P < 0.05). Similar results were exhibited in the validation datasets. The RiskScore was incredibly relevant to clinicopathological factors like gender, AJCC stage, and T stage. Also, it can mirror the distribution state of 15 kinds of TIICs and have some predictive value for the sensitivity of therapeutic drugs.

Mechanisms of Progression and Heterogeneity in Multiple Nodules of Lung Adenocarcinoma

Lung cancer remains the leading cause of cancer-related death worldwide. Lung adenocarcinoma (LUAD) is thought to be caused by precursor lesions of atypical adenoma-like hyperplasia and may have extensive in situ growth before infiltration. To explore the relevant factors in heterogeneity and evolution of lung adenocarcinoma subtypes, the authors perform single-cell RNA sequencing (scRNA-seq) on tumor and normal tissue from five multiple nodules' LUAD patients and conduct a thorough gene expression profiling of cancer cells and cells in their microenvironment at single-cell level. This study gives a deep understanding of heterogeneity and evolution in early glandular neoplasia of the lung. This dataset leads to discovery of the changes in the immune microenvironment during the development of LUAD, and the development process from adenocarcinoma in situ (AIS) to invasive adenocarcinoma (IAC). This work sheds light on the direction of early tumor development and whether they are homologous.

Long-Term Prognosis of Patients With Resected Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma of the Lung

INTRODUCTION

The WHO classification of lung tumors defines adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) as cancers with no or limited histologic invasive components. The probability of patients with AIS or MIA being recurrence free for 5 years postoperatively has been found to be 100%. This study aimed to analyze the prognosis of patients with AIS or MIA after more than 5 postoperative years.

METHODS

We reviewed the pathologic findings of 4768 patients who underwent resection for lung cancer between 1998 and 2010. Of these, 524 patients with curative resection for AIS (207 cases, 39.5%) and MIA (317 cases, 60.5%) were included. Postoperative recurrence, survival, and development of secondary primary lung cancer (SPLC) were analyzed.

RESULTS

Of the included patients, 342 (65.3%) were of female sex, 333 (63.5%) were nonsmokers, and 229 (43.7%) underwent sublobar resection. Average pathologic total tumor diameter was 15.2 plus or minus 5.5 mm. Median postoperative follow-up period was 100 months (range: 1-237). No recurrence of lung cancer was observed for either AIS or MIA cases. Estimated 10-year postoperative disease-specific survival rates were 100% and 100% (p = 0.72), and overall survival rates were 95.3% and 97.8% (p = 0.94) for AIS and MIA cases, respectively. Estimated incidence rates of metachronous SPLC at 10 years after surgery were 5.6% and 7.7% for AIS and MIA, respectively (p = 0.45), and these were not correlated with the EGFR mutation status.

CONCLUSIONS

Although the development of metachronous SPLC should be noted, the risk of recurrence is quite low at more than 5 years after resection of AIS and MIA. This finding strengthens the clinical value of distinguishing AIS and MIA from other adenocarcinomas of the lung.

Validation Study of the International Association for the Study of Lung Cancer Histologic Grading System of Invasive Lung Adenocarcinoma

INTRODUCTION

A histologic grading system for invasive lung adenocarcinoma (ADC) has been proposed by the International Association for the Study of Lung Cancer (IASLC) Pathology Committee in June 2020. This study evaluated the prognostic value of the IASLC histologic grading system (the IASLC system) in a large Japanese cohort.

METHODS

We performed comprehensive histologic subtyping using the semiquantitative estimation of five major patterns and complex glandular patterns in patients with a completely resected lung ADC and determined the histologic grade using the IASLC system. Concordance index and receiver-operating characteristic curves were used to evaluate the clinical utility of the IASLC system for recurrence and death; the comparison was performed with the architectural-pattern system (the Arch system) and the grading system on the basis of the two most predominant patterns (the Sica's system).

RESULTS

Of 1002 patients with invasive ADC, 235 had recurrent disease and 166 died of lung cancer. The concordance index and area under the curve of the IASLC system were 0.777 and 0.807 for recurrence and 0.767 and 0.776 for death, respectively. These were similar to those of the Arch system (0.763 and 0.796 for recurrence, 0.743 and 0.755 for death) and the Sica's system (0.786 and 0.814 for recurrence, 0.762 and 0.773 for death).

CONCLUSIONS

We reported that the IASLC system for invasive lung ADC has prognostic significance by evaluating a large Japanese cohort. We believe that the IASLC grading system will provide physicians with better information for postsurgery treatment.

Development and Validation a Nomogram Incorporating CT Radiomics Signatures and Radiological Features for Differentiating Invasive Adenocarcinoma From Adenocarcinoma In Situ and Minimally Invasive Adenocarcinoma Presenting as Ground-Glass Nodules Measuring 5-10mm in Diameter

Purpose

To develop and validate a nomogram for differentiating invasive adenocarcinoma (IAC) from adenocarcinoma in situ (AIS) and minimally invasive adenocarcinoma (MIA) presenting as ground-glass nodules (GGNs) measuring 5-10mm in diameter.

Materials and Methods

This retrospective study included 446 patients with 478 GGNs histopathologically confirmed AIS, MIA or IAC. These patients were assigned to a primary cohort, an internal validation cohort and an external validation cohort. The segmentation of these GGNs on thin-slice computed tomography (CT) were performed semi-automatically with in-house software. Radiomics features were then extracted from unenhanced CT images with PyRadiomics. Radiological features of these GGNs were also collected. Radiomics features were investigated for usefulness in building radiomics signatures by spearman correlation analysis, minimum redundancy maximum relevance (mRMR) feature ranking method and least absolute shrinkage and selection operator (LASSO) classifier. Multivariable logistic regression analysis was used to develop a nomogram incorporating the radiomics signature and radiological features. The performance of the nomogram was assessed with discrimination, calibration, clinical usefulness and evaluated on the validation cohorts.

Results

Five radiomics features remained after features selection. The model incorporating radiomics signatures and four radiological features (bubble-like appearance, tumor-lung interface, mean CT value, average diameter) showed good calibration and good discrimination with AUC of 0.831(95%CI, 0.772~0.890). Application of the nomogram in the internal validation cohort with AUC of 0.792 (95%CI, 0.712~0.871) and in the external validation cohort with AUC of 0.833 (95%CI, 0.729-0.938) also indicated good calibration and good discrimination. The decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusion

This study presents a nomogram incorporating the radiomics signatures and radiological features, which can be used to predict the risk of IAC in patients with GGNs measuring 5-10mm in diameter individually.

Predicting benign, preinvasive, and invasive lung nodules on computed tomography scans using machine learning

OBJECTIVE

The study objective was to investigate if machine learning algorithms can predict whether a lung nodule is benign, adenocarcinoma, or its preinvasive subtype from computed tomography images alone.

METHODS

A dataset of chest computed tomography scans containing lung nodules was collected with their pathologic diagnosis from several sources. The dataset was split randomly into training (70%), internal validation (15%), and independent test sets (15%) at the patient level. Two machine learning algorithms were developed, trained, and validated. The first algorithm used the support vector machine model, and the second used deep learning technology: a convolutional neural network. Receiver operating characteristic analysis was used to evaluate the performance of the classification on the test dataset.

RESULTS

The support vector machine/convolutional neural network-based models classified nodules into 6 categories resulting in an area under the curve of 0.59/0.65 when differentiating atypical adenomatous hyperplasia versus adenocarcinoma in situ, 0.87/0.86 with minimally invasive adenocarcinoma versus invasive adenocarcinoma, 0.76/0.72 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma, 0.89/0.87 atypical adenomatous hyperplasia + adenocarcinoma in situ versus minimally invasive adenocarcinoma + invasive adenocarcinoma, and 0.93/0.92 atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma. Classifying benign versus atypical adenomatous hyperplasia + adenocarcinoma in situ + minimally invasive adenocarcinoma versus invasive adenocarcinoma resulted in a micro-average area under the curve of 0.93/0.94 for the support vector machine/convolutional neural network models, respectively. The convolutional neural network-based methods had higher sensitivities than the support vector machine-based methods but lower specificities and accuracies.

CONCLUSIONS

The machine learning algorithms demonstrated reasonable performance in differentiating benign versus preinvasive versus invasive adenocarcinoma from computed tomography images alone. However, the prediction accuracy varies across its subtypes. This holds the potential for improved diagnostic capabilities with less-invasive means.

Molecular biomarkers in early stage lung cancer

Low dose computed tomography (LDCT) screening, together with the recent advances in targeted and immunotherapies, have shown to improve non-small cell lung cancer (NSCLC) survival. Furthermore, screening has increased the number of early stage-detected tumors, allowing for surgical resection and multimodality treatments when needed. The need for improved sensitivity and specificity of NSCLC screening has led to increased interest in combining clinical and radiological data with molecular data. The development of biomarkers is poised to refine inclusion criteria for LDCT screening programs. Biomarkers may also be useful to better characterize the risk of indeterminate nodules found in the course of screening or to refine prognosis and help in the management of screening detected tumors. The clinical implications of these biomarkers are still being investigated and whether or not biomarkers will be included in further decision-making algorithms in the context of screening and early lung cancer management still needs to be determined. However, it seems clear that there is much room for improvement even in early stage lung cancer disease-free survival (DFS) rates; thus, biomarkers may be the key to refine risk-stratification and treatment of these patients. Clinicians’ capacity to register, integrate, and analyze all the available data in both high risk individuals and early stage NSCLC patients will lead to a better understanding of the disease’s mechanisms, and will have a direct impact in diagnosis, treatment, and follow up of these patients. In this review, we aim to summarize all the available data regarding the role of biomarkers in LDCT screening and early stage NSCLC from a multidisciplinary perspective. We have highlighted clinical implications, the need to combine risk stratification, clinical data, radiomics, molecular information and artificial intelligence in order to improve clinical decision-making, especially regarding early diagnostics and adjuvant therapy. We also discuss current and future perspectives for biomarker implementation in routine clinical practice.

Radiomic signature based on CT imaging to distinguish invasive adenocarcinoma from minimally invasive adenocarcinoma in pure ground-glass nodules with pleural contact

Background

Pure ground-glass nodules (pGGNs) with pleural contact (P-pGGNs) comprise not only invasive adenocarcinoma (IAC), but also minimally invasive adenocarcinoma (MIA). Radiomics recognizes complex patterns in imaging data by extracting high-throughput features of intra-tumor heterogeneity in a non-invasive manner. In this study, we sought to develop and validate a radiomics signature to identify IAC and MIA presented as P-pGGNs.

Methods

In total, 100 patients with P-pGGNs (69 training samples and 31 testing samples) were retrospectively enrolled from December 2012 to May 2018. Imaging and clinical findings were also analyzed. In total, 106 radiomics features were extracted from the 3D region of interest (ROI) using computed tomography (CT) imaging. Univariate analyses were used to identify independent risk factors for IAC. The least absolute shrinkage and selection operator (LASSO) method with 10-fold cross-validation was used to generate predictive features to build a radiomics signature. Receiver-operator characteristic (ROC) curves and calibration curves were used to evaluate the predictive accuracy of the radiomics signature. Decision curve analyses (DCA) were also conducted to evaluate whether the radiomics signature was sufficiently robust for clinical practice.

Results

Univariate analysis showed significant differences between MIA (N = 47) and IAC (N = 53) groups in terms of patient age, lobulation signs, spiculate margins, tumor size, CT values and relative CT values (all P < 0.05). ROC curve analysis showed, when MIA was identified from IAC, that the critical value of tumor length diameter (TLD) was1.39 cm and the area under the ROC curve (AUC) was 0.724 (sensitivity = 0.792, specificity = 0.553). The critical CT value on the largest axial plane (CT-LAP) was − 597.45 HU, and the AUC was 0.666 (sensitivity = 0.698, specificity= 0.638). The radiomics signature consisted of seven features and exhibited a good discriminative performance between IAC and MIA, with an AUC of 0.892 (sensitivity = 0.811, specificity 0.719), and 0.862 (sensitivity = 0.625, specificity = 0.800) in training and testing samples, respectively.

Conclusions

Our radiomics signature exhibited good discriminative performance in differentiating IAC from MIA in P-pGGNs, and may offer a crucial reference point for follow-up and selective surgical management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s40644-020-00376-1.

Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma

Background

The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD.

Methods

The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts.

Results

A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design.

Conclusions

Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

A retrospective study of the relationship between the pathologic subtype and lymph node metastasis of lung adenocarcinomas of ≤3 cm diameter

To analyze the relationship between pathologic subtype and lymph node metastasis for lung adenocarcinomas of ≤3 cm diameter.We retrospectively studied 384 patients with operable lung adenocarcinomas of ≤3 cm diameter that had been radically resected by lobectomy or anatomic segmentectomy with systematic nodal dissection, at the Fujian Medical University Union Hospital between March 2014 and March 2016.Lymph node metastasis pN1 + pN2 (pN+) was found in 2 of 104 (1.9%) patients with tumor diameter ≤1.0 cm, 12 of 159 (7.5%) patients with tumor diameter >1.0 cm but ≤2.0 cm, and 35 of 121 (28.9%) patients with tumor size >2.0 cm but ≤3.0 cm (P < .01). Lymph node metastasis pN+ was found in 19 of 53 (35.8%) patients with visceral invasion pleural (VIP) and 30 of 331 (9.0%) patients without VIP (P < .05). It was also found in 16 of 51 (31.3%) patients with high serum CEA concentrations and 28 of 297 (9.4%) patients with normal concentrations (P < .05). In a multivariate analysis, tumor diameter, VIP, high serum CEA concentration, and pathologic subtype were significant risk factors. The prevalences of lymph node metastasis pN+ were: 0.0% (0/2), 0.0% (0/89), 3.2% (1/31), 16.2% (34/209), 7.7% (1/13), 46.7% (7/15), 100% (4/4), and 11.8% (2/17) for adenocarcinoma in situ (AIS); minimally invasive adenocarcinoma (MIA); predominantly lepidic (LEP), acinar (ACI), papillary, solid (SOL), and micropapillary (MIP) tumors; and variants of invasive adenocarcinoma, respectively (P < .05). For predominant SOL and MIP tumors, the prevalences of lymph node involvement were significantly higher than for the other subtypes.We have shown that lymph node metastasis in patients with tumor diameter ≤3 cm differs according to lung adenocarcinoma subtype. AIS and MIA were not associated with lymph node metastasis; therefore, systematic nodal dissection may be unnecessary. The prevalence of lymph node metastasis rate was low for LEP, suggesting that systemic lymph node sampling is sufficient. In contrast, for other pathologic subtypes, including SOL and MIP, systematic lymph node dissection should be performed.

LINC00467 promotes cell proliferation and stemness in lung adenocarcinoma by sponging miR-4779 and miR-7978

Lung adenocarcinoma (LAD), as one of the most common types of lung tumors, is lethal and malignant. Long noncoding RNAs (lncRNAs) play important roles in various cancers according to many previous studies. LINC00467 was proposed to be a tumor promoter. Despite the validated promotive effect of LINC00467 on neuroblastoma progression, its regulatory mechanism in LAD remains unclear. In this study, LINC00467 expressed higher in LAD tissues and cell lines, and increased LINC00467 indicated a poor prognosis. Knockdown of LINC00467 inhibited cell proliferation, the expressions of tumor stem cell-related genes, and cell spheroid formation ability, while it promoted cell apoptosis. miR-4779 and miR-7978 were reported to play antitumor roles in several cancers before. LINC00467 could combine with miR-4779 and miR-7978, and negatively regulated miR-4779 and miR-7978. miR-4779 and miR-7978 inhibitor could partly rescue the LINC00467 knockdown-induced influence on cell proliferation, apoptosis, and stemness. In a word, this study innovatively investigated the mechanism of LINC00467 in LAD and verified LINC00467 exerted its carcinogenesis function by sponging miR-4779 and miR-7978, which may become a catalyst for generating new therapeutic targets for LAD treatment.

Genetic profile of non-small cell lung cancer (NSCLC): A hospital-based survey in Jinhua

Background

We describe the clinical features, genetic profile, and their correlation in NSCLC patients.

Methods

A total of 256 Chinese patients with NSCLC were enrolled in this study. NGS‐based genomic profiling of major lung cancer‐related genes was performed on formalin‐fixed paraffin‐embedded tumor samples.

Results

Of 256 patients with NSCLC, 219 were adenocarcinoma and most of them were in the early stage. Among patients, 63.3% patients have more than two gene mutations. By analyzing variant allele frequency (VAF), we found that the median VAF has significant differences between squamous cell carcinoma and adenocarcinoma, as well as early stage and advanced stage. The frequency of mutations in EGFR, MET, and RET were significantly higher in nonsmokers than in smokers. Besides, Pearson correlation analysis found that ALK, BRAF, and MET mutations had a strong correlation with age. Notably, higher frequencies of ALK and BRAF alterations were associated with younger age, while more frequent MET mutations appear in the patients at age 55 or older.

Conclusion

More unique features of cancer driver genes in Chinese NSCLC were identified by next‐generation sequencing. These findings highlighted that it is necessary to carry out targeted detection according to different clinical features for NSCLC.

Radiological-pathological correlation of subsolid pulmonary nodules: A single centre retrospective evaluation of the 2011 IASLC adenocarcinoma classification system

INTRODUCTION

The 2011 IASLC classification system proposes guidelines for radiologists and pathologists to classify adenocarcinomas spectrum lesions as preinvasive, minimally invasive adenocarcinoma (MIA), or invasive adenocarcinoma (IA). IA portends the worst clinical prognosis, and the imaging distinction between MIA and IA is controversial.

MATERIALS AND METHODS

Subsolid pulmonary nodules resected by microcoil localization over a three-year period were retrospectively reviewed by three chest radiologists and a pulmonary pathologist. Nodules were classified radiologically based on preoperative computed tomography (CT), with the solid nodule component measured on mediastinal windows applied to high-frequency lung kernel reconstructions, and pathologically according to 2011 IASLC criteria. Radiology interobserver and radiological-pathological variability of nodule classification, and potential reasons for nodule classification discordance were assessed.

RESULTS

Seventy-one subsolid nodules in 67 patients were included. The average size of invasive disease focus at histopathology was 5 mm (standard deviation 5 mm). Radiology interobserver agreement of nodule classification was good (Cohen's Kappa = 0.604, 95 % CI: 0.447 to 0.761). Agreement between consensus radiological interpretation and pathological category was fair (Cohen's Kappa = 0.236, 95 % CI: 0.054-0.421). Radiological and pathological nodule classification were concordant in 52 % (37 of 71) of nodules. The IASLC proposed CT solid component cut-off of 5 mm to distinguish MIA and IA yielded a sensitivity of 59 % and specificity of 80 %. Common reasons for nodule classification discordance included multiple solid components within a nodule on CT, scar and stromal collapse at pathology, and measurement variability.

CONCLUSION

Solid component(s) within persistent part-solid pulmonary nodules raise suspicion for invasive adenocarcinoma. Preoperative imaging classification is frequently discordant from final pathology, reflecting interpretive and technical challenges in radiological and pathological analysis.

Comprehensive Analysis of Candidate Diagnostic and Prognostic Biomarkers Associated with Lung Adenocarcinoma

Background

We aimed to screen and identify central genetic and molecular targets involved in advancement of lung adenocarcinoma (LUAD) and to perform an integrated analysis and clinical validation.

Material/Methods

The GEO2R technique was utilized to assess differentially expressed genes (DEGs) among the gene sets GSE75037, GSE85716, and GSE118370. Subsequently, gene Ontology (GO) analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytical methods were executed to determine related biofunctions and signaling pathways, which were annotated with tools from the Database for Annotation, Visualization and Integrated Discovery (DAVID) resource. Then, a protein-protein interaction (PPI) network complex consisting of all detected DEGs was built with the STRING web interface. Cytohubba and MCODE plug-ins for Cytoscape software and Gene Expression Profiling Interactive Analysis (GEPIA) were employed to identify the hub genes. Finally, the mRNA expression of the identified hub genes was quantitatively validated by The Cancer Genome Atlas (TCGA) database analysis and real-time quantitative polymerase chain reaction (RT-qPCR).

Results

We screened 146 upregulated DEGs and 431 downregulated DEGs with the criteria of |logFC| >1 and P<0.05, and the GO analysis indicated that DEGs were implicated in mitotic nuclear division (biological process, BP), the nucleus (cellular component, CC), and protein binding (molecular function, MF) and were associated with multiple KEGG pathways, such as the p53 signaling pathway in cancer. Then, the top 8 genes that predicted significantly different outcomes in LUAD patients were filtered from the DEGs and selected as hub genes. The TCGA database analysis and RT-qPCR results demonstrated that these genes were differentially expressed with the same trends in LUAD tissues compared with normal tissues.

Conclusions

Overall, we propose that 8 genes (PECAM1, CDK1, MKI67, SPP1, TOP2A, CHEK1, CCNB1, and RRM2) might be novel hub genes strongly associated with the progression and prognosis of LUAD.

Oncologic outcomes of segmentectomy vs lobectomy in pathologic stage IA (≤2 cm) invasive lung adenocarcinoma: A population-based study

BACKGROUND AND OBJECTIVES

For early-stage invasive lung adenocarcinoma, it remains unclear whether segmentectomy can yield outcomes equivalent to those of lobectomy. This study aimed to compare survival outcomes after segmentectomy and lobectomy among patients with stage IA invasive lung adenocarcinoma.

METHODS

We identified patients with stage IA (≤2 cm) invasive lung adenocarcinoma who underwent segmentectomy or lobectomy from the Surveillance, Epidemiology, and End Results database (2004-2015). Propensity score matching (PSM) was used to balance the baseline characteristics. Overall survival (OS) and lung cancer-specific survival (LCSS) were compared using the Kaplan-Meier method and Cox proportional hazards regression.

RESULTS

A total of 5474 patients were included. Before PSM, the 5-year OS was 78.3% for patients undergoing lobectomy vs 76.5% for patients undergoing segmentectomy (P = .166) while LCSS were 86.8% vs 83.0% (P = .015). After PSM, survival analyses showed that segmentectomy had OS (75.8% vs 76.4%; P = .694) and LCSS (82.7% vs 82.9%; P = .604) equivalent to those of lobectomy. Cox regression demonstrated that segmentectomy was equivalent to lobectomy in terms of OS and LCSS before and after PSM.

CONCLUSION

For stage IA (≤2 cm) invasive lung adenocarcinoma, segmentectomy may have oncologic outcomes equivalent to those of lobectomy.

IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis

Background

Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported.

Methods

We first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models.

Results

Gene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR = 1.98, 95% CI 1.48 to 2.64, P = 4.19e−06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis.

Conclusions

In this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma.

Impact

The study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.

PRKCZ-AS1 promotes the tumorigenesis of lung adenocarcinoma via sponging miR-766-5p to modulate MAPK1

Lung adenocarcinoma (LUAD) is the most prevalent histological subclass of non-small cell lung cancer. Long non-coding RNAs (lncRNAs) have been recognized as the crucial regulatory factors in tumor development and progression. Nevertheless, limited research has been carried on the function of PRKCZ-AS1 in LUAD. In this study, the expression of PRKCZ-AS1 in LUAD tissues and cell lines was notably upregulated. Moreover, knockdown of PRKCZ-AS1 inhibited the proliferation and migration, but promoted apoptosis in LUAD cells. Furthermore, miR-766-5p could bind with PRKCZ-AS1. Besides, the expression miR-766-5p was negatively regulated by PRKCZ-AS1 expression in LUAD cells. Furtherly, PRKCZ-AS1 expression positively regulated the expression of MAPK1. Similarly, the expression of MAPK1 was negatively regulated by miR-766-5p expression. Moreover, the binding ability between miR-766-5p and MAPK1 was confirmed. Furthermore, knockdown of MAPK1 partly rescued the miR-766-5p inhibition-mediated promoting effect on proliferation and migration in LUAD cells transfected with PRKCZ-AS1#1. Overall, above results suggested that PRKCZ-AS1 promotes the occurrence of LUAD by sponging miR-766-5p to upregulate MAPK1 expression, which may provide new insights into LUAD treatment.

Low-depth whole genome sequencing reveals copy number variations associated with higher pathologic grading and more aggressive subtypes of lung non-mucinous adenocarcinoma

Objective

Histology grade, subtypes and TNM stage of lung adenocarcinomas are useful predictors of prognosis and survival. The aim of the study was to investigate the relationship between chromosomal instability, morphological subtypes and the grading system used in lung non-mucinous adenocarcinoma (LNMA).

Methods

We developed a whole genome copy number variation (WGCNV) scoring system and applied next generation sequencing to evaluate CNVs present in 91 LNMA tumor samples.

Results

Higher histological grades, aggressive subtypes and more advanced TNM staging were associated with an increased WGCNV score, particularly in CNV regions enriched for tumor suppressor genes and oncogenes. In addition, we demonstrate that 24-chromosome CNV profiling can be performed reliably from specific cell types (<100 cells) isolated by sample laser capture microdissection.

Conclusions

Our findings suggest that the WGCNV scoring system we developed may have potential value as an adjunct test for predicting the prognosis of patients diagnosed with LNMA.