Investigating the Clinico-Molecular and Immunological Evolution of Lung Adenocarcinoma Using Pseudotime Analysis

Construction and evaluation of a prognostic risk model of tumor metastasis-related genes in patients with non-small cell lung cancer

BACKGROUND

Lung cancer is a high-incidence cancer, and it is also the most common cause of cancer death worldwide. 80-85% of lung cancer cases can be classified as non-small cell lung cancer (NSCLC).

METHODS

NSCLC transcriptome data and clinical information were downloaded from the TCGA database and GEO database. Firstly, we analyzed and identified the differentially expressed genes (DEGs) between non-metastasis group and metastasis group of NSCLC in the TCGA database, Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) were consulted to explore the functions of the DEGs. Thereafter, univariate Cox regression and LASSO Cox regression algorithms were applied to identify prognostic metastasis-related signature, followed by the construction of the risk score model and nomogram for predicting the survival of NSCLC patients. GSEA analyzed that differentially expressed gene-related signaling pathways in the high-risk group and the low-risk group. The survival of NSCLC patients was analyzed by the Kaplan-Meier method. ROC curve was plotted to evaluate the accuracy of the model. Finally, the GEO database was further applied to verify the metastasis‑related prognostic signature.

RESULTS

In total, 2058 DEGs were identified. GO functions and KEGG pathways analysis results showed that the DEGs mainly concentrated in epidermis development, skin development, and the pathway of Neuro active ligand -receptor interaction in cancer. A six-gene metastasis-related risk signature including C1QL2, FLNC, LUZP2, PRSS3, SPIC, and GRAMD1B was constructed to predict the overall survival of NSCLC patients. The reliability of the gene signature was verified in GSE13213. The NSCLC patients were grouped into low-risk and high-risk groups based on the median value of risk scores. And low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for NSCLC.

CONCLUSION

Our study identified 6 metastasis biomarkers in the NSCLC. The biomarkers may contribute to individual risk estimation, survival prognosis.

Prognostic Significance of Pseudotime from Texture Parameters of FDG PET/CT in Locally Advanced Non-Small-Cell Lung Cancer with Tri-Modality Therapy

Simple Summary

Although texture parameters of F-18 fluorodeoxyglucose positron emission tomography/computed tomography images were known to associate tumor biology and clinical features, the types and implications of parameters are too various and complicated. To overcome the limitation of texture parameter, we attempted to produce a new simplified parameter from texture parameters of F-18 fluorodeoxyglucose positron emission tomography/computed tomography images in lung cancer patients using pseudotime analysis. Pseudotime analysis is a recently developed method to explore changes in cell or tissue characteristics based on transcriptomic expression. It is the first study to apply pseudotime analysis into radiomics dataset other than transcriptomics data. Herein, we demonstrated that pseudotime can be successfully estimated from texture parameters. In the aspect of prognostic prediction, pseudotime was an independent prognostic factor for overall survival in contrast to conventional parameters such as metabolic tumor volume and total lesion glycolysis. This study showed possibility of integrating various texture parameters into single parameter which reflects disease progression status. Pseudotime, as a concrete value of disease progression, is expected to be used in clinical field to evaluate disease and predict prognosis.

Abstract

Texture analysis provides image parameters from F-18 fluorodeoxyglucose positron emission tomography/computed tomography (FDG PET/CT). Although some parameters are associated with tumor biology and clinical features, the types and implications of these parameters are complicated. We applied pseudotime analysis, which has recently been used to estimate changes in individual sample characteristics, to texture parameters from FDG PET/CT images of locally advanced non-small-cell lung cancer (NSCLC) patients undergoing neoadjuvant concurrent chemoradiation therapy (CCRT) followed by surgery. Our subjects were 303 NSCLC patients who underwent pretherapeutic FDG PET/CT and tri-modality therapy. Texture parameters of the primary tumor were calculated from FDG PET/CT images acquired before neoadjuvant CCRT. Pseudotime analysis was performed using the PhenoPath tool. Clinicopathologic features including survival data were collected and survival analysis was performed to compare the prognostic significances of pseudotime parameters with those of conventional PET parameters. Pseudotime was successfully estimated from texture parameters. Normalized co-occurrence homogeneity, normalized co-occurrence inverse difference moment, and black–white symmetry showed positive correlations with pseudotime, short run emphasis, normalized co-occurrence dissimilarity, and short zone emphasis negative correlation. The maximum standardized uptake value (SUV) and mean SUV were not associated with overall survival. Pseudotime, metabolic tumor volume (MTV), and total lesion glycolysis (TLG) showed significant associations with overall survival. In contrast to MTV and TLG, pseudotime was an independent prognostic factor for overall survival. Various metabolic texture parameters can be integrated into a single parameter using pseudotime analysis. Pseudotime of the primary tumor, estimated from FDG PET/CT images, better predicts overall survival in locally advanced NSCLC patients treated with tri-modality therapy than conventional PET parameters.

Genomics Analysis and Nomogram Risk Prediction of Occult Lymph Node Metastasis in Non-Predominant Micropapillary Component of Lung Adenocarcinoma Measuring ≤ 3 cm

Background

The efficacy of sublobar resection and selective lymph node dissection is gradually being accepted by thoracic surgeons for patients within early-stage non-small cell lung cancer (NSCLC). Nevertheless, there are still some NSCLC patients develop lymphatic metastasis at clinical T1 stage. Lung adenocarcinoma with a micropapillary (MP) component poses a higher risk of lymph node metastasis and recurrence even when the MP component is not predominant. Our study aimed to explore the genetic features and occult lymph node metastasis (OLNM) risk factors in patients with a non-predominant micropapillary component (NP-MPC) in a large of patient’s cohort with surgically resected lung adenocarcinoma.

Methods

Between January 2019 and December 2021, 6418 patients who underwent complete resection for primary lung adenocarcinoma at the Qilu Hospital of Shandong University. In our study, 442 patients diagnosed with lung adenocarcinoma with NP-MPC with a tumor size ≤3 cm were included. Genetic alterations were analyzed using amplification refractory mutation system-polymerase chain reaction (ARMS-PCR). Abnormal protein expression of gene mutations was validated using immunohistochemistry. A nomogram risk model based on clinicopathological parameters was developed to predict OLNM. This model was invalidated using the calibration plot and concordance index.

Results

In our retrospective cohort, the incidence rate of the micropapillary component was 11.17%, and OLNM was observed in 20.13% of the patients in our study. ARMS-PCR suggested that EGFR exon 19 del was the most frequent alteration in NP-MCP patients compared with other gene mutations (frequency: 21.2%, P<0.001). Patients harboring exon 19 del showed significantly higher risk of OLNM (P< 0.001). A nomogram was developed based on five risk parameters, which showed good calibration and reliable discrimination ability (C-index = 0.84) for evaluating OLNM risk.

Conclusions.

Intense expression of EGFR exon 19 del characterizes lung adenocarcinoma in patients with NP-MCP and it’s a potential risk factor for OLNM. We firstly established a nomogram based on age, CYFRA21-1 level, tumor size, micropapillary and solid composition, that was effective in predicting OLNM among NP-MCP of lung adenocarcinoma measuring ≤ 3 cm.