Genomic landscape of allelic imbalance in premalignant atypical adenomatous hyperplasias of the lung

Advancements in the diagnosis and treatment of sub‑centimeter lung cancer in the era of precision medicine (Review)

Lung cancer is the malignancy with the highest global mortality rate and imposes a substantial burden on society. The increasing popularity of lung cancer screening has led to increasing number of patients being diagnosed with pulmonary nodules due to their potential for malignancy, causing considerable distress in the affected population. However, the diagnosis and treatment of sub-centimeter grade pulmonary nodules remain controversial. The evolution of genetic detection technology and the development of targeted drugs have positioned the diagnosis and treatment of lung cancer in the precision medicine era, leading to a marked improvement in the survival rate of patients with lung cancer. It has been established that lung cancer driver genes serve a key role in the development and progression of sub-centimeter lung cancer. The present review aimed to consolidate the findings on genes associated with sub-centimeter lung cancer, with the intent of serving as a reference for future studies and the personalized management of sub-centimeter lung cancer through genetic testing.

DNA methylation molecular subtypes for prognosis prediction in lung adenocarcinoma

Aims

Lung cancer is one of the main results in tumor-related mortality. Methylation differences reflect critical biological features of the etiology of LUAD and affect prognosis.

Methods

In the present study, we constructed a prediction prognostic model integrating various DNA methylation used high-throughput omics data for improved prognostic evaluation.

Results

Overall 21,120 methylation sites were identified in the training dataset. Overall, 237 promoter genes were identified by genomic annotation of 205 CpG loci. We used Akakike Information Criteria (AIC) to obtain the validity of data fitting, but to prevent overfitting. After AIC clustering, specific methylation sites of cg19224164 and cg22085335 were left. Prognostic analysis showed a significant difference among the two groups (P = 0.017). In particular, the hypermethylated group had a poor prognosis, suggesting that these methylation sites may be a marker of prognosis.

Conclusion

The model might help in the identification of unknown biomarkers in predicting patient prognosis in LUAD.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01924-0.

Molecular charcterization of lung adenocarcinoma combining whole exome sequencing, copy number analysis and gene expression profiling

INTRODUCTION

Lung cancer is the leading cause of cancer mortality worldwide; lung adenocarcinoma (LUAD) corresponds to about 40% of lung cancers. LUAD is a genetically heterogeneous disease and the definition of this heterogeneity is of fundamental importance for prognosis and treatment.

AREAS COVERED

Based on primary literature, this review provides an updated analysis of multiomics studies based on the study of mutation profiling, copy number alterations and gene expression allowing for definition of molecular subgroups, prognostic factors based on molecular biomarkers, and identification of therapeutic targets. The authors sum up by providing the reader with their expert opinion on the potentialities of multiomics analysis of LUADs.

EXPERT OPINION

A detailed and comprehensive study of the co-occurring genetic abnormalities characterizing different LUAD subsets represents a fundamental tool for a better understanding of the disease heterogeneity and for the identification of subgroups of patients responding or resistant to targeted treatments and for the discovery of new therapeutic targets. It is expected that a comprehensive characterization of LUADs may provide a fundamental contribution to improve the survival of LUAD patients.

Identifying Common Genes Related to Platelet and Immunity for Lung Adenocarcinoma Prognosis Prediction

Background

Although 1000s of immune-related and platelet receptor-related genes have been identified in lung adenocarcinoma, their role in prognosis prediction remains unclear.

Methods

We downloaded mRNA data from the Cancer Genome Atlas Dataset (TCGA), and GSE68465 or GSE14814 data sets from the Gene Expression Omnibus (GEO) database.

Results

The high-risk group’s overall survival (OS) time was lower than that of the low-risk group’s in TCGA (p = 1.15e-03). Additionally, the risk score was an independent prognostic survival factor for lung adenocarcinoma patients in TCGA (HR = 2.136, 95%CI = 1.553–2.937, p < 0.001). The model’s prognostic performance was verified with two independent GEO cohorts (GSE68465 and GSE14814). We also developed a nomogram and provided free webpage prediction tools.¹ The mechanism of the high-risk group in this risk score may be have been related to somatic mutations and copy number changes. In addition, this risk score can distinguish the prognosis of the other two cancers (ACC, p < 0.001 and KIRP, p < 0.001). Also, among the other seven cancers, the OS prognosis for high and low risk groups show wide variation (p < 0.05).

Conclusion

Our research demonstrates that CCNA2 and TGFB2 are potential diagnostic and prognostic biomarkers, as well as therapeutic targets in lung adenocarcinoma (LUAD). We also determined a novel and reliable prognostic score for lung adenocarcinoma prognosis. The online nomogram prediction tool that contains this risk score may also help clinical medical staff.

Identification of a long non-coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) have a number of functions in various cellular processes and are potential prognostic factors for lung adenocarcinoma (LUAD). A gene risk model could provide novel evidence to improve the prediction of overall outcomes and provide more potential biomarkers. The present study aimed improve a previously published method of gene signature construction to make it more robust and accurate. The lncRNA expression profiles from 594 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database and samples were divided into high- and low-risk groups based on median risk scores calculated using a prognosis-related risk score formula. Univariate Cox regression, least absolute shrinkage and selection operator algorithm and multivariate Cox regression were performed to construct a gene signature based on the differentially expressed lncRNAs in patients with LUAD. The robustness and accuracy of the present model was assessed using area under the calculated curves (AUC) and Kaplan-Meier (K-M) survival analysis of the high- and low-risk cohorts. Potential biomarkers associated with survival status were then identified using K-M survival analysis and potential biomarker functions were predicted using enrichment analysis of co-expressed mRNAs. The gene signature constructed contained 44 lncRNAs. The AUCs for 3- and 5-year survival with the model were 0.836 and 0.818, respectively, of a time-dependent receiver operator characteristic curve. Moreover, lncRNAs AC124804.1 and MIR34AHG were identified using K-M survival analysis and the potential function of these two lncRNAs was predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment. The present lncRNA model provides novel insight which may improve prediction of prognosis for patients with LUAD and identify potentially novel biomarkers for the diagnosis.

Insights Into Lung Cancer Immune-Based Biology, Prevention, and Treatment

Lung cancer is the number one cause of cancer-related deaths. The malignancy is characterized by dismal prognosis and poor clinical outcome mostly due to advanced-stage at diagnosis, thereby inflicting a heavy burden on public health worldwide. Recent breakthroughs in immunotherapy have greatly benefited a subset of lung cancer patients, and more importantly, they are undauntedly bringing forth a paradigm shift in the drugs approved for cancer treatment, by introducing "tumor-type agnostic therapies". Yet, and to fulfill immunotherapy's potential of personalized cancer treatment, demarcating the immune and genomic landscape of cancers at their earliest possible stages will be crucial to identify ideal targets for early treatment and to predict how a particular patient will fare with immunotherapy. Recent genomic surveys of premalignant lung cancer have shed light on early alterations in the evolution of lung cancer. More recently, the advent of immunogenomic technologies has provided prodigious opportunities to study the multidimensional landscape of lung tumors as well as their microenvironment at the molecular, genomic, and cellular resolution. In this review, we will summarize the current state of immune-based therapies for cancer, with a focus on lung malignancy, and highlight learning outcomes from clinical and preclinical studies investigating the naïve immune biology of lung cancer. The review also collates immunogenomic-based evidence from seminal reports which collectively warrant future investigations of premalignancy, the tumor-adjacent normal-appearing lung tissue, pulmonary inflammatory conditions such as chronic obstructive pulmonary disease, as well as systemic microbiome imbalance. Such future directions enable novel insights into the evolution of lung cancers and, thus, can provide a low-hanging fruit of targets for early immune-based treatment of this fatal malignancy.

Large-scale analysis of acquired chromosomal alterations in non-tumor samples from patients with cancer

Mosaicism, the presence of subpopulations of cells bearing somatic mutations, is associated with disease and aging and has been detected in diverse tissues, including apparently normal cells adjacent to tumors. To analyze mosaicism on a large scale, we surveyed haplotype-specific somatic copy number alterations (sCNAs) in 1,708 normal-appearing adjacent-to-tumor (NAT) tissue samples from 27 cancer sites and in 7,149 blood samples from The Cancer Genome Atlas. We find substantial variation across tissues in the rate, burden and types of sCNAs, including those spanning entire chromosome arms. We document matching sCNAs in the NAT tissue and the adjacent tumor, suggesting a shared clonal origin, as well as instances in which both NAT tissue and tumor tissue harbor a gain of the same oncogene arising in parallel from distinct parental haplotypes. These results shed light on pan-tissue mutations characteristic of field cancerization, the presence of oncogenic processes adjacent to cancer cells.