Adenocarcinoma ; classification ; genetics ; pathology ; Forecasting ; Humans ; Lung ; pathology ; Lung Neoplasms ; classification ; genetics ; pathology

Carcinoma, Non-Small-Cell Lung ; genetics ; Exons ; genetics ; Genes, erbB-1 ; genetics ; Humans ; Lung Neoplasms ; pathology

Animals ; Humans ; Lung Neoplasms ; genetics ; pathology ; MicroRNAs ; genetics ; physiology ; Neoplasm Metastasis ; genetics ; pathology

Medical studies have found that tumor mutation burden (TMB) is positively correlated with the efficacy of immunotherapy for non-small cell lung cancer (NSCLC), and TMB value can be used to predict the efficacy of targeted therapy and chemotherapy. However, the calculation of TMB value mainly depends on the whole exon sequencing (WES) technology, which usually costs too much time and expenses. To deal with above problem, this paper studies the correlation between TMB and slice images by taking advantage of digital pathological slices commonly used in clinic and then predicts the patient TMB level accordingly. This paper proposes a deep learning model (RCA-MSAG) based on residual coordinate attention (RCA) structure and combined with multi-scale attention guidance (MSAG) module. The model takes ResNet-50 as the basic model and integrates coordinate attention (CA) into bottleneck module to capture the direction-aware and position-sensitive information, which makes the model able to locate and identify the interesting positions more accurately. And then, MSAG module is embedded into the network, which makes the model able to extract the deep features of lung cancer pathological sections and the interactive information between channels. The cancer genome map (TCGA) open dataset is adopted in the experiment, which consists of 200 pathological sections of lung adenocarcinoma, including 80 data samples with high TMB value, 77 data samples with medium TMB value and 43 data samples with low TMB value. Experimental results demonstrate that the accuracy, precision, recall and F1 score of the proposed model are 96.2%, 96.4%, 96.2% and 96.3%, respectively, which are superior to the existing mainstream deep learning models. The model proposed in this paper can promote clinical auxiliary diagnosis and has certain theoretical guiding significance for TMB prediction.
Humans ; Lung Neoplasms/pathology* ; Carcinoma, Non-Small-Cell Lung/genetics* ; Mutation ; Adenocarcinoma of Lung/genetics* ; Biomarkers, Tumor/genetics*

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrous interstitial lung disease of unknown etiology. IPF is also considered to be among the independent risk factors for lung cancer, increasing the risk of lung cancer by 7% and 20%. The incidence of IPF complicated with lung cancer, especially non-small cell lung cancer (NSCLC), is increasing gradually, but there is no consensus on unified management and treatment. IPF and NSCLC have similar pathological features. Both appear in the surrounding area of the lung. In pathients with IPF complicated with NSCLC, NSCLC often develops from the honeycomb region of IPF, but the mechanism of NSCLC induced by IPF remains unclear. In addition, IPF and NSCLC have similar genetic, molecular and cellular processes and common signal transduction pathways. The universal signal pathways targeting IPF and NSCLC will become potential therapeutic drugs for IPF complicated with NSCLC. This article examines the main molecular mechanisms involved in IPF and NSCLC and the research progress of drugs under development targeting these signal pathways.
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Humans ; Carcinoma, Non-Small-Cell Lung/genetics* ; Idiopathic Pulmonary Fibrosis/drug therapy* ; Lung Neoplasms/genetics* ; Lung/pathology* ; Signal Transduction

In recent years, series of driver genes, such as EGFR, KRAS/NRAS, BRAF, PIK3CA, ALK and ROS1 and so on, have been found in non-small cell lung cancer (NSCLC) one after another with the development of molecular detecting technology. Targeted drugs bring benefits for these NSCLC patients with driver gene variations. However, some NSCLC did not have any known driver gene variations; we called it pan-negative lung cancer. In this paper, we summarize the concept, clinical pathological characteristics, the epidemiological characteristics, treatment and prognosis of pan-negative NSCLC.
Carcinoma, Non-Small-Cell Lung ; diagnosis ; drug therapy ; genetics ; pathology ; Humans ; Lung Neoplasms ; diagnosis ; drug therapy ; genetics ; pathology ; Mutation ; Prognosis

BACKGROUND: m6A RNA methylation modification plays an important role in the occurrence and progression of lung cancer and regulates tumor immunity. Current studies mostly focus on the differential expression of some specific m6A effectors and infiltrating immune cell. m6A methylation modification is the result of mutual adjustment and balance between effectors, and changes in the expression of one or two effectors are far from enough to reflect the panorama of m6A methylation. The role of m6A in the immune microenvironment of lung adenocarcinoma (LUAD) is still poorly understood. The aim of this study is to investigate the effect of different m6A modification patterns in immune microenvironment of LUAD. METHODS: LUAD data was obtained from The Cancer Genome Atlas (TCGA), University of California Santa Cruz Xena (UCSC Xena) and Gene Expression Omnibus (GEO) databases. Gene mutation, differential expression and survival analysis were performed for 24 m6A effectors. The m6A modification pattern was constructed by unsupervised clustering method, and the m6A clusters survival analysis, gene set variation analysis, immune score and immune cell infiltration analysis were performed. The association between LRPPRC protein expression levels and infiltration of CD8+ cytotoxic T lymphocytes and CD68+ macrophages in the tumor microenvironment was validated by immunohistochemistry in LUAD tissue microarray with 68 cases. RESULTS: The mutations of m6A effector were found in 150 of 567 LUAD cases with a frequency of 26.46%. 6 readers and 3 writers were significantly up regulated in LUAD tissues compared with normal tissues. IGF2BP1 and HNRNPC are the independent risk factors for prognosis of LUAD. Abundant cross-talks among writers, erasers and readers were demonstrated. Three m6A modification patterns with different immune cell infiltration characteristics and clinical prognosis were established. Among m6A effectors, LRPPRC was found to be inversely associated with the infiltration of CD8+ cytotoxic T lymphocytes and CD68+ macrophages, and was validated in 68 LUAD tissues. CONCLUSIONS m6A modification patterns play non-negligible roles in regulating the immune microenvironment. LRPPRC has potential to be a new biomarker for checkpoint inhibitor immunotherapy.
Adenocarcinoma/genetics* ; Adenocarcinoma of Lung/pathology* ; Adenosine/metabolism* ; Gene Expression Regulation, Neoplastic ; Humans ; Lung Neoplasms/pathology* ; Methylation ; Tumor Microenvironment/genetics*

How to improve the performance of circulating tumor DNA (ctDNA) signal acquisition and the accuracy to authenticate ultra low-frequency mutation are major challenges of minimal residual disease (MRD) detection in solid tumors. In this study, we developed a new MRD bioinformatics algorithm, namely multi-variant joint confidence analysis (MinerVa), and tested this algorithm both in contrived ctDNA standards and plasma DNA samples of patients with early non-small cell lung cancer (NSCLC). Our results showed that the specificity of multi-variant tracking of MinerVa algorithm ranged from 99.62% to 99.70%, and when tracking 30 variants, variant signals could be detected as low as 6.3 × 10 ^-5 variant abundance. Furthermore, in a cohort of 27 NSCLC patients, the specificity of ctDNA-MRD for recurrence monitoring was 100%, and the sensitivity was 78.6%. These findings indicate that the MinerVa algorithm can efficiently capture ctDNA signals in blood samples and exhibit high accuracy in MRD detection.
Humans ; Carcinoma, Non-Small-Cell Lung/genetics* ; Lung Neoplasms/genetics* ; Neoplasm, Residual/pathology* ; Biomarkers, Tumor/genetics* ; Computational Biology

OBJECTIVETo detect DNA and chromosomes instabilities during the progression of tumors and screen new molecular markers coupled to putative or unknown oncogenes and/or tumor suppressor genes.

METHODSFive kinds of tumors, in a total of 128 specimens, were analyzed by random amplified polymorphic DNA (RAPD) PCR. Bands representing instabilities were recovered, purified, and cloned, labeled as probes for Southern and Northern blot analysis and DNA sequencing.

RESULTSSample 5 and 3 of the gastric cancer tissues showed the highest genomic changes and the average detectability in five cancers was up to at least 40% (42.2% - 49.4%). There were significant differences in the ability of each primer to detect genomic instability, which ranged from 27% (primer 8) to 68% (primer 2). Band B is a single copy fragment, and was found to be an allelic loss in gastric and colon cancers. It is a novel sequence and was registered in GenBank with Accession Number AF151005. Further analysis revealed that it might be part of a cis-regulatory element of a new tumor suppressor gene, containing a promoter of cis-action "CACA" box, an enhancer of "GATA" family and a start codon.

CONCLUSIONSIt was impossible or difficult to get great achievements for cancer treatments with the procedure of gene therapy only to one oncogene or one tumor suppressor gene because the extensive DNA variations occurred during the progression of tumor. RAPD assay connected with other techniques was a good tool for the detection of genomic instabilities and direct screening of some new molecular markers related to tumor suppressor genes or oncogenes.

Base Sequence ; Blotting, Southern ; Colonic Neoplasms ; genetics ; pathology ; Humans ; Liver Neoplasms ; genetics ; pathology ; Lung Neoplasms ; genetics ; pathology ; Molecular Sequence Data ; Neoplasms ; genetics ; pathology ; Polymorphism, Restriction Fragment Length ; Random Amplified Polymorphic DNA Technique ; Sequence Analysis, DNA ; Stomach Neoplasms ; genetics ; pathology

Objective Many studies have revealed the crucial roles of miRNA in multiple human cancers, including lung adenocarcinoma (LUAD). In this study, we sought to explore new miRNA-mRNA pairs that are associated with LUAD prognosis. Methods A novel miRNA-mRNA regulatory network associated with prognosis in LUAD was identified and validated using the bioinformatic tools including OncomiR database, StarBase, miRnet, GEPIA2, UALCAN. Results Twenty key miRNAs were compiled after the analysis of the expression and prognostic value in OncomiR and StarBase. Targeted mRNAs of these key miRNAs were predicted in miRnet, and the resulting mRNAs were also analyzed for their prognostic values and expression patterns in GEPIA2 and UALCAN, respectively. Further expression correlation analysis was performed in StarBase. Subsequently, a new miRNA-mRNA network was built, of which each RNA pair showed negative expression correlation, opposite expression pattern, and prognostic value. Protein-protein interaction network was under construction for the mRNAs, and 19 hub genes were determined. Enrichment analysis showed that "Cell Cycle, Mitotic" was the most significantly enriched term. Then, a miRNA-hub gene sub-network was built. We selected and validated the regulatory relationship of some miRNA-hub pairs, including hsa-miR-1976/RFC2, hsa-let-7c-5p/RFC2, hsa-let-7c-5p/ESPL1, hsa-let-7c-5p/CDC25A, and hsa-miR-101-3p/KIF2C. Moreover, over-expression of hsa-miR-1976 and hsa-let-7c-5p resulted in significant cell cycle arrest. Conclusions Our results determined new prognosis-associated miRNA-mRNA pairs and might shed further light on the mechanism via which miRNA-mRNA network influences prognosis in LUAD.
Adenocarcinoma of Lung/genetics* ; Humans ; Lung Neoplasms/pathology* ; MicroRNAs/metabolism* ; Prognosis ; RNA, Messenger/metabolism*