Identification and Validation of Two Lung Adenocarcinoma-Development Characteristic Gene Sets for Diagnosing Lung Adenocarcinoma and Predicting Prognosis

Integrated multiomics analysis and machine learning refine molecular subtypes and prognosis for muscle-invasive urothelial cancer

Muscle-invasive urothelial cancer (MUC), characterized by high aggressiveness and significant heterogeneity, is currently lacking highly precise individualized treatment options. We used a computational pipeline to synthesize multiomics data from MUC patients using 10 clustering algorithms, which were then combined with 10 machine learning algorithms to identify molecular subgroups of high resolution and develop a robust consensus machine learning-driven signature (CMLS). Through multiomics clustering, we identified three cancer subtypes (CSs) that are related to prognosis, with CS2 exhibiting the most favorable prognostic outcome. Subsequent screening enabled identification of 12 hub genes that constitute a CMLS with robust predictive power for prognosis. The low-CMLS group exhibited a more favorable prognosis and greater responsiveness to immunotherapy and was more likely to exhibit the "hot tumor" phenotype. The high-CMLS group had a poor prognosis and lower likelihood of benefitting from immunotherapy, but dasatinib and romidepsin may serve as promising treatments for them. Comprehensive analysis of multiomics data can offer important insights and further refine the molecular classification of MUC. Identification of CMLS represents a valuable tool for early prediction of patient prognosis and for screening potential candidates likely to benefit from immunotherapy, with broad implications for clinical practice.

Single-cell data analysis of malignant epithelial cell heterogeneity in lung adenocarcinoma for patient classification and prognosis prediction

Lung cancer is one of the leading causes of cancer-related death. Most advanced lung adenocarcinoma (LUAD) patients have poor survival because of drug resistance and relapse. Neglecting intratumoral heterogeneity might be one of the reasons for treatment insensitivity, while single-cell RNA sequencing (scRNA-seq) technologies can provide transcriptome information at the single-cell level. Herein, we combined scRNA-seq and bulk RNA-seq data of LUAD and identified a novel cluster of malignant epithelial cells - KRT81+ malignant epithelial cells - associated with worse prognoses. Further analysis revealed that the hypoxia and EMT pathways of these cells were activated to predispose them to differentiate into metastatic lung adenocarcinoma cells. Finally, we also studied the role of these tumor cells in the immune microenvironment and their role in the classification and prognosis prediction of lung adenocarcinoma patients.

Molecular fingerprints of nuclear genome and mitochondrial genome for early diagnosis of lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most prevalent subtype of lung cancer with high morbidity and mortality rates. Due to the heterogeneity of LUAD, its characteristics remain poorly understood. Exploring the clinical and molecular characteristics of LUAD is challenging but vital for early diagnosis.

METHODS

This observational and validation study enrolled 80 patients and 13 healthy controls. Nuclear and mtDNA-captured sequencings were performed.

RESULTS

This study identified a spectrum of nuclear and mitochondrial genome mutations in early-stage lung adenocarcinoma and explored their association with diagnosis. The correlation coefficient for somatic mutations in cfDNA and patient-matched tumor tissues was high in nuclear and mitochondrial genomes. The mutation number of highly mutated genes was evaluated, and the Least Absolute Shrinkage and Selection Operator (LASSO) established a diagnostic model. Receiver operating characteristic (ROC) curve analysis explored the diagnostic ability of the two panels. All models were verified in the testing cohort, and the mtDNA panel demonstrated excellent performance. This study identified somatic mutations in the nuclear and mitochondrial genomes, and detecting mutations in cfDNA displayed good diagnostic performance for early-stage LUAD. Moreover, detecting somatic mutations in the mitochondria may be a better tool for diagnosing early-stage LUAD.

CONCLUSIONS

This study identified specific and sensitive diagnostic biomarkers for early-stage LUAD by focusing on nuclear and mitochondrial genome mutations. This also further developed an early-stage LUAD-specific mutation gene panel for clinical utility. This study established a foundation for further investigation of LUAD molecular pathogenesis.

Prognosis-correlated Systems Involving Characteristic Diagnostic Gene Sets for Survival Analysis on Glioma

As the most prevalent brain tumor, glioma is malignant with poor prognostic outcomes. As a result, it is of great importance to detect biomarkers for glioma diagnosis and prognosis. In this study, we determined grade-based characteristic gene clusters with gradual expression following grade change, including 1479 down- and 526 up-regulated genes. Combined interaction among proteins originating from these genes was analyzed, and hub genes were exhibited after GSEA enrichment, containing 12 and 11 genes which were correlated with prognostic outcomes, named as unfavorable and favorable gene sets, respectively. The GSVA score of each gene set was calculated and divided into high/low groups; we observed that cases in the low score group had better outcomes than the high score group based on the GSVA of the unfavorable set, while with favorable GSVA score, the low group had poorer outcomes than the high group. Eventually, we compared a variety of infiltrating immune cells between low/high GSVA subgroup, showing various immune cell types (B cell naive, activated mast cells, resting CD4 memory T cell, and so on) with opposite proportion. And interestingly, these cell types also accounted for a contrary percentage between unfavorable and favorable conditions. In conclusion, these two hub gene sets are of good importance as an evaluation system for clinical grade classification and prognosis prediction.

Modulation of Tumor Immune Microenvironment and Prognostic Value of Ferroptosis-Related Genes, and Candidate Target Drugs in Glioblastoma Multiforme

Glioblastoma multiforme (GBM) is the most common type of malignant brain tumor, among which IDH1-wild type GBM has a poor prognosis. Recent studies have shown that ferroptosis-related genes (FRGs) are correlated with the development and progression of cancer. In GBM, the role of FRGs associated with IDH1 status as biological indicators and therapeutic targets remains to be clarified. Ten of FRGs (STEAP3, HSPB1, MAP1LC3A, SOCS1, LOX, CAPG, CP, GDF15, CDKN1A, and CD44) associated with IDH1 status in GBM were identified as key genes through screening by survival analysis and Random Forest using The Cancer Genome Atlas (TCGA) datasets, and the protein expressions of key genes were verified. Transwell and qPCR results showed that ferroptosis promoted the migration of glioblastoma cells and affected the expression of key genes. Our study established the ferroptosis-related prognostic model for GBM patients based on ten key genes by a different modeling method from previous study, the GSVA algorithm. Further, we took the methods of functional enrichment analysis, clinical characteristics, immune cell infiltration, immunomodulator, ESTIMATE and single nucleotide variant (SNV) analysis to study the molecular mechanisms of prognostic model and key genes. The results showed that ten key genes were strongly associated with immune-related factors and were significantly involved in the p53 signaling pathway, senescence and autophagy in cancer, and in the negative regulation of protein kinase activity. Moreover, potential therapeutic drugs were identified by Virtual Screening and Molecular Docking. Our study indicated that the novel ferrotosis-related prognostic model for GBM patients and key genes possessed the prognostic and therapeutic values.

The clinical outcome and risk factors analysis of immune checkpoint inhibitor-based treatment in lung adenocarcinoma patients with brain metastases

Background

The data about efficacy of immunotherapy for non-small cell lung cancer with brain metastases (BMs) from real-word settings are controversial. This real-word study is aimed to evaluate the clinical outcome of immune checkpoint inhibitor (ICI)-based treatment in lung adenocarcinoma patients with brain metastases (BMs) and explore potential risk factors, with a focus on the spatial distribution of BMs as previous studies suggested spatial heterogeneity on the brain immune microenvironment.

Methods

Advanced lung adenocarcinoma patients with non-oncogene-addicted, who received ICI monotherapy or plus chemotherapy, were enrolled. Efficacy was assessed by Response Evaluation Criteria in Solid Tumors version 1.1. Intergroup comparisons were performed using Pearson's χ2 or Fisher's exact tests for categorical variables. The progression-free survival (PFS) was estimated using Kaplan-Meier method and compared using log-rank test. Cox proportional hazards model was used for multivariate analyses. Peripheral blood was collected from 15 patients with BMs. Tumor-derived exosomes in plasma were isolated by size exclusion chromatography and the cDNA library preparations for miRNA were sequenced on an Illumina Hiseq platform. Differentially expressed genes in the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were analyzed.

Results

A total of 198 patients were enrolled and brain metastasis occurred in 20.7% patients (N=41). Compared with patients without BMs, those with BMs had a comparable objective response rate (ORR; 29.3% vs. 43.9%; P=0.089), a lower disease control rate (DCR; 58.5% vs. 78.3%; P=0.01), and a shorter PFS (3.6 vs. 8.6 months; P=0.069). For patients with BMs, factors, including the presence of neurological symptoms, the treatment of intracranial radiotherapy, and the combination of ICI with chemotherapy, had no impact on PFS, whereas cerebellum metastasis was significantly associated with shorter PFS (2.8 vs. 13.8 months, P=0.007). Six upregulated miRNAs were identified in patients with cerebellum metastases (N=8) compared with those without (N=7). The enrichment of differentially expression genes in the KEGG pathways indicated upregulated sulfur metabolism pathway in patients with cerebellum metastases.

Conclusions

For lung adenocarcinoma patients, those with BMs have inferior response to ICI-based treatment, but not significantly, and cerebellum metastasis is an independent risk factor with poor outcome for such patients, might attributing to the upregulated sulfur metabolism.

Junctional Adhesion Molecule-Like Protein (JAML) Is Correlated with Prognosis and Immune Infiltrates in Lung Adenocarcinoma

BACKGROUND Junctional adhesion molecule-like protein (JAML) is a member of the junctional adhesion molecule family and mediates migration of immune cells, but its function in cancers remains unclear. This study aimed to evaluate the role of JAML in the prognosis and immune infiltrates of lung adenocarcinoma (LUAD). MATERIAL AND METHODS JAML expressions in LUAD tissues and normal tissues were compared using The Cancer Genome Atlas (TCGA) database and datasets from the Gene Expression Omnibus (GEO) database. The influence of JAML expression on prognosis was analyzed by Kaplan-Meier curve and Cox regression model. Interactive and functional analyses of JAML were performed by LinkedOmics and GeneMANIA databases. TIMER2.0, TISIDB, and GEPIA2 databases were used to investigate the correlation between JAML expression and immune infiltrates. RESULTS JAML expression was decreased in LUAD (P<0.001), and lower JAML expression was associated with worse outcomes of LUAD patients. High JAML expression was the protective factor for overall survival (OS) (HR 0.706, 95% CI 0.500-0.997, P=0.048). Interactive and functional analyses suggested that co-expressed genes with JAML have an obvious link to immune-related pathways. In addition, JAML expression was positively associated with infiltrating levels of CD8+ T cells, CD4+ T cells, B cells, dendritic cells, macrophages, and neutrophils, and had significant correlations with diverse immune marker sets in LUAD. CONCLUSIONS JAML expression was significantly correlated with prognosis and immune infiltrates. These preliminary findings suggested JAML could be considered as a potential prognostic biomarker and therapeutic target for LUAD.

In silico assessment of EpCAM transcriptional expression and determination of the prognostic biomarker for human lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC)

Epithelial cell adhesion molecule (EpCAM) is a transmembrane glycoprotein which is involved in cell signaling, proliferation, maturation, and movement, all of which are crucial for the proper development of cells and tissues. Cleavage of the EpCAM protein leads to the up-regulation of c-myc, e-fabp, and cyclins A and E which promote tumorigenesis. EpCAM can act as potential diagnostic and prognostic biomarker for different types of cancers as it is also found to be expressed in epithelia and epithelial-derived neoplasms. Hence, we aimed to analyze the EpCAM gene expression and any associated feedback in the patients of two major types of lung cancer (LC) i.e., lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), based on the publicly available online databases. In this study, server-based gene expression analysis represents the up-regulation of EpCAM in both LUAD and LUSC subtypes as compared to the corresponding normal tissues. Besides, the histological sections revealed the over-expression of EpCAM protein in cancerous tissues by depicting strong staining signals. Furthermore, mutation analysis suggested missense as the predominant type of mutation both in LUAD and LUSC in the EpCAM gene. A significant correlation (P-value < 0.05) between the higher EpCAM expression and lower patient survival was also found in this study. Finally, the co-expressed genes were identified with their ontological features and signaling pathways associated in LC development. The overall study suggests EpCAM to be a significant biomarker for human LC prognosis.