A prognosis-related molecular subtype for early-stage non-small lung cell carcinoma by multi-omics integration analysis

The clinical implication and translational research of OSCC differentiation

BACKGROUND

The clinical value and molecular characteristics of tumor differentiation in oral squamous cell carcinoma (OSCC) remain unclear. There is a lack of a related molecular classification prediction system based on pathological images for precision medicine.

METHODS

Integration of epidemiology, genomics, experiments, and deep learning to clarify the clinical value and molecular characteristics, and develop a novel OSCC molecular classification prediction system.

RESULTS

Large-scale epidemiology data (n = 118,817) demonstrated OSCC differentiation was a significant prognosis indicator (p < 0.001), and well-differentiated OSCC was more chemo-resistant than poorly differentiated OSCC. These results were confirmed in the TCGA database and in vitro. Furthermore, we found chemo-resistant related pathways and cell cycle-related pathways were up-regulated in well- and poorly differentiated OSCC, respectively. Based on the characteristics of OSCC differentiation, a molecular grade of OSCC was obtained and combined with pathological images to establish a novel prediction system through deep learning, named ShuffleNetV2-based Molecular Grade of OSCC (SMGO). Importantly, our independent multi-center cohort of OSCC (n = 340) confirmed the high accuracy of SMGO.

CONCLUSIONS

OSCC differentiation was a significant indicator of prognosis and chemotherapy selection. Importantly, SMGO could be an indispensable reference for OSCC differentiation and assist the decision-making of chemotherapy.

Integrative bioinformatics analysis reveals ECM and nicotine-related genes in both LUAD and LUSC, but different lung fibrosis-related genes are involved in LUAD and LUSC

There are several bioinformatics studies related to lung cancer, but most of them have mainly focused on either microarray data or RNA-Seq data alone. In this study, we have combined both types of data to identify differentially expressed genes (DEGs) specific to lung cancer subtypes. We obtained six microarray datasets from the GEO and also the expression matrix of LUSC and LUAD from TCGA, which were analyzed by GEO2R tool and GEPIA2, respectively. Enrichment analyses of DEGs were performed using the Enrichr database. Protein module identification was done by MCODE plugin in cytoscape software. We identified 30 LUAD-specific, 17 LUSC-specific, and 17 DEGs shared between LUAD and LUSC. Enrichment analyses revealed that LUSC-specific DEGs are involved in lung fibrosis. In addition, DEGs shared between LUAD and LUSC are involved in extracellular matrix (ECM), nicotine metabolism, and lung fibrosis. We identified lung fibrosis-related genes, including SPP1, MMP9, and CXCL2, involved in both LUAD and LUSC, but SERPINA1 and PLAU genes involved only in LUSC. We also found an important module separately for LUAD-specific, LUSC-specific, and shared DEGs between LUSC and LUAD. S100P, GOLM, AGR2, AK1, TMEM125, SLC2A1, COL1A1, and GHR genes were significantly associated with survival. Our findings suggest that different lung fibrosis-related genes may play roles in LUSC and LUAD. Additionally, nicotine metabolism and ECM remodeling were found to be associated with both LUSC and LUAD, regardless of subtype, emphasizing the role of smoking in the development of lung cancer and ECM in the high aggressiveness and mortality of lung cancer.

The Tumor Stemness Indice mRNAsi can Act as Molecular Typing Tool for Lung Adenocarcinoma

Due to the high heterogeneity, lung adenocarcinoma (LUAD) cannot be distinguished into precise molecular subtypes, thereby resulting in poor therapeutic effect and low 5-year survival rate clinically. Although the tumor stemness score (mRNAsi) has been shown to accurately characterize the similarity index of cancer stem cells (CSCs), whether mRNAsi can serve as an effective molecular typing tool for LUAD isn't reported to date. In this study, we first demonstrate that mRNAsi is significantly correlated with the prognosis and disease degree of LUAD patients, i.e., the higher the mRNAsi, the worse the prognosis and the higher the disease degree. Second, we identify 449 mRNAsi-related genes based on both weighted gene co-expression network analysis (WGCNA) and univariate regression analysis. Third, our results display that 449 mRNAsi-related genes can accurately distinguish the LUAD patients into two molecular subtypes: ms-H subtype (with high mRNAsi) and ms-L subtype (with low mRNAsi), particularly the ms-H subtype has a worse prognosis. Remarkably, significant differences in clinical characteristics, immune microenvironment, and somatic mutation exist between the two molecular subtypes, which might lead to the poorer prognosis of the ms-H subtype patients than that of the ms-L subtype ones. Finally, we establish a prognostic model containing 8 mRNAsi-related genes, which can effectively predict the survival rate of LUAD patients. Taken together, our work provides the first molecular subtype related to mRNAsi in LUAD, and reveals that these two molecular subtypes, the prognostic model and marker genes may have important clinical value for effectively monitoring and treating LUAD patients.

Early Stage Finding of an Immune-Enhanced Genetic Subtype of Nonsmall Cell Lung Cancer Related with T-Cell Depletion

Background

Molecular categorization of lung cancer in medical care is becoming increasingly important on a regular basis. One of the molecular classifications of NSCLC (early-stage NSCLC) supports that tumors of different biological varieties differ in terms of their genomes and clinical characteristics.

Methods

Based on published immune cell signatures and early-stage NSCLC gene expression data including cancer genome maps, we used consensus cluster analysis to identify immune molecular subtypes associated with early-stage NSCLC expression subtypes. These subtypes were correlated with early-stage NSCLC expression subtypes. Next, applying a wide range of statistical techniques, we evaluated the link between molecular subtypes and clinical features, immunological microenvironment, and immunotherapy reactivity. Molecular subtypes were defined as a classification of cancerous cells.

Results

Multiple RNAseq cross-platform datasets of immune genes were used to identify two molecular subtypes (C1 and C2) of NSCLC, with C1 showing a more favorable prognosis than C2. The results were validated on the CSE datasets. In terms of clinical characteristics, C2 subtype samples with a worse prognosis showed a more advanced tumor stage and higher mortality. C2 showed immuno-infiltrative characteristics but had depletion of T-cells. Biological functions such as EMT were enriched on C2. A low TIDE score in C1 indicated that C1 samples could benefit from taking immunotherapy. C2 were more susceptible to standard chemotherapeutic treatments such paclitaxel, cisplatin, sorafenib, crizotinib, and erlotinib.

Conclusion

According to our findings, early-stage NSCLC patients may benefit from receiving treatment with immune checkpoint blockade therapy.