Integrative Genomic and Transcriptomic Analyses of Tumor Suppressor Genes and Their Role on Tumor Microenvironment and Immunity in Lung Squamous Cell Carcinoma

TMSB4X: A novel prognostic marker for non-small cell lung cancer

Non-small cell lung cancer (NSCLC), as the main type of lung cancer, has a long history of high incidence and mortality. Despite the continuous updates to the American Joint Committee on Cancer (AJCC) staging system, which adapt to evolving treatment modalities and diagnostic advancements, it is evident that patients at the same stage exhibit varying prognoses. The heterogeneity of tumors underscores the need for molecular diagnostics to assume a pivotal role in tumor staging and patient stratification. In our investigation, we meticulously analyzed the data of the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) database, incorporating clinical patients and scrutinizing pathological specimens. Through this comprehensive approach, we established a correlation between the expression of the Thymosin beta 4 X-linked (TMSB4X) gene and poorer disease-free survival (DFS) and overall survival (OS) post-surgery. Compared to the TMSB4X positive expression group, patients in the negative expression group had a better prognosis, with longer DFS (median disease-free survival (median DFS): 16.2 months vs. 11.3 months, P = 0.032) and OS (median overall survival (mOS): 29.8 months vs. 18.5 months, P = 0.033). Furthermore, our findings suggest that TMSB4X may facilitate immune evasion in non-small cell lung cancer cells by influencing the activation of infiltrating dendritic cells (DCs) in tumor infiltrating immune cells (TIICs) (R = 0.27, P = 4.8E+08). In summary, TMSB4X emerges as an unfavorable prognostic factor for NSCLC, potentially modulating the tumor immune microenvironment through its regulatory impact on dendritic cell function, thus facilitating tumor immune escape.

Prognostic and predictive impact of molecular tumor burden index in non-small cell lung cancer patients

BACKGROUND

The biomarkers of immune checkpoint inhibitors in the treatment of non-small cell lung cancer (NSCLC) patients have limited predictive performance. In this study we aimed to investigate the feasibility of molecular tumor burden index (mTBI) in circulating tumor DNA (ctDNA) as a predictor for immunotherapy in patients with NSCLC.

METHODS

From February 2017 to November 2020, pretreatment and on-treatment (3~6 weeks after first cycle of immunotherapy) dynamic plasma ctDNA samples from NSCLC patients receiving immune monotherapy or combination therapy were analyzed by targeted capture sequencing of 1021 genes. PyClone was used to infer the mTBI. The impact of pretreatment mTBI on survival outcomes was verified in the POPLAR/OAK trials.

RESULTS

We found that patients without detectable baseline ctDNA had better survival outcomes (median overall survival [OS]: not reached vs. 12.8 months; hazard ratio [HR], 0.15; p = 0.035]). RB1 and SMARCA4 mutations were remarkably associated with worse survival outcomes. Furthermore, lower pretreatment mTBI was associated with superior OS (median: not reached vs. 8.1 months; HR, 0.22; p = 0.024) and PFS (median: 32.9 vs. 5.4 months; HR, 0.35; p = 0.045), but not objective response, which was validated in the POPLAR/OAK cohort, suggesting that baseline mTBI was a prognostic factor for NSCLC immunotherapy. Early dynamic changes of mTBI (ΔmTBI) significantly distinguished responsive patients, and patients with mTBI decrease to more than 68% at the final tumor evaluation had longer OS (median: 38.2 vs. 4.0 months; HR, 0.18; p = 0.017) and PFS (median: not reached vs. 2.3 months; HR, 0.24; p = 0.030).

CONCLUSION

ΔmTBI had a good sensitivity to identify potential beneficial patients based on the best effect CT scans, demonstrating that mTBI dynamics were predictive of benefit from immune checkpoint blockade.

Characterization of virus-mediated autoimmunity and the consequences for pathological process in patients with systemic lupus erythematosus

INTRODUCTION/OBJECTIVES

This study aimed to identify differentially expressed genes (DEGs) of systemic lupus erythematosus (SLE) using gene expression-based computational methodologies to analyze disease-immune interactions, which affect the development and progression of SLE.

METHOD

Twenty-six patients with SLE and 46 healthy controls were selected from the Gene Expression Omnibus (GEO) database. The significantly enriched immune and virus-related gene lists were computed and visualized by using the DEGs from the gene set enrichment analysis (GSEA). Quantification of 38 immune cells was performed in determining the impact of immune cells on the virus mediated immunity in SLE by using ImmQuant algorithm.

RESULTS

Thirty-nine upregulated and 57 downregulated were identified in SLE patient compared to the healthy controls. Upregulated genes were significantly implicated in Gene Ontology gene sets as cytokine mediated signaling, secretion, and exocytosis in immune response pathways in 26 female SLE patients. In addition, these genes were enriched in hepatitis C, influenza A, measles, Epstein-Barr virus, and herpes simplex virus 1 infection in Kyoto Encyclopedia of Genes and Genomes pathways. Especially, FCGR1A, IRF7, OAS2, CAMP, MX1, OAS3, OAS1, DEFA3, ISG15, and RSAD2 were involved in virus mediated SLE mechanism, and the expression for OAS1, OAS2, and IRF7 was closely associated with the quantities of colony forming unit-monocyte and colony forming unit-granulocyte.

CONCLUSIONS

Identifying virus-mediated SLE genes and quantifies of immune cells were used to understand the pathological process and perform early diagnosis of female SLE, and will lead to clinical tools for treating SLE in patients. Key Points • Using gene expression-based computational methodologies, the 57 immune and viral genes were significantly upregulated in 26 SLE patients. • The identified three key  viral genes such as OAS1, OAS2, and IF7 were closely associated with colony-forming unit-monocytes and colony-forming unit-granulocytes, which affect the virus mediated immunity in SLE. • The viral genes and quantifies of immune cells are useful in understanding pathogenesis of SLE, and this will provide clinical strategies of potential treatment choices in SLE patients.

Arachidonate 15-lipoxygenase type B: Regulation, function, and its role in pathophysiology

As a lipoxygenase (LOX), arachidonate 15-lipoxygenase type B (ALOX15B) peroxidizes polyenoic fatty acids (PUFAs) including arachidonic acid (AA), eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and linoleic acid (LA) to their corresponding fatty acid hydroperoxides. Distinctive to ALOX15B, fatty acid oxygenation occurs with positional specificity, catalyzed by the non-heme iron containing active site, and in addition to free PUFAs, membrane-esterified fatty acids serve as substrates for ALOX15B. Like other LOX enzymes, ALOX15B is linked to the formation of specialized pro-resolving lipid mediators (SPMs), and altered expression is apparent in various inflammatory diseases such as asthma, psoriasis, and atherosclerosis. In primary human macrophages, ALOX15B expression is associated with cellular cholesterol homeostasis and is induced by hypoxia. Like in inflammation, the role of ALOX15B in cancer is inconclusive. In prostate and breast carcinomas, ALOX15B is attributed a tumor-suppressive role, whereas in colorectal cancer, ALOX15B expression is associated with a poorer prognosis. As the biological function of ALOX15B remains an open question, this review aims to provide a comprehensive overview of the current state of research related to ALOX15B.

Classification prediction of early pulmonary nodes based on weighted gene correlation network analysis and machine learning

OBJECTIVE

To use weighted gene correlation network analysis (WGCNA) and machine learning algorithm to predict classification of early pulmonary nodes with public databases.

METHODS

The expression data and clinical data of lung cancer patients were firstly extracted from public database (GTEx and TCGA) to study the differentially expressed genes (DEGs) of lung adenocarcinoma (LUAD). The intersection of three R packages (Dseq2, Limma, EdgeR) methods were selected as candidate DEGs for further study. WGCNA was used to obtain relevant modules and key genes of lung cancer classification, GO and KEGG enrichment analysis was performed. The model was built using two machine learning methods, Least Absolute Shrinkage and Selection Operator (LASSO) regression and tumor classification was also predicted with extreme Gradient Boosting (XGBoost) algorithm.

RESULTS

DEGs analysis revealed that there were 1306 LUAD genes. WGCNA module analysis showed that a total of 116 genes were significantly related to classification, and module genes were mainly related to 14 KEGG pathways. The machine learning algorithm identified 10 target genes by LASSO regression analysis of differential genes, and 18 genes were identified by XGBoost model. A total of 6 genes were found from the intersection of the above methods as classification signatures of early pulmonary nodules, including "HMGB3" "ARHGAP6" "TCF21" "FCN3" "COL6A6" "GOLM1".

CONCLUSION

Using DEGs analysis, WGCNA method and machine learning algorithm, six gene signatures related to early stage of LUAD, which can assist clinicians in disease classification prediction.

Modulation of Lymphocyte Functions in the Microenvironment by Tumor Oncogenic Pathways

Despite the broad application of different immunotherapeutic strategies for the treatment of solid as well as hematopoietic cancers, the efficacy of these therapies is still limited, with only a minority of patients having a long-term benefit resulting in an improved survival rate. In order to increase the response rates of patients to the currently available immunotherapies, a better understanding of the molecular mechanisms underlying the intrinsic and/or extrinsic resistance to treatment is required. There exist increasing evidences that activation of different oncogenic pathways as well as inactivation of tumor suppressor genes (TSG) in tumor cells inhibit the immune cell recognition and influegnce the composition of the tumor microenvironment (TME), thus leading to an impaired anti-tumoral immune response. A deeper understanding of the link between the tumor milieu and genomic alterations of TSGs and oncogenes is indispensable for the optimization of immunotherapies and to predict the patients’ response to these treatments. This review summarizes the role of different cancer-related, oncogene- and TSG-controlled pathways in the context of anti-tumoral immunity and response to different immunotherapies.

ZNF143 Expression is Associated with COPD and Tumor Microenvironment in Non-Small Cell Lung Cancer

Background

Chronic obstructive pulmonary disease (COPD) is an inflammatory-related disease highly associated with increased lung cancer risk. Studies have explored the tumor promoting roles for zinc finger protein 143 (ZNF143). However, the role of ZNF143 in COPD and tumor microenvironment of non-small cell lung cancer (NSCLC) has not been fully elucidated.

Methods

COPD-related key genes were identified by differential gene expression evaluation, WGCNA and SVM-RFE analysis using mRNA expression data retrieved from public databases. ROC analysis was conducted to evaluate the diagnostic value of ZNF143. Correlation between ZNF143 and clinic-pathological features, associations with tumor-infiltrating immune cells (TICs) and the relationship with predictors of immunotherapy efficacy were explored. ZNF143 gene expression was validated by qRT-PCR using an independent cohort.

Results

Bioinformatic and machine learning analysis showed that ZNF143 was a COPD-related gene. ZNF143 expression was significantly upregulated in COPD and is a potential diagnostic biomarker in COPD with AUC > 0.85. ZNF143 expression was significantly upregulated in lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). ZNF143 expression levels were significantly higher in LUAD patients with COPD relative to the levels in patients only with LUAD. Upregulation of ZNF143 in patients with comorbidity of NSCLC and COPD was further confirmed by qRT-PCR analysis. High expression of ZNF143 was significantly correlated with advanced TNM stage in LUSC. High ZNF143 expression was associated with activated TICs in both LUAD and LUSC samples. Moreover, ZNF143 expression was significantly correlated with the levels of several known predictors of immunotherapy efficacy, including PD-L1, PD-L2, TMB and TIDE in NSCLC.

Conclusion

ZNF143 is a novel COPD biomarker. High expression level of ZNF143 is associated with immune microenvironment and high risk of progression of COPD to NSCLC.

Tumor suppressor gene mutations correlate with prognosis and immunotherapy benefit in hepatocellular carcinoma

INTRODUCTION

The tumor microenvironment (TME) has profound impacts on prognosis and immunotherapy. The TME can be altered by the genomic mutations on specific tumor-suppressor genes (TSG), thus, comprehending the association between TME and TSG in hepatocellular carcinoma (HCC) is imperative.

METHODS

With a total of 1699 HCC patients from 6 international multicenter cohorts, we delineated the mutational landscape of TSG and summarized the proportion of TSG mutated HCC in different countries. Using the genomic and transcriptomic data, we comprehensively explored the impacts of TSG mutations on TME and immunity in HCC. A dataset of 31 HCC patients from the cBioPortal database was utilized to evaluate the predictive value of TSG subtypes for immunotherapy response.

RESULTS

Interestingly, TSG non-mutated HCC will have more "immune-hot" tumors, and display the infiltration abundance of immune cells such as B cell, CD4+/CD8+T cell, and neutrophil. Moreover, TSG non-mutated HCC was characterized by the higher expression level of three immune checkpoints, including CD40, CD40LG, and TNFRSF4. In line with the TME characterization and immune checkpoint profiles, TSG non-mutated HCC displayed prolonged overall survival and relapse-free survival, notably, are more likely to respond to immune checkpoint inhibitors.

CONCLUSIONS

Our findings suggested the TSG subtypes could serve as a promising biomarker for guiding surveillance protocol and immunotherapeutic decisions for patients with HCC.

Expression of Lipid-Metabolism Genes Is Correlated With Immune Microenvironment and Predicts Prognosis in Osteosarcoma

Objectives

Osteosarcoma was the most popular primary malignant tumor in children and adolescent, and the 5-year survival of osteosarcoma patients gained no substantial improvement over the past 35 years. This study aims to explore the role of lipid metabolism in the development and diagnosis of osteosarcoma.

Methods

Clinical information and corresponding RNA data of osteosarcoma patients were downloaded from TRGET and GEO databases. Consensus clustering was performed to identify new molecular subgroups. ESTIMATE, TIMER and ssGSEA analyses were applied to determinate the tumor immune microenvironment (TIME) and immune status of the identified subgroups. Functional analyses including GO, KEGG, GSVA and GSEA analyses were conducted to elucidate the underlying mechanisms. Prognostic risk model was constructed using LASSO algorithm and multivariate Cox regression analysis.

Results

Two molecular subgroups with significantly different survival were identified. Better prognosis was associated with high immune score, low tumor purity, high abundance of immune infiltrating cells and relatively high immune status. GO and KEGG analyses revealed that the DEGs between the two subgroups were mainly enriched in immune- and bone remodeling-associated pathways. GSVA and GSEA analyses indicated that, lipid catabolism downregulation and lipid hydroxylation upregulation may impede the bone remodeling and development of immune system. Risk model based on lipid metabolism related genes (LMRGs) showed potent potential for survival prediction in osteosarcoma. Nomogram integrating risk model and clinical characteristics could predict the prognosis of osteosarcoma patients accurately.

Conclusion

Expression of lipid-metabolism genes is correlated with immune microenvironment of osteosarcoma patients and could be applied to predict the prognosis of in osteosarcoma accurately.