The Lung Adenocarcinoma Microenvironment Mining and Its Prognostic Merit

A Novel Gene Signature based on Immune Cell Infiltration Landscape Predicts Prognosis in Lung Adenocarcinoma Patients

BACKGROUND

The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes.

METHODS

For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions.

RESULTS

Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells.

CONCLUSION

We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

Tespa1 deficiency reduces the antitumour immune response by decreasing CD8+T cell activity in a mouse Lewis lung cancer model

Thymocyte-expressed, positive selection-associated 1 (Tespa1) is a key molecule in T-cell development and has been linked to immune diseases. However, its role in antitumour CD8+T cell immunity remains unclear. Here, we demonstrated that Tespa1 plays an important role in antitumour CD8+T cell immunity. First, compared with wild-type (WT) mice, Lewis lung cancer cells grew faster in Tespa1 knockout (Tespa1-/-) mice, with reduced apoptosis, and decreased CD8+T cells in peripheral blood and tumor tissues. Second, the proportion of CD8+T and Th1 cells in the splenocytes of Tespa1-/- mice was lower than that in WT mice. Third, Tespa1-/- CD8+ tumor-infiltrating lymphocytes (TILs) showed weakened proliferation, invasion, cytotoxicity, and protein expression of IL-2 signalling pathway components compared to WT CD8+TILs. Furthermore, PD-1 expression in CD8+TILs was higher in Tespa1-/- than in WT mice. Lastly, CD8+TILs in WT mice improved the antitumour ability of Tespa1-/- mice. In conclusion, these findings suggest that Tespa1 plays a critical role in the tumor immune system by regulating CD8+T cells.

Hypoxia-associated prognostic markers and competing endogenous RNA coexpression networks in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common form of non-small cell lung cancer (NSCLC). Hypoxia has been found in 50-60% of locally advanced solid tumors and is associated with poor prognosis in various tumors, including NSCLC. This study focused on hypoxia-associated molecular hallmarks in LUAD. Fifteen hypoxia-related genes were selected to define the hypoxia status of LUAD by ConsensusClusterPlus based on data from The Cancer Genome Atlas (TCGA). Then, we investigated the immune status under different hypoxia statuses. Subsequently, we constructed prognostic models based on hypoxia-related differentially expressed genes (DEGs), identified hypoxia-related microRNAs, lncRNAs and mRNAs, and built a network based on the competing endogenous RNA (ceRNA) theory. Two clusters (Cluster 1 and Cluster 2) were identified with different hypoxia statuses. Cluster 1 was defined as the hypoxia subgroup, in which all 15 hypoxia-associated genes were upregulated. The infiltration of CD4+ T cells and Tfh cells was lower, while the infiltration of regulatory T (Treg) cells, the expression of PD-1/PD-L1 and TMB scores were higher in Cluster 1, indicating an immunosuppressive status. Based on the DEGs, a risk signature containing 7 genes was established. Furthermore, three differentially expressed microRNAs (hsa-miR-9, hsa-miR-31, hsa-miR-196b) associated with prognosis under different hypoxia clusters and their related mRNAs and lncRNAs were identified, and a ceRNA network was built. This study showed that hypoxia was associated with poor prognosis in LUAD and explored the potential mechanism from the perspective of the gene signature and ceRNA theory.

Cancer-associated fibroblast-related gene signatures predict survival and drug response in patients with colorectal cancer

Background: Cancer-associated fibroblasts (CAFs) play an important role in the tumorigenesis, immunosuppression and metastasis of colorectal cancer (CRC), and can predict poor prognosis in patients with CRC. The present study aimed to construct a CAFs-related prognostic signature for CRC. Methods: The clinical information and corresponding RNA data of CRC patients were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The Estimation of STromal and Immune cells in MAlignant Tumor tissues (ESTIMATES) and xCell methods were applied to evaluate the tumor microenvironment infiltration from bulk gene expression data. Weighted gene co-expression network analysis (WGCNA) was used to construct co-expression modules. The key module was identified by calculating the module-trait correlations. The univariate Cox regression and least absolute shrinkage operator (LASSO) analyses were combined to develop a CAFs-related signature for the prognostic model. Moreover, pRRophetic and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms were utilized to predict chemosensitivity and immunotherapy response. Human Protein Atlas (HPA) databases were employed to evaluate the protein expressions. Results: ESTIMATES and xCell analysis showed that high CAFs infiltration was associated with adverse prognoses. A twenty-gene CAFs-related prognostic signature (CAFPS) was established in the training cohort. Kaplan-Meier survival analyses reveled that CRC patients with higher CAFs risk scores were associated with poor prognosis in each cohort. Univariate and multivariate Cox regression analyses verified that CAFPS was as an independent prognostic factor in predicting overall survival, and a nomogram was built for clinical utility in predicting CRC prognosis. Patients with higher CAFs risk scores tended to not respond to immunotherapy, but were more sensitive to five conventional chemotherapeutic drugs. Conclusion: In summary, the CAFPS could serve as a robust prognostic indicator in CRC patients, which might help to optimize risk stratification and provide a new insight into individual treatments for CRC.

Fe3O4 magnetic nanoparticle-enhanced radiotherapy for lung adenocarcinoma via delivery of siBIRC5 and AS-ODN

Background

Radiotherapy is the mainstay treatment for lung adenocarcinoma, yet remains highly susceptible to resistance. Fe₃O₄ magnetic nanoparticles (MNPs) possess the ability to induce biological therapeutic effects. Herein, the current study set out to explore the effects of Fe₃O₄ MNPs on radiosensitivity of lung adenocarcinoma cells.

Methods

Fe₃O₄ MNPs loaded with both negatively-charged small interfering RNA against baculoviral IAP repeat containing 5 (siBIRC5) and oligodeoxynucleotide antisense (AS-ODN) to generate co-delivery NPs, followed by evaluation. Gel retardation assay was further performed to determine the binding ability of Fe₃O₄ MNPs to AS-ODN/siBIRC5. The radiosensitizing effect of NPs on lung adenocarcinoma cells was determined in the absence or the presence of NPs or radiotherapy. A549 and H460 tumor-bearing mice were established, where tumor tissues were subjected to immunohistochemistry.

Results

NPs were successfully prepared and characterized. BIRC5 expression levels were augmented in tissues of lung cancer patients. Fe₃O₄ MNPs enhanced the uptake of siBIRC5 and AS-ODN by lung adenocarcinoma cells. The presence of NPs under magnetic field reduced the BIRC5 expression and elevated the DR5 expression in lung adenocarcinoma cells. Lung adenocarcinoma cells treated with NPs exhibited inhibited tumor cell migration and increased DNA damage. After magnetic field treatment, tumors were better suppressed in the tumor-bearing mice treated with NPs, followed by radiotherapy.

Conclusion

Findings obtained in our study indicated that Fe₃O₄ MNPs-targeted delivery of siBIRC5 and AS-ODN enhances radiosensitivity, providing an innovative solution for the current clinically existing lung adenocarcinoma patients with radiotherapy resistance with a low risk of toxicity.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-02971-7.

Comprehensive Profiling of Genomic and Transcriptomic Differences between Risk Groups of Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer is the second most frequently diagnosed cancer type and responsible for the highest number of cancer deaths worldwide. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are subtypes of non-small-cell lung cancer which has the highest frequency of lung cancer cases. We aimed to analyze genomic and transcriptomic variations including simple nucleotide variations (SNVs), copy number variations (CNVs) and differential expressed genes (DEGs) in order to find key genes and pathways for diagnostic and prognostic prediction for lung adenocarcinoma and lung squamous cell carcinoma. We performed a univariate Cox model and then lasso-regularized Cox model with leave-one-out cross-validation using The Cancer Genome Atlas (TCGA) gene expression data in tumor samples. We generated 35- and 33-gene signatures for prognostic risk prediction based on the overall survival time of the patients with LUAD and LUSC, respectively. When we clustered patients into high- and low-risk groups, the survival analysis showed highly significant results with high prediction power for both training and test datasets. Then, we characterized the differences including significant SNVs, CNVs, DEGs, active subnetworks, and the pathways. We described the results for the risk groups and cancer subtypes separately to identify specific genomic alterations between both high-risk groups and cancer subtypes. Both LUAD and LUSC high-risk groups have more downregulated immune pathways and upregulated metabolic pathways. On the other hand, low-risk groups have both up- and downregulated genes on cancer-related pathways. Both LUAD and LUSC have important gene alterations such as CDKN2A and CDKN2B deletions with different frequencies. SOX2 amplification occurs in LUSC and PSMD4 amplification in LUAD. EGFR and KRAS mutations are mutually exclusive in LUAD samples. EGFR, MGA, SMARCA4, ATM, RBM10, and KDM5C genes are mutated only in LUAD but not in LUSC. CDKN2A, PTEN, and HRAS genes are mutated only in LUSC samples. The low-risk groups of both LUAD and LUSC tend to have a higher number of SNVs, CNVs, and DEGs. The signature genes and altered genes have the potential to be used as diagnostic and prognostic biomarkers for personalized oncology.