The tumor environment immune phenotype of LUSC by genome-wide analysis

A novel tumor microenvironment-related gene signature with immune features for prognosis of lung squamous cell carcinoma

PURPOSE

Lung squamous cell carcinoma (LUSC) is an aggressive subset of non-small-cell lung cancer (NSCLC). The tumor microenvironment (TME) plays an important role in the development of LUSC. We aim to identify potential therapeutic targets and a TME-related prognostic signature and for LUSC.

METHODS

TME-related genes were obtained from TCGA-LUSC dataset. LUSC samples were clustered by the non-negative matrix clustering algorithm (NMF). The prognostic signature was constructed through univariate Cox regression, multivariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) analyses. Gene set enrichment analysis (GSEA) was carried out to explore the enrichment pathways.

RESULTS

This study constructed a prognostic signature which contained 12 genes: HHIPL2, PLK4, SLC6A4, LSM1, TSLP, P4HA1, AMH, CLDN5, NRTN, CDH2, PTGIS, and STX1A. Patients were classified into high-risk and low-risk groups according to the median risk score of this signature. Compared with low-risk group patients, patients in high-risk group patients had poorer overall survival, which demonstrated this signature was an independent prognostic factor. Besides, correlation analysis and GSEA results revealed that genes of this signature were correlated with immune cells and drug response.

CONCLUSION

Our novel signature based on 12 TME-related genes might be applied as an independent prognostic indicator. Importantly, the signature could be a promising biomarker and accurately predict the prognosis of LUSC patients.

Transcriptome and pan-cancer system analysis identify PM2.5-induced stanniocalcin 2 as a potential prognostic and immunological biomarker for cancers

Epidemiological studies have shown that air pollution and particulate matter (PM) are closely related to the occurrence of cancer. However, the potential prognostic and immunological biomarkers for air pollution related cancers are lacking. In this study, we proved PM2.5 exposure was correlated with lung cancer through transcriptome analysis. Importantly, we identified STC2 as a key gene regulated by PM2.5, whose expression in epithelial cells was significantly increased after PM2.5 treatment and validated by using RT-qPCR and immunofluorescence. Kaplan-Meier OS curves suggested that high STC2 expression positively correlated with a poor prognosis in lung cancer. Furthermore, we discovered that STC2 was associated with multiple cancers and pathways in cancer. Next, Pan-Cancer Expression Landscape of STC2 showed that STC2 exhibited inconsistent expression across 26 types of human cancer, lower in KIRP in cancer versus adjacent normal tissues, and significantly higher in another cancers. Cox regression results suggested that STC2 expression was positively or negatively associated with prognosis in different cancers. Moreover, STC2 expression was associated with clinical phenotypes including age, gender, stage and grade. Mutation features of STC2 were also analyzed, in which the highest alteration frequency of STC2 was presented in KIRC with amplification. Meanwhile, the effects of copy number variation (CNV) on STC2 expression were investigated across various tumor types, suggesting that STC2 expression was significantly correlated with CNV in tumors. Additionally, STC2 was closely related to tumor heterogeneity, tumor stemness and tumor immune microenvironment like immune cell infiltration. In the meantime, we analyzed methylation modifications and immunological correlation of STC2. The results demonstrated that STC2 expression positively correlated with most RNA methylation genes and immunomodulators across tumors. Taken together, the findings revealed that PM2.5-induced STC2 might be a potential prognostic and immunological biomarker for cancers related to air pollution.

Pan-Cancer Analysis of B4GALNT1 as a Potential Prognostic and Immunological Biomarker

Background. Gangliosides act as important roles in tumor progression. B4GALNT1 is a key enzyme in ganglioside biosynthesis. B4GALNT1 expression is linked to tumorigenesis and the prognosis of tumor patients. Nevertheless, the role of B4GALNT1 in pan-cancer remains unclear. Methods. Several databases, including TCGA, GEO, GTEx, NCI-60, and TIMER, were searched. Methods including correlation analysis, Cox regression analysis, and Kaplan-Meier analysis were used to explore the expression pattern, prognosis, tumor infiltration pattern, genetics and epigenetics, and drug sensitivity of B4GALNT1 in pan-cancer patients from the above datasets. Results. B4GALNT1 was found to be aberrantly expressed in multiple types of tumors. The survival status of tumor patients was significantly related to B4GALNT1 expression, but the correlations were tumor-specific. Moreover, the expression of B4GALNT1 was associated with ImmuneScore and StromalScore in 21 and 27 tumor types, respectively. Also, B4GALNT1 was significantly associated with TMB, MSI, MMR, and DNA methylation. Additionally, the sensitivity of 9 drugs was correlated with the expression of B4GALNT1. Conclusion. A correlation of B4GALNT1 expression with prognosis exists in multiple types of cancers. In addition, B4GALNT1 expression may play a role in TME and tumor immunity regulation. Further investigation of the biological mechanisms of its different roles in tumorigenesis and clinical application as a biomarker is still required.

Multiple Machine Learning Methods Reveal Key Biomarkers of Obstructive Sleep Apnea and Continuous Positive Airway Pressure Treatment

Obstructive sleep apnea (OSA) is a worldwide health issue that affects more than 400 million people. Given the limitations inherent in the current conventional diagnosis of OSA based on symptoms report, novel diagnostic approaches are required to complement existing techniques. Recent advances in gene sequencing technology have made it possible to identify a greater number of genes linked to OSA. We identified key genes in OSA and CPAP treatment by screening differentially expressed genes (DEGs) using the Gene Expression Omnibus (GEO) database and employing machine learning algorithms. None of these genes had previously been implicated in OSA. Moreover, a new diagnostic model of OSA was developed, and its diagnostic accuracy was verified in independent datasets. By performing Single Sample Gene Set Enrichment Analysis (ssGSEA) and Counting Relative Subsets of RNA Transcripts (CIBERSORT), we identified possible immunologic mechanisms, which led us to conclude that patients with high OSA risk tend to have elevated inflammation levels that can be brought down by CPAP treatment.

Identification of Significant Secreted or Membrane-Located Proteins in Laryngeal Squamous Cell Carcinoma

Background. This study is aimed at investigating the expressions and prognostic values of secreted or membrane-located proteins (SMPs) in laryngeal squamous cell carcinoma (LSCC). The correlations between the expressions of SMPs and immune cells’ infiltrations were also investigated. Methods. The expression data of normal laryngeal and LSCC samples were obtained from the TCGA and GEO datasets. The differentially expressed SMPs were identified, and their prognostic values were analyzed. The biological functions of differentially expressed and worse-survival-related SMPs were explored. LASSO regression, Cox multivariate analysis, and nomogram were used to construct a model to predict the survival. Then, the infiltrations of the 24 immune cell populations were calculated using the GSVA method, and the correlations between the expression of SMPs and the immune infiltration were investigated. Results. 122 samples (12 normal and 120 LSCC) of the TCGA database and 114 samples (57 normal and 57 LSCC) of GSE127165 were included. We identified that 138 SMPs were significantly upregulated in LSCC samples of both the TCGA and GEO datasets, among which 52 SMPs were significantly correlated with worse survival. GO and KEGG analyses revealed those 52 SMPs significantly participate in tumor microenvironment and immune cells’ communication. Nine of 52 SMPs (ABCC5, ATP1B3, CLEC11A, FLNA, FSTL3, MMP1, NME1, OAS3, and PHLDB2) were included in the nomogram to effectively and accurately predict the LSCC patients’ survival. The expressions of most SMPs, such as MMP1 and FSTL3, were significantly positively correlated with the immune infiltration of LSCC. Conclusions. In this study, the expression, prognostic values, and correlations with immune infiltration of SMPs were analyzed in LSCC samples. Our analyses identified several significant SMPs differentially expressed between normal laryngeal and LSCC samples, correlated with worse survival, and related to the immune infiltration.

Identification and Validation of Immune Infiltration Phenotypes in Laryngeal Squamous Cell Carcinoma by Integrative Multi-Omics Analysis

Background

Laryngeal squamous cell carcinoma (LSCC) is one of the world’s most common head and neck cancer. However, the immune infiltration phenotypes of LSCC have not been well investigated.

Methods

The multi-omics data of LSCC were obtained from the TCGA (n=111) and GEO (n=57) datasets. The infiltrations of the 24 immune cell populations were calculated using the GSVA method. Then LSCC samples with different immune cell infiltrating patterns were clustered, and the multi-omics differences were investigated.

Results

Patients were clustered into the high-infiltration and low-infiltration groups. The infiltration scores of most immune cells were higher in the high-infiltration group. Patients with high-infiltration phenotype have high N and TNM stages but better survival, as well as less mutated COL11A1 and MUC17. Common targets of immunotherapies such as PD1, PDL1, LAG3, and CTLA4 were significantly up-regulated in the high-infiltration group. The differentially expressed genes were mainly enriched in several immune-related GOs and KEGG pathways. Based on the genes, miRNAs, and lncRNAs differentially expressed in both the TCGA and GEO cohorts, we built a ceRNA network, in which BTN3A1, CCR1, miR-149-5p, and so on, located at the center. A predictive model was also constructed to calculate a patient’s immune infiltration phenotype using 16 genes’ expression values, showing excellent accuracy and specificity in the TCGA and GEO cohorts.

Conclusions

In this study, the immune infiltration phenotypes of LSCC and the corresponding multi-omics differences were explored. Our model might be valuable to predicting immunotherapy’s outcome.