Developing and validating the model of tumor-infiltrating immune cell to predict survival in patients receiving radiation therapy for head and neck squamous cell carcinoma.

Background: Radiotherapy (RT) is a mainstay of head and neck squamous cell carcinoma (HNSCC) treatment. Due to the influence of RT on tumor cells and immune/stromal cells in microenvironment, some studies suggest that immunologic landscape could shape treatment response. To better predict the survival based on genomic data, we developed a prognostic model using tumor-infiltrating immune cell (TIIC) signature to predict survival in patients undergoing RT for HNSCC. Methods: Gene expression data and clinical information were downloaded from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Data from HNSCC patients undergoing RT were extracted for analysis. TIICs prevalence in HNSCC patients was quantified by gene set variation analysis (GSVA) algorithm. TIICs and post-RT survival were analyzed using univariate Cox regression analysis and used to construct and validate a tumor-infiltrating cells score (TICS). Results: Five of 26 immune cells were significantly associated with HNSCC prognosis in the training cohort (all P<0.05). Kaplan-Meier (KM) survival curves showed that patients in the high TICS group had better survival outcomes (log-rank test, P<0.05). Univariate analyses demonstrated that the TICS had independent prognostic predictive ability for RT outcomes (P<0.05). Patients with high TICS scores showed significantly higher expression of immune-related genes. Functional pathway analyses further showed that the TICS was significantly related to immune-related biological process. Stratified analyses supported integrating TICS and tumor mutation burden (TMB) into individualized treatment planning, as an adjunct to classification by clinical stage and human papillomavirus (HPV) infection. Conclusions: The TICS model supports a personalized medicine approach to RT for HNSCC. Increased prevalence of TIIC within the tumor microenvironment (TME) confers a better prognosis for patients undergoing treatment for HNSCC.

A polyamine metabolism risk signature for predicting the prognosis and immune therapeutic response of kidney cancer.

Background: Polyamine metabolism is critically involved in the proliferation and metastasis of tumor cells, including in kidney renal clear cell (KIRC) cancer. However, the molecular mechanisms underlying the effect of polyamines in KIRC cancer remain largely unknown. Methods: The messenger RNA (mRNA) expression profile of KIRC was downloaded from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and ArrayExpress database. Differential expression analysis was performed with the "limma" package in R. Univariate Cox regression and multivariable Cox regression were used to estimate correlation between variables and prognosis. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was employed to screen variables and construct a risk signature. A nomogram model was established using the risk signature and clinical variables. Receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA) were used to assess the predicted accuracy and clinical benefit of the model. Results: We identified nine differentially expressed polyamine metabolism-related genes (PMRGs) in TCGA-KIRC. Of these, six were closely associated with patients' outcomes. These six genes participated in different pathways and originated from different cell types within the tumor microenvironment (TME). Using the mRNA expression values of these genes, we constructed a 4-gene PMRG risk signature. Patients with high PMRG risk exhibited worse outcomes, and our analysis showed that the PMRG risk signature was an independent prognostic factor when clinical information was used as a covariate. We also found that multiple immune- or metabolism-related pathways were differentially enriched in high or low PMRG risk groups, suggesting that altering these pathways could lead to different clinical outcomes. Finally, in two external datasets, we found that the PMRG risk signature could predict the response of patients to immune therapy. Conclusions: In summary, our study identified several potentially important PMRGs in KIRC and constructed a practical risk signature, which could serve as a foundation for further development of polyamine metabolism-based targeted therapies for KIRC.

Weighted gene co-expression network analysis reveals specific modules and hub genes related to immune infiltration of osteoarthritis.

BACKGROUND: The incidence of osteoarthritis (OA), a chronic degenerative disease, is increasing every year. There is no effective clinical treatment for OA and the pathological mechanism remains unclear. Early diagnosis is an effective strategy to control the progress of OA. In this study, we aimed to identify potential early diagnostic biomarkers. METHODS: We downloaded the gene expression profile dataset, GSE51588 and GSE55235, from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO) public database. The differentially expressed genes (DEGs) were screened out using the "limma" R package. Weighted gene co-expression network analysis (WGCNA) was utilized to build the co-expression network between the normal and OA samples. A Venn diagram was constructed to detect the hub genes. Potential molecular mechanisms and signaling pathways were enriched by gene set variation analysis (GSVA). Single sample gene set enrichment analysis (ssGSEA) was used to identify the immune infiltration of OA. RESULTS: We screened out three hub genes based on WGCNA and DEGs in this study. GSVA results showed that nuclear factor interleukin-3 (NFIL3) was related to tumor necrosis factor alpha (TNF-α) signaling via nuclear factor kappa-B (NF-κB), the reactive oxygen species pathway, and myelocytomatosis (MYC) targets v2. Highly-expressed ADM (adrenomedullin) pathways included TNF-α signaling via NF-κB, the reactive oxygen species pathway, and ultraviolet (UV) response up. OGN (osteoglycin)-enriched pathways included epithelial mesenchymal transition, coagulation, and peroxisome. CONCLUSIONS: We identified three hub genes (NFIL3, ADM, and OGN) that were correlated to the development and progression of OA, which may provide new biomarkers for early diagnosis.

Characteristics of hypoxic tumor microenvironment in non-small cell lung cancer, involving molecular patterns and prognostic signature.

BACKGROUND: The mechanisms of hypoxia or immune microenvironment in cancer have been studied respectively, but the role of hypoxia immune microenvironment in non-small cell lung cancer (NSCLC) still needs further exploration. METHODS: By applying the K-means algorithm, 1,121 patients with NSCLC were divided into three categories. We evaluated the constructed signature in order to link it with the prognosis, which was constructed by univariate and least absolute shrinkage operator (LASSO) Cox regression analysis. RESULTS: A total of three clusters were obtained by clustering five Gene Expression Omnibus (GEO) data sets. Gene Set Variation Analysis (GSVA) and immune infiltration analysis were performed to explore the biological behavior. Cluster one presented an activated state of oncogenic pathways, and compared with the other two clusters, the median risk score was the highest, which was the reason for its poor survival. Cluster three showed that the immune pathway was active and the median risk score was the lowest, so the survival was the best. However, cluster two presented a state in which both immune and matrix pathways were activate. This was manifested as mutual antagonism, and its risk score was in the middle. Its survival was in the middle. CONCLUSIONS: This work revealed the role of hypoxia related genes (HRGs) modification in tumor microenvironment, which was conducive to our comprehensive analysis of the prognosis of NSCLC, and provided direction and guidance for clinical immunotherapy.

lncRNA OSTN-AS1 May Represent a Novel Immune-Related Prognostic Marker for Triple-Negative Breast Cancer Based on Integrated Analysis of a ceRNA Network.

The competing endogenous RNA (ceRNA) networks are an effective method for investigating cancer; however, construction of ceRNA networks among different subtypes of breast cancer has not been previously performed. Based on analysis of differentially expressed RNAs between 150 triple-negative breast cancer (TNBC) tissues and 823 non-triple-negative breast cancer (nTNBC) tissues downloaded from TCGA database, a ceRNA network was constructed based on database comparisons using Cytoscape. Survival analysis and receiver operating characteristic curve data were combined to screen out prognostic candidate genes, which were subsequently analyzed using co-expressed functionally related analysis, Gene Set Variation Analysis (GSVA) pathway-related analysis, and immune infiltration and tumor mutational burden immune-related analysis. A total of 190 differentially expressed lncRNAs (DElncRNAs), 48 differentially expressed mRNAs (DEmRNAs), and 13 differentially expressed miRNAs (DEmiRNAs) were included in the ceRNA network between TNBC and nTNBC subtypes. Gene ontology analysis of mRNAs coexpressed with prognostic candidate lncRNAs (AC104472.1, PSORS1C3, DSCR9, OSTN-AS1, AC012074.1, AC005035.1, SIAH2-AS1, and ERVMER61-1) were utilized for functional prediction. Consequently, OSTN-AS1 was primarily related to immunologic function, for instance, immune cell infiltration and immune-related markers coexpression. The GSVA deviation degree was increased with OSTN increased expression. In addition, many important immune molecules, such as PDCD1 and CTLA-4, were strongly correlated in terms of their quantitative expression. Competing endogenous RNA networks may identify candidate therapeutic targets and potential prognostic biomarkers in breast cancer. In particular, OSTN-AS1 serves as a novel immune-related molecule and could be involved in immunotherapy efforts in the future.