Bioinformatic Analysis Reveals an Immune/Inflammatory-Related Risk Signature for Oral Cavity Squamous Cell Carcinoma

Identification and Validation of Inflammatory Response-Related Gene Signatures to Predict the Prognosis of Neuroblastoma

Background. Neuroblastoma (NB) is the third most common malignant tumor in children. The inflammation is believed to be closely related to NB patients’ prognosis. However, there is no comprehensive research to study the role of inflammatory response-related gene (IRRG) in NB patients. Methods. We downloaded the gene expression profiles of NB patients from GEO and TARGET database, and the expression of 200 IRRGs was extracted. Then, we performed differentially analysis between INSS stage 4 and INSS stage 4S NB patients. The univariate and multivariate Cox regression analyses were performed to screen out the overall survival- (OS-) and event-free survival- (EFS-) related IRRGs in GSE49710, and two signatures were constructed; both signatures were evaluated by Kaplan-Meier (K-M) survival curve and receiver operating characteristic (ROC) curve. Finally, the TARGET cohort was used to validate IRRG signatures, and the independence of the prognostic IRRG signatures was evaluated by integrating clinical information. Results. We screened out 10 OS-related IRRGs and 11 EFS-related IRRGs. Then, we identified that OS- and EFS-related IRRG signatures and found that the OS and EFS of NB patients in the low-risk group were significantly superior than those in the high-risk group (both $P$ value < 0.0001). The AUC values of 3-, 5-, and 7-year OS are 0.910, 0.933, and 0.921, respectively, and 3-, 5-, and 7-year EFS are 0.840, 0.835, and 0.837, respectively. In addition, we found that both IRRG signatures can be used as independent prognostic indicators for patients with NB. Both IRRG signatures still have good predictive ability in validation cohort. Conclusions. We constructed and validated two prognostic gene signatures based on IRRGs. Our study helped us to better understand the role of inflammation in NB and provided new insights for the prognosis assessment and treatment strategy for NB patients.

A Novel Inflammatory-Related Gene Signature Based Model for Risk Stratification and Prognosis Prediction in Lung Adenocarcinoma

Inflammation is an important hallmark of cancer and plays a role in both neogenesis and tumor development. Despite this, inflammatory-related genes (IRGs) remain to be poorly studied in lung adenocarcinoma (LUAD). We aim to explore the prognostic value of IRGs for LUAD and construct an IRG-based prognosis signature. The transcriptomic profiles and clinicopathological information of patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). Least absolute shrinkage and selection operator (LASSO) analysis and multivariate Cox regression were applied in the TCGA set to generate an IRG risk signature. LUAD cases with from the GSE31210 and GSE30219 datasets were used to validate the predictive ability of the signature. Analysis of the TCGA cohort revealed a five-IRG risk signature consisting of EREG, GPC3, IL7R, LAMP3, and NMUR1. This signature was used to divide patients into two risk groups with different survival rates. Multivariate Cox regression analysis verified that the risk score from the five-IRG signature negatively correlated with patient outcome. A nomogram was developed using the IRG risk signature and stage, with C-index values of 0.687 (95% CI: 0.644-0.730) in the TCGA training cohort, 0.678 (95% CI: 0.586-0.771) in GSE30219 cohort, and 0.656 (95% CI: 0.571-0.740) in GSE30219 cohort. Calibration curves were consistent between the actual and the predicted overall survival. The immune infiltration analysis in the TCGA training cohort and two GEO validation cohorts showed a distinctly differentiated immune cell infiltration landscape between the two risk groups. The IRG risk signature for LUAD can be used to predict patient prognosis and guide individual treatment. This risk signature is also a potential biomarker of immunotherapy.

p53 signaling in cancer progression and therapy

The p53 protein is a transcription factor known as the "guardian of the genome" because of its critical function in preserving genomic integrity. The TP53 gene is mutated in approximately half of all human malignancies, including those of the breast, colon, lung, liver, prostate, bladder, and skin. When DNA damage occurs, the TP53 gene on human chromosome 17 stops the cell cycle. If p53 protein is mutated, the cell cycle is unrestricted and the damaged DNA is replicated, resulting in uncontrolled cell proliferation and cancer tumours. Tumor-associated p53 mutations are usually associated with phenotypes distinct from those caused by the loss of the tumor-suppressing function exerted by wild-type p53protein. Many of these mutant p53 proteins have oncogenic characteristics, and therefore modulate the ability of cancer cells to proliferate, escape apoptosis, invade and metastasize. Because p53 deficiency is so common in human cancer, this protein is an excellent option for cancer treatment. In this review, we will discuss some of the molecular pathways by which mutant p53 proteins might perform their oncogenic activities, as well as prospective treatment methods based on restoring tumor suppressive p53 functions.

Immuno-Oncological Biomarkers for Squamous Cell Cancer of the Head and Neck: Current State of the Art and Future Perspectives

The era of immune checkpoint inhibitors has altered the therapeutic landscape in squamous cell cancer of the head and neck (SCCHN). Our knowledge about the tumor microenvironment has fueled the research in SCCHN, leading to several well-known and less-known prognostic and predictive biomarkers. The clinical staging, p16/HPV status, and PD-L1 expression are currently the main tools for assessing the patients' diagnosis and prognosis. However, several novel biomarkers have been thoroughly investigated, some reaching actual significant clinical contributions. The untangling of the immune infiltrate with the subtyping of tissue-associated tumor infiltrating lymphocytes, tumor-associated macrophages, and circulating blood-based biomarkers are an interesting avenue to be further explored and prospectively assessed. Although PD-L1 expression remains the most important response predictor for immune checkpoint inhibitors, several flaws impede proper assessment such as technical issues, different scoring protocol, and intra-, inter-, and temporal heterogeneity. In addition, the construction of an immune-related gene panel has been proposed as a prognostic and predictive stratification but lacks consensus. Recently, the role of microbioma have also been explored regarding its systemic and antitumor immunity. This review gives a comprehensive overview of the aforementioned topics in SCCHN. To this end, the integration of these clinically advantageous biomarkers via construction of an immunogram or nomogram could be an invaluable tool for SCCHN in future prospects.

Immune-related genes play an important role in the prognosis of patients with testicular germ cell tumor

Background

Testicular cancer is a very common malignancy in young men. Although testicular cancer has a high cure rate, patients have a high long-term risk of secondary malignant tumors and cardiovascular disease. In addition, for patients resistant to traditional treatment methods, new treatment methods and methods for predicting prognosis are also urgently needed.

Methods

Gene expression profiles of 165 normal testicular tissues and 156 testicular germ cell tumor (TGCT) tissues from GTEx database and TCGA database were used to obtain differentially expressed genes (DEGs) in TGCT. Through the ImmPort database, we obtained immune-related genes (IRGs). Univariate Cox regression analysis was used to identify prognostic IRGs. A transcription factor regulatory network was constructed to clarify the possible regulatory mechanism for the differential expression of these IRGs. Multivariate Cox regression analysis was used to establish a prognostic model. Gene expression data and related survival data of 108 TCGT patients from GEO database were used for external validation. Survival analysis, receiver operating characteristic curves (ROC) curve analysis, independent prognostic analysis, principal component analysis (PCA) and clinical correlation analysis were performed to evaluate this model.

Results

Three hundred and thirty-three IRGs were differentially expressed between TGCT and normal testicular tissues. We established a prognostic model (riskScore) based on 5 risk genes (SEMA6B, SEMA3G, OBP2B, INSL6 and RETN). Whether in the training cohort, the testing cohort or the entire TCGA cohort, this model could accurately stratify patients with different survival outcomes. The prognostic value of riskScore and 5 risk genes was also confirmed in the GEO database. GSEA analysis showed that DEGs in patients with better prognosis were enriched in immune-related pathways, while DEGs in patients with poorer prognosis were enriched in cancer-related pathways and cardiovascular disease-related pathways. Finally, a new Nomogram with higher prognostic value was constructed to better predict the 1-year PFS, 3-year PFS and 5-year PFS of TCGT patients.

Conclusions

We successfully established an immune-related risk model with high prognostic value and created a new Nomogram. We found that different immune status in tumor microenvironment may be responsible for the different survival outcomes among TGCT patients.