Characterization of a prognostic model for lung squamous cell carcinoma based on eight stemness index-related genes

Human nonunion tissues display differential gene expression in comparison to physiological fracture callus

The healing of bone fractures can become aberrant and lead to nonunions which in turn have a negative impact on patient health. Understanding why a bone fails to normally heal will enable us to make a positive impact in a patient's life. While we have a wealth of molecular data on rodent models of fracture repair, it is not the same with humans. As such, there is still a lack of information regarding the molecular differences between normal physiological repair and nonunions. This study was designed to address this gap in our molecular knowledge of the human repair process by comparing differentially expressed genes (DEGs) between physiological fracture callus and two different nonunion types, hypertrophic (HNU) and oligotrophic (ONU). RNA sequencing data revealed over ∼18,000 genes in each sample. Using the physiological callus as the control and the nonunion samples as the experimental groups, bioinformatic analyses identified 67 and 81 statistically significant DEGs for HNU and ONU, respectively. Out of the 67 DEGs for the HNU, 34 and 33 were up and down-regulated, respectively. Similarly, out of the 81 DEGs for the ONU, 48 and 33 were up and down-regulated, respectively. Additionally, we also identified common genes between the two nonunion samples; 8 (10.8 %) upregulated and 12 (22.2 %) downregulated. We further identified many biological processes, with several statistically significant ones. Some of these were related to muscle and were common between the two nonunion samples. This study represents the first comprehensive attempt to understand the global molecular events occurring in human nonunion biology. With further research, we can perhaps decipher new molecular pathways involved in aberrant healing of human bone fractures that can be therapeutically targeted.

Identification of Novel Stemness-based Subtypes and Construction of a Prognostic Risk Model for Patients with Lung Squamous Cell Carcinoma

BACKGROUND

Although cancer stem cells (CSCs) contribute to tumorigenesis, progression, and drug resistance, stemness-based classification and prognostic signatures of lung squamous cell carcinoma (LUSC) remain unclarified. This study attempted to identify stemness-based subtypes and develop a prognostic risk model for LUSC.

METHODS

Based on RNA-seq data from The Cancer Genome Atlas (TCGA), Gene-Expression Omnibus (GEO) and Progenitor Cell Biology Consortium (PCBC), mRNA expression-based stemness index (mRNAsi) was calculated by one-class logistic regression (OCLR) algorithm. A weighted gene coexpression network (WGCNA) was employed to identify stemness subtypes. Differences in mutation, clinical characteristics, immune cell infiltration, and antitumor therapy responses were determined. We constructed a prognostic risk model, followed by validations in GEO cohort, pan-cancer and immunotherapy datasets.

RESULTS

LUSC patients with subtype C2 had a better prognosis, manifested by higher mRNAsi, higher tumor protein 53 (TP53) and Titin (TTN) mutation frequencies, lower immune scores and decreased immune checkpoints. Patients with subtype C2 were more sensitive to Imatinib, Pyrimethamine, and Paclitaxel therapy, whereas those with subtype C1 were more sensitive to Sunitinib, Saracatinib, and Dasatinib. Moreover, we constructed stemness-based signatures using seven genes (BMI1, CCDC51, CTNS, EIF1AX, FAM43A, THBD, and TRIM68) and found high-risk patients had a poorer prognosis in the TCGA cohort. Similar results were found in the GEO cohort. We verified the good performance of risk scores in prognosis prediction and therapy responses.

CONCLUSION

The stemness-based subtypes shed novel insights into the potential roles of LUSC-stemness in tumor heterogeneity, and our prognostic signatures offer a promising tool for prognosis prediction and guide therapeutic decisions in LUSC.

Comprehensive analysis of TLX2 in pan cancer as a prognostic and immunologic biomarker and validation in ovarian cancer

T cell leukemia homeobox 2 (TLX2) plays an important role in some tumors. Bioinformatics and experimental validation represent a useful way to explore the mechanisms and functions of TLX2 gene in the cancer disease process from a pan cancer perspective. TLX2 was aberrantly expressed in pan cancer and cell lines and correlated with clinical stage. High TLX2 expression was significantly associated with poor overall survival in COAD, KIRC, OC, and UCS. The greatest frequency of TLX2 alterations in pan cancer was amplification. Alterations of NXF2B, MSLNL, PCGF1, INO80B-WBP1, LBX2-AS1, MRPL53, LBX2, TTC31, WDR54, and WBP1 co-occurred in the TLX2 alteration group. PFS was significantly shorter in the TLX2-altered group (n = 6) compared to the TLX2-unaltered group (n = 400). Methylation levels of TLX2 were high in 17 tumors. TLX2 expression was associated with MSI in seven tumors and TMB in five tumors. TLX2 expression was associated with immune infiltration and immune checkpoint genes. TLX2 may be associated with some pathways and chemoresistance. We constructed a possible competing endogenous RNA (ceRNA) network of LINC01010/miR-146a-5p/TLX2 in OC. TLX2 expression was significantly upregulated in ovarian cancer cell lines compared to ovarian epithelial cell lines. Aberrant expression of TLX2 in pan cancer may promote tumorigenesis and progression through different mechanisms. TLX2 may represent an important therapeutic target for human cancers.

Construction and validation of a prognosis signature based on the immune microenvironment in gastric cancer

Background

Gastric cancer (GC) is an aggressive malignant tumor with a high degree of heterogeneity, and its immune microenvironment is closely associated with tumor growth, development and drug resistance. Therefore, a classification system of gastric cancer based explicitly on the immune microenvironment context might enrich the strategy for gastric cancer prognosis and therapy.

Methods

A total of 668 GC patients were collected from TCGA-STAD (n = 350), GSE15459 (n = 192), GSE57303 (n = 70) and GSE34942 (n = 56) datasets. Three immune-related subtypes (immunity-H, -M, and -L) were identified by hierarchical cluster analysis based on the ssGSEA score of 29 immune microenvironment-related gene sets. The immune microenvironment-related prognosis signature (IMPS) was constructed via univariate Cox regression, Lasso-Cox regression and multivariate Cox regression, and nomogram model combining IMPS and clinical variables was further constructed by the “rms” package. RT-PCR was applied to validate the expression of 7 IMPS genes between two human GC cell lines (AGS and MKN45) and one normal gastric epithelial cell line (GES-1).

Results

The patients classified as immunity-H subtype exhibited highly expressed immune checkpoint and HLA-related genes, with enriched naïve B cells, M1 macrophages and CD8 T cells. We further constructed and validated a 7-gene (CTLA4, CLDN6, EMB, GPR15, ENTPD2, VWF and AKR1B1) prognosis signature, termed as IMPS. The patients with higher IMPS expression were more likely to be associated with higher pathology grade, more advanced TNM stages, higher T and N stage, and higher ratio of death. In addition, the prediction values of the combined nomogram in predicting 1-year (AUC = 0.750), 3-year (AUC = 0.764) and 5-year (AUC = 0.802) OS was higher than IMPS and individual clinical characteristics.

Conclusions

The IMPS is a novel prognosis signature associated with the immune microenvironment and clinical characteristics. The IMPS and the combined nomogram model provide a relatively reliable predictive index for predicting the survival outcomes of gastric cancer.