Integrative analysis-based identification and validation of a prognostic immune cell infiltration-based model for patients with advanced gastric cancer

Construction of novel 7 integrin-related gene signatures in thyroid cancer construction of model based on integrin genes

Advanced and metastatic THCA patients usually have a poor prognosis. Thus, this study aimed to establish a risk model to discriminate the high risk population. The expression and clinical data were obtained from TCGA database. The cluster analysis, lasso, univariate and multivariate cox analyses were used to construct risk model. K-M, ROC and DCA were applied to validate the efficiency and stability of the model. GO, KEGG, and ssGSEA analysis were performed to identify the potential mechanism of signatures. The 7-gene prognosis model was constructed, including FAM27E3, FIGN, GSTM4, BEX5, RBPMS2, PHF13, and DCSTAMP. ROC and DCA results showed our model had a better prognosis prediction performance than other risk models. The high risk score was associated with the poor prognosis of THCA patients with different clinical characteristics. The risk score was closely related to cell cycle. Further, we found that the expressions of signatures were significantly dysregulated in THCA and associated with prognosis. These gene expressions were affected by some clinical characteristics, methylation and CNV. Some signatures played a role in drug sensitivity and pathway activation. We constructed a 7-gene signature model based on the integrin-related genes, which showed a great prognostic value in THCA.

Mitochondrial-related genes markers that predict survival in patients with head and neck squamous cell carcinoma affect immunomodulation through hypoxia, glycolysis, and angiogenesis pathways

Mitochondria play a crucial role in the occurrence and development of tumors. We used mitochondria-related genes for consistent clustering to identify three stable molecular subtypes of head and neck squamous cell carcinoma (HNSCC) with different prognoses, mutations, and immune characteristics. Significant differences were observed in clinical characteristics, immune microenvironment, immune cell infiltration, and immune cell scores. TP53 was the most significantly mutated; cell cycle-related pathways and tumorigenesis-related pathways were activated in different subtypes. Risk modeling was conducted using a multifactor stepwise regression method, and nine genes were identified as mitochondria-related genes affecting prognosis (DKK1, EFNB2, ITGA5, AREG, EPHX3, CHGB, P4HA1, CCND1, and JCHAIN). Risk score calculations revealed significant differences in prognosis, immune cell scores, immune cell infiltration, and responses to conventional chemotherapy drugs. Glycolysis, angiogenesis, hypoxia, and tumor-related pathways were positively correlated with the RiskScore. Clinical samples were subjected to qPCR to validate the results. In this work, we constructed a prognostic model based on the mitochondrial correlation score, which well reflects the risk and positive factors for the prognosis of patients with HNSCC. This model can be used to guide individualized adjuvant and immunotherapy in patients with HNSCC.

Quantitative Analysis on Molecular Characteristics Evolution of Gastric Cancer Progression and Prognosis

The dynamic changes of key biological characteristics from gastric low-grade intraepithelial neoplasia (LGIN) to high-grade intraepithelial neoplasia (HGIN) to early gastric cancer (EGC) are still unclear, which greatly affect the accurate diagnosis and treatment of EGC and prognosis evaluation of gastric cancer (GC). In this study, bioinformatics methods/tools are applied to quantitatively analyze molecular characteristics evolution of GC progression, and a prognosis model is constructed. This study finds that some dysregulated differentially expressed mRNAs (DEmRNAs) in the LGIN stage may continue to promote the occurrence and development of EGC. Among the LGIN, HGIN, and EGC stages, there are differences and relevance in the transcription expression patterns of DEmRNAs, and the activation related to immune cells is very different. The biological functions continuously changed during the progression from LGIN to HGIN to EGC. The COX model constructed based on the three EGC-related DEmRNAs has GC prognostic risk prediction ability. The evolution of biological characteristics during the development of EGC mined by the authors provides new insight into understanding the molecular mechanism of EGC occurrence and development. The three-gene prognostic risk model provides a new method for assisting GC clinical treatment decisions.

The Plasma DIA-Based Quantitative Proteomics Reveals the Pathogenic Pathways and New Biomarkers in Cervical Cancer and High Grade Squamous Intraepithelial Lesion

OBJECTIVE

The process of normal cervix changing into high grade squamous intraepithelial lesion (HSIL) and invasive cervical cancer is long and the mechanisms are still not completely clear. This study aimed to reveal the protein profiles related to HSIL and cervical cancer and find the diagnostic and prognostic molecular changes.

METHODS

Data-independent acquisition (DIA) analysis was performed to identify 20 healthy female volunteers, 20 HSIL and 20 cervical patients in a cohort to screen differentially expressed proteins (DEPs) for the HSIL and cervical cancer. Subsequently, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used for functional annotation of DEPs; the protein-protein interaction (PPI) and weighted gene co-expression network analysis (WGCNA) were performed for detection of key molecular modules and hub proteins. They were validated using the Enzyme-Linked Immunosorbent Assay (ELISA).

RESULTS

A total of 243 DEPs were identified in the study groups. GO and KEGG analysis showed that DEPs were mainly enriched in the complement and coagulation pathway, cholesterol metabolism pathway, the IL-17 signaling pathway as well as the viral protein interaction with cytokine and cytokine receptor pathway. Subsequently, the WGCNA analysis showed that the green module was highly correlated with the cervical cancer stage. Additionally, six interesting core DEPs were verified by ELISA, APOF and ORM1, showing nearly the same expression pattern with DIA. The area under the curve (AUC) of 0.978 was obtained by using ORM1 combined with APOF to predict CK and HSIL+CC, and in the diagnosis of HSIL and CC, the AUC can reach to 0.982. The high expression of ORM1 is related to lymph node metastasis and the clinical stage of cervical cancer patients as well as the poor prognosis.

CONCLUSION

DIA-ELSIA combined analysis screened and validated two previously unexplored but potentially useful biomarkers for early diagnosis of HSIL and cervical cancer, as well as possible new pathogenic pathways and therapeutic targets.