Screening and identification of key biomarkers of papillary renal cell carcinoma by bioinformatic analysis

Bioinformatics screening of prognostic immune-related genes in renal clear cell carcinoma

This study aims to harness bioinformatics to identify prognostic immune-related genes in clear cell renal cell carcinoma (ccRCC), focusing particularly on LILRB3. It evaluates LILRB3's expression in ccRCC, its association with patient prognosis, and its potential as a biomarker for predicting survival, thereby providing a preliminary basis for the diagnosis of ccRCC. Utilizing The Cancer Genome Atlas (TCGA) datasets and an immune gene set, we sought immune-related genes with elevated expression in ccRCC. Seventy-two normal tissue samples and 531 ccRCC samples were analyzed, and differential genes were identified with a screening criterion of fold change (FC) > 2 and P value < 0.01. Survival analysis and receiver operating characteristic (ROC) curve analysis were employed to discover genes of prognostic and diagnostic relevance to ccRCC. Pearson correlation analysis with a cutoff of |r|≥ 0.5, facilitated by cBioPortal, assessed genes co-expressed with LILRB3. The DAVID online tool conducted functional and pathway enrichment analyses for LILRB3-coexpressed genes. The TIMER and TCIA databases were utilized to explore LILRB3's influence on immune infiltration in the tumor microenvironment and its relation to key immunological checkpoints. Screening the TCGA database revealed 3719 up-regulated differential genes in ccRCC, with 355 overlapping immune-related genes. Survival analysis of these 355 genes revealed 100 with significant survival impact. ROC curve analysis pinpointed the top 10 genes, including LILRB3, with the highest diagnostic efficiency. LILRB3 emerged as an independent risk factor from the Cox risk regression model. GO and KEGG analyses linked LILRB3 to various biological processes, including chemokine signaling pathways, immunological response, antigen processing and presentation, inflammatory response, T cell co-stimulation, and signal transduction. LILRB3 significantly affected ccRCC immune infiltration and correlated positively with several immunological checkpoints, such as PD-1, LAG3, IDO1, PD-L1, CTLA4, TIM3, TIGIT, and VISTA. LILRB3 shows higher expression levels in ccRCC than in normal tissues and correlates with poor patient prognosis. Its impactful role in the immune infiltration of the RCC microenvironment suggests that LILRB3 could serve as a novel target for ccRCC treatment and prognosis, underlining its diagnostic and prognostic significance.

Transcriptomic analysis of Porphyromonas gingivalis-infected head and neck cancer cells: Identification of PLAU as a candidate prognostic biomarker

Periodontal disease is a risk factor for head and neck squamous cell carcinoma (HNSCC), and Porphyromonas gingivalis, a major periodontal pathogen, has been identified as a specific and potentially independent microbial factor that increases the risk of cancer mortality. Gene expression in HNSCC due to P. gingivalis infection and how changes in gene expression affect the prognosis of HNSCC patients are not clarified. When P. gingivalis was cultured with HNSCC cells, it efficiently adhered to these cells and enhanced their invasive ability. A transcriptome analysis of P. gingivalis -infected HNSCC cells showed that genes related to migration, including CCL20, CITED2, CTGF, C8orf44-SGK3, DUSP10, EGR3, FUZ, HBEGF, IL1B, IL24, JUN, PLAU, PTGS2, P2RY1, SEMA7A, SGK1 and SIX2, were highly up- or down-regulated. The expression of up-regulated genes was examined using the expression data of HNSCC patients obtained from The Cancer Genome Atlas (TCGA) database, and the expression of 5 genes, including PLAU, was found to be higher in cancer tissue than in solid normal tissue. An analysis of protein-protein interactions revealed that these 5 genes formed a dense network. A Cox regression analysis showed that high PLAU expression levels were associated with a poor prognosis in patients with TCGA-HNSCC. Furthermore, the prognostic impact correlated with tumour size and the presence or absence of lymph node metastasis. Collectively, these results suggest the potential of PLAU as a molecular prognostic marker in HNSCC patients. Further in vivo and in vitro studies are needed to verify the findings of this study.

Integrated transcriptome and network analysis identifies EZH2/CCNB1/PPARG as prognostic factors in breast cancer

Breast cancer (BC) has high morbidity, with significant relapse and mortality rates in women worldwide. Therefore, further exploration of its pathogenesis is of great significance. This study selected therapy genes and possible biomarkers to predict BC using bioinformatic methods. To this end, the study examined 21 healthy breasts along with 457 BC tissues in two Gene Expression Omnibus (GEO) datasets and then identified differentially expressed genes (DEGs). Survival-associated DEGs were screened using the Kaplan-Meier curve. Based on Gene Ontology (GO) annotation, survival-associated DEGs were mostly associated with cell division and cellular response to hormone stimulus. The enriched Kyoto Encyclopedia of Gene and Genome (KEGG) pathway was mostly correlated with cell cycle and tyrosine metabolism. Using overlapped survival-associated DEGs, a survival-associated PPI network was constructed. PPI analysis revealed three hub genes (EZH2, CCNB1, and PPARG) by their degree of connection. These hub genes were confirmed using The Cancer Genome Atlas (TCGA)-BRCA dataset and BC tissue samples. Through Gene Set Enrichment Analysis (GSEA), the molecular mechanism of the potential therapy and prognostic genes were evaluated. Thus, hub genes were shown to be associated with KEGG_CELL_CYCLE and VANTVEER_BREAST_CANCER_POOR_PROGNOSIS gene sets. Finally, based on integrated bioinformatics analysis, this study identified three hub genes as possible prognostic biomarkers and therapeutic targets for BC. The results obtained further understanding of the underground molecular mechanisms related to BC occurrence and prognostic outcomes.