Paxillin (PXN) is a protein involved in numerous physiological processes, and its presence is closely related to the occurrence and development of many types of tumors. However, no studies have analyzed PXN from a pancancer perspective. We analyzed PXN expression, immune cell infiltration, prognosis, and biological function across different types of tumors included in The Cancer Genome Atlas and Gene Expression Omnibus datasets. The results showed that expression of PXN varies in different tumors. Expression of PXN strongly correlated with prognosis in patients with tumors; higher PXN expression usually was linked to poor overall and diseasefree survival. Expression of PXN in breast invasive carcinoma and lymphoid neoplasm diffuse large B-cell lymphoma was related to the degree of CD8+ T-cell infiltration, and infiltration of cancer-associated fibroblasts, such as kidney renal papillary cell carcinoma and brain lower-grade glioma, was also observed in other tumors. The results of pan-cancer analysis showed that abnormal PXN expression was related to poor prognosis, immune infiltration, and protein phosphorylation in different tumor types. Therefore, the PXN gene may become a potential biomarker of clinical tumor prognosis.

Oral squamous cell carcinoma (OSCC) is the most invasive oral malignancy in adults and is associated with a poor prognosis. Accurate prognostic models are urgently needed, however, knowledge of the probable mechanisms behind OSCC tumorigenesis and prognosis remain limited. The clinical importance of the interplay between the immune system and tumor microenvironment has become increasingly evident. This study explored immune-related alterations at the multi-omics level to extract accurate prognostic markers linked to the immune response and presents a more accurate landscape of the immune genomic map during OSCC. The Cancer Genome Atlas (TCGA) OSCC cohort (n = 329) was used to detect the immune infiltration pattern of OSCC and categorize patients into two immunity groups using single-sample gene set enrichment analysis (ssGSEA) and hierarchical clustering analysis. Multiple strategies, including lasso regression (LASSO), Cox proportional hazards regression, and principal component analysis (PCA) were used to screen clinically significant signatures and identify an incorporated prognosis model with robust discriminative power on the survival status of both the training and testing set. We identified two OSCC subtypes based on immunological characteristics: Immunity-high and immunity low, and verified that the categorization was accurate and repeatable. Immunity_ high cluster with a higher immunological and stromal score. 1047 differential genes (DEGs) integrate with immune genes to obtain 319 immue-related DEGs. A robust model with five signatures for OSCC patient prognosis was established. The GEO cohort (n = 97) were used to validate the risk model’s predictive value. The low-risk group had a better overall survival (OS) than the high-risk group. Significant prognostic potential for OSCC patients was found using ROC analysis and immune checkpoint gene expression was lower in the low-risk group. We also investigated at the therapeutic sensitivity of a number of frequently used chemotherapeutic drugs in patients with various risk factors. The underlying biological behavior of the OSCC cell line was preliminarily validated. This study characterizes a reliable marker of OSCC disease progression and provides a new potential target for immunotherapy against this disease.

Periodontitis is a globally prevalent disease that imposes a functional and aesthetic burden on patients. The oral microbiome influences human health. The aim of this study was at assessing gender variation in the subgingival bacterial microbiome of elderly patients with initial periodontitis and to determine the causes of this variation. Twelve males and twenty females (range 50–68 years old) with initial periodontitis provided subgingival plaque samples. 16S rRNA gene sequencing, QIIME-based data processing, and statistical analyses were carried out using several different analytical approaches to detect differences in the oral microbiome between the two groups. Males had higher Chao1 index, observed species, and phylogenetic diversity whole tree values than females. Analysis of β-diversity indicated that the samples were reasonably divided by the gender. The linear discriminant analysis effect size showed that the most representative biomarkers were the genus Haemophilus in males, whereas the dominant bacteria in females were Campylobacter. Kyoto Encyclopedia of Genes and Genomes analysis showed that predicting changes in the female oral microbiota may be related to the immune system and immune system diseases are the main factor in males. These data suggest that gender may be a differentiating factor in the microbial composition of subgingival plaques in elderly patients with initial periodontitis. These results could deepen our understanding of the role of gender in the oral microbiota present during initial periodontitis.