Data Collection
We obtained the genomic and clinicopathological data of 33 cancer cases from the University of California Santa Cruz Xena Explorer (TCGA Pan-Cancer cohort) and The Cancer Genome Atlas (TCGA) database (https://portal.gdc.cancer.gov/). Moreover, we retrieved somatic mutation information from the TCGA database. To assemble the therapeutic group, we conducted a comprehensive search to identify cohorts involving immune checkpoint blockade, publicly accessible, and accompanied by complete clinical details. Ultimately, this study included three immunotherapeutic cohorts: (1) metastatic melanoma treated with pembrolizumab (we downloaded the GSE78220 cohort from the Gene Expression Omnibus database, GEO), (2) advanced urothelial cancer with atezolizumab intervention (we obtained the IMvigor210 cohort from previously published research) [20], and (3) renal cell carcinoma treated with nivolumab (we acquired the GSE67501 cohort from GEO).
Clinical Correlation Between OIP5 Expression and Various Cancers
Differential gene expression analysis was performed to decide whether OIP5 expression varied between normal groups and tumor by using the limma package in R studio software. The correlation between clinical parameter (tumor stage) and OIP5 expression was also investigated. Moreover, to figure out the time-dependent prognostic value of OIP5 in 33 cancers, we performed univariate Cox regression analysis by using the survival package in R. The survival outcomes included disease free survival (DFS; period from the start of treatment to disease recurrence or death from any cause), overall survival (OS; period from the start of treatment to death from any cause), progression free survival (PFS; period from the start of treatment to disease progression or death from any cause), and disease specific survival (DSS; cancer survival in the absence of other causes of death). Significant consideration was given to variations that had a p-value < 0.05, indicating that the exposure factor (OIP5 expression) acted as a promoting factor for positive events (death) when the hazard ratio was greater than 1 (HR > 1)
Generation and Investigation of OIP5 Activity
In order to further illustrate the protein level of OIP5 in pan-cancers, 69 relevant genes that were significantly down-regulated after GNF-351 antagonist treatment and up-regulated after 3-MC agonist treatment in eight cell lines were uncover from a published study [21]. OIP5 activity was elicited by single sample gene set enrichment analysis (GSEA). Subsequently, we elucidated the difference in OIP5 activity between the tumor and normal group. Next, the expression and activity were computed to determine the mean value, which was then organized across 33 cancer types. This analysis aimed to highlight the possible characteristics associated with OIP5 expression and activity.
Analysis of Potential Association Between Immune-Related Factors and OIP5 Expression
ESTIMATE, a useful tool for predicting the presence of infiltrating stromal/immune cells in tumor issues and tumor purity, was applied to calculate the immune score and stromal score of each case [22]. Based on single sample GSEA, the ESTIMATE algorithm can finally generates three scores: the immune score (represents the infiltration of immune cells in tumor tissues), the stromal score (indicates the presence of stromal cells in tumor tissues) and the tumor purity. Next, we evaluated the situation of immune cell infiltration in the high OIP5-expressing and low OIP5-expressing groups by using the CIBERSORT algorithm, which is a outstanding algorithm that assesses the proportions of 22 tumor-infiltrating lymphocyte subsets [23]. In brief, the eligible samples in the cohort underwent further inquiry if their p-value was less than 0.05, while the permutations were fixed at 1000 in number. Previous research has demonstrated a strong connection between immune response and both TMB and MSI. Thus, this study sought to explore the relationship between these indicators and the expression level of OIP5. In this study, TMB was defined as the overall count of errors observed in gene coding, including gene insertions, base substitutions, or deletions, per one million bases. To calculate the TMB for each case, the total number of mutations identified was divided by the exome size (which was taken as 38 Mb). The MSI score for each cancer case from the TCGA dataset was obtained from a previously published investigation [24]. Furthermore, the connection between the expression of OIP5 and immunological regulators (including molecules that inhibit or stimulate the immune system and major histocompatibility complex molecules) was investigated using the TISIDB website (http://cis.hku.hk/TISIDB/index.php). Subsequently, the four most pertinent findings were emphasized and illustrated in graphs. Ultimately, in order to further examine the relevant signaling pathways, GSEA was employed to elucidate the differential pathways between the group with low OIP5 expression and the group with high OIP5 expression, which were obtained from the Kyoto Encyclopedia of Genes and Genomes database. Finally, we presented the relevant signaling pathways in plots if they met criteria (p < 0.05) and the pathways with the top five highest normalized enrichment score.
Analysis of Immunotherapeutic Response
As mentioned above, we analyzed three relevant independent immunotherapeutic cohorts in this study. In general, immunotherapeutic approaches appeared four outcomes: partial response (PR), complete response (CR), stable disease (SD), and progressive disease (PD). In this study, we categorized patients as responders (CR or PR) and non-responders (SD or PD) for immunotherapy. After that, was used the Wilcoxon test to identify differences in OIP5 expression between the responder and non-responder groups.