Golgi scaffold protein PAQR11 in pan-cancer landscape: A comprehensive bioinformatics exploration of expression patterns, prognostic significance, and potential immunological function

Abstract

Background: The progestin and adipoQ receptor family member, PAQR11, is recognized for its roles in vesicle trafficking, mitogenic signaling, and metastatic spread, positioning it as a crucial regulator in cancer biology. PAQR11 influences lipid metabolism and susceptibility to ferroptosis in cancer cells. This study aims to investigate the prognostic significance of PAQR11, its relevance to immune responses, and its association with drug sensitivity across various cancer types. By elucidating these aspects, the research seeks to assess PAQR11's potential as a biomarker and therapeutic target in oncology.

Methods: We conducted a comprehensive bioinformatics analysis using publicly available pan-cancer datasets from TCGA, GEO, UALCAN, TIMER, GEPIA2, KM plotter, and TISIDB. This analysis encompassed gene expression profiles across 33 cancer types, with a focus on PAQR11's expression patterns, prognostic significance, and immunological relevance. In addition, the study explored the correlation between PAQR11 expression and drug sensitivity, alongside its molecular and pathological characteristics in various tumors.

Results: Our findings demonstrate elevated PAQR11 expression levels across multiple cancer types, which significantly correlate with patient prognostic outcomes. The analysis further revealed PAQR11's involvement in immunological and epigenetic processes, underscoring its critical role in cancer progression and treatment response. Notably, a strong correlation between PAQR11 expression and drug sensitivity was identified, suggesting its potential influence on the initiation and progression of various cancers and highlighting its promise as a therapeutic target.

Conclusions: The comprehensive analysis of PAQR11 underscores its significance as a biomarker for cancer prognosis and its role in regulating immunological and epigenetic processes. These findings offer valuable insights that could inform early detection strategies and the development of novel therapeutic approaches. Further exploration and validation of PAQR11 are essential, highlighting the need for its integration into future oncological research and treatment strategy development.

Trial registration: Not applicable.

Keywords: Monocyte to macrophage differentiation-associated (MMD); Pan-cancer analysis; Potential immunological function; Progestin and adipoQ receptor family member 11 (PAQR11); TCGA.

Fig. 1

Pa-cancer analysis of PAQR11 expression and prognosis. A: exhibits the expression levels of PAQR11 in various cancer cell lines, utilizing data extracted from the CCLE database. B: The differences in PAQR11 expression between tumor and normal tissues were analyzed using the GEPIA2 database. An unpaired two-sample t-test was employed to assess statistical significance, with a p-value threshold set at 0.01. In the visualization, red represents tumor tissues, while blue indicates normal tissues. Statistically significant differences are marked with an asterisk (∗P < 0.01). C: The Kaplan-Meier survival curve was used to analyze human cancers, with groups segregated into high and low PAQR11 expression based on the median cutoff value, as assessed by the GEPIA2 database. Statistical significance was determined using the logrank test to calculate the P-value.

Fig. 2

Analysis of the correlation between PAQR11 expression and A (Overall Survival, OS), B (Disease-Specific Survival, DSS), C (Disease-Free Interval, DFI), and D (Progression-Free Interval, PFI) was conducted using Cox proportional hazard regression models. Hazard ratios (HR) with 95 % confidence intervals were calculated, and statistical significance was assessed using the Log-rank test. The results are presented as forest plots.

Fig. 3

Pan-cancer analysis focusing on the genomic alterations associated with PAQR11. A: The landscape of PAQR11 genetic alterations was generated utilizing the cBioPortal database. B: An analysis was conducted to examine the correlation between PAQR11 expression and CNV across different tumors, utilizing the GSCA website. C: Association of PAQR11 Copy Number Alterations (CNA) with molecular subtypes of breast cancer. Pairwise comparisons between subtypes were assessed using a t-test, with statistical significance indicated as ∗P < 0.05, ∗∗P < 0.01, and ∗∗∗P < 0.001. D: The GSCA website was employed to investigate the prevalence of SNVs within the PAQR11 gene across various tumor samples. E: Comparison of single nucleotide variants (SNVs) in TCGA UCEC patients stratified by MSI NABTIS score into microsatellite stable (MSS, n = 358) and microsatellite instability (MSI, n = 117) groups. The frequency of SNVs between the two groups was analyzed using a chi-squared test to assess statistical significance, with p-values reported.

Fig. 4

DNA promoter methylation levels between normal and cancerous tissues were analyzed and compared using the UALCAN database. Statistical significance was determined via Student's t-test, with ∗ indicating P < 0.05, ∗∗ indicating P < 0.01, and ∗∗∗ indicating P < 0.001. A: The methylation levels of the PAQR11 promoter were observed to be significantly elevated in cancerous tissues in comparison to normal tissues. B: The methylation levels of the PAQR11 promoter were significantly higher in normal tissues compared to cancerous tissues.

Fig. 5

Pan-cancer analysis of PAQR11 effects on the tumor immune microenvironment. A: KEGG Pathway Enrichment Analysis of 87 Statistically Significant Differentially Expressed Genes (DEGs) in TCGA-Pancancer Dataset, Highlighting Three Immune-Related Pathways Potentially Regulated by PAQR11. B: The association between PAQR11 expression and six immune subtypes across pan-cancer was investigated, encompassing subtypes C1 through C6. C: Analysis of TCGA-STAD data examining the correlation between PAQR11 and TGFB1 expression. Pearson's correlation coefficient was used to evaluate the relationship, and statistical significance was assessed using the corresponding p-value.

Fig. 6

Correlation analysis of PAQR11 expression with tumor mutational burden, microsatellite instability, and stromal score. A: The relationship between PAQR11 expression and Tumor Mutational Burden (TMB) was analyzed across multiple cancer types. B: The correlation between PAQR11 expression and Microsatellite Instability (MSI) was investigated. C: The association between PAQR11 expression and stromal score across various cancers was assessed using Pearson's correlation analysis.

Fig. 7

Correlation analysis to investigate the association between the expression of PAQR11 and drug sensitivity. The correlation between drug sensitivity and PAQR11 (MMD) mRNA expression was assessed utilizing A (GDSC database) and B (CTRP database). C: A correlation analysis was conducted to investigate the relationship between PAQR11 expression and drug IC50 values. Pearson's correlation coefficient was used to assess the strength and direction of these associations, with statistical significance determined by the corresponding p-values.

Survival analyses of PAQR11 in human cancers were performed using the Kaplan-Meier plotter database. The tool’s default setting, which computes all possible cutoff values between the lower and upper quartiles and selects the best-performing threshold, was applied. Hazard Ratio (HR) values and logrank P values are displayed on the corresponding plots.

The landscape of PAQR11 genetic alterations was generated utilizing the cBioPortal database.

Figure S3 The landscape of PAQR11 genetic alterations was generated utilizing the cBioPortal database.

Figure S4 UALCAN Database Analysis Indicating Higher PAQR11 Expression in HNSC, LIHC, LUAD, and LUSC Tumors with Reduced PAQR11 Promoter Methylation.

Figure S5 Analysis of MMD Promoter Methylation and Expression Levels in Tumor Samples.

Figure S6 Using the Sangerbox webtool, the correlation analysis of PAQR11 with three RNA modification markers (m1A, m5C, and m6A), including writers, readers, and erasers, was investigated.

Figure S7 Correlation Analysis of PAQR11 Expression and Tumor Purity Across 23 Tumor Types.

Figure S8 Analysis of correlations between PAQR11 and the previously reported genes associated with immune checkpoints.

Figure S9 correlation analysis was conducted to examine the relationship between PAQR11 expression and immune scores across 26 types of cancer.

Figure S10 correlation analysis was conducted to examine the relationship between PAQR11 expression and ESTIMATE scores across 29 types of cancer.

Figure S11The correlation between the expression of PAQR11 and the degree of infiltration of six immune cell types—B cells, CD8⁺ T cells, CD4⁺ T cells, neutrophils, dendritic cells, and macrophages.