Multi-Omics Pan-Cancer Bioinformatics Workflow for Evaluating PTDSS1 and PTDSS2 as Prognostic and Immune Biomarkers

Summary

This study conducted a comprehensive bioinformatics analysis using multiple public databases to evaluate the gene expression patterns, clinical correlations, survival prognosis, tumor stemness scores, and immune-related characteristics of PTDSS1 and PTDSS2 across various cancers. The findings suggest these genes may serve as potential biomarkers for cancer diagnosis, prognosis, and immunotherapy.

Abstract

The phosphatidylserine synthesis enzymes PTDSS1 and PTDSS2 play important roles in cellular processes, including membrane composition and signal transduction. Abnormal expressions of these enzymes has been associated with tumor-associated macrophage infiltration and poor survival outcomes in breast cancer. However, their comprehensive roles across different cancer types remain unclear. In this study, we performed a pan-cancer analysis to evaluate the diagnostic, prognostic, immune infiltration, and immunotherapeutic relevance of PTDSS1 and PTDSS2. Multiple public databases were used to analyze gene expression patterns, clinical correlations, survival outcomes, tumor stemness scores, and immune-related characteristics across various cancers. The results showed that PTDSS1 and PTDSS2 are widely expressed in human tissues and significantly upregulated in most tumor tissues compared with normal tissues. High expression of PTDSS1 or PTDSS2 was associated with poorer overall survival (OS) in several cancer types and showed significant correlations with clinical stage and tumor stemness scores. In addition, functional analyses suggested that these genes may contribute to tumorigenesis, immune regulation, and chemoresistance. Overall, the findings highlight the prognostic significance of PTDSS1 and PTDSS2 in multiple cancers and suggest that they may serve as potential biomarkers for cancer diagnosis and prognosis, as well as promising targets for immunotherapy.

Introduction

Although substantial advances have been achieved in oncology over the past several centuries, cancer remains one of the primary causes of death and disease burden worldwide^1,2. Current clinical management increasingly relies on multimodal treatment strategies, combining different therapeutic approaches to maximize efficacy while improving patient survival and overall well-being². In this context, cancer immunotherapy has rapidly evolved into a central pillar of modern oncology. Its advantages—including target selectivity, sustained therapeutic effects, applicability across multipl....

Protocol

This study was based exclusively on publicly available datasets and did not involve direct human or animal participation; therefore, institutional ethical approval and informed consent were not required

1. PTDSS1 or PTDSS2 expression analysis

1. Assess PTDSS1 and PTDSS2 mRNA expression in normal human tissues using the Human Protein Atlas (HPA) database (https://www.proteinatlas.org).
2. Retrieve PTDSS1 and PTDSS2 gene expression profiles across multiple cancer types from the “Gene DE” module of the TIMER2 platform (http://timer.cistrome.org/).
3. Download RNA-seq expression data for norma....

Results

PTDSS1 or PTDSS2 mRNA expression in various human normal tissues

Using data from the HPA database, the mRNA expression profiles of PTDSS1 and PTDSS2 across a range of human tissues were examined. Analysis revealed notable tissue-specific expression patterns. PTDSS1 was highly expressed in the parathyroid gland, heart muscle, lymph nodes, tonsils, and bone marrow (Figure 1A), whereas PTDSS2 showed elevated expression in the parathyroid gland, testi.......

Discussion

Cancer is a systemic disease characterized by the dysregulation of multiple systems and typically progresses through three stages: elimination, equilibrium, and escape²⁵. In the elimination stage, the immune system effectively eliminates newly formed cancer cells; during the equilibrium stage, cancer cells and immune cells are in a relative balance, preventing tumor expansion or metastasis; ultimately, in the escape stage, cancer cells evade immune surveillance and rapidly proliferate and metastas.......

Materials

List of materials used in this article

Name	Company	Catalog Number	Comments
cBioPortal platform	cBioPortal	http://www.cbioportal.org/	Platform for visualization and analysis of multidimensional cancer genomics data
GDC portal	Genomic Data Commons (GDC)	https://portal.gdc.cancer.gov/	Portal for downloading TCGA genomic and mutation datasets
GEPIA platform	GEPIA	http://gepia.cancer-pku.cn/	Web server for gene expression profiling and survival analysis based on TCGA and GTEx datasets
GEPIA2	GEPIA2	http://gepia2.cancer-pku.cn/#index	Updated web server for gene expression correlation and interactive analysis
GTEx Databases	Genotype-Tissue Expression (GTEx) Project	http://commonfund.nih.gov/GTEx/	Database containing gene expression profiles from normal human tissues
Human Protein Atlas (HPA) database	Human Protein Atlas	https://www.proteinatlas.org	Database for analyzing protein and RNA expression profiles in normal tissues and cancers
KEGG REST API	Kyoto Encyclopedia of Genes and Genomes (KEGG)	https://www.kegg.jp/kegg/rest/keggapi.html	Application programming interface for retrieving KEGG pathway annotations
KM Plotter tool	KMplot	https://kmplot.com/analysis/	Online tool for evaluating the prognostic significance of genes in multiple cancers
maftools R package	Bioconductor package maftools	version 2.8.05	R package used for tumor mutation burden and mutation analysis
psych R package	CRAN R package psych	version 2.1.6	R package used for correlation and psychological statistical analyses
R package	R Foundation for Statistical Computing	version 4.1.3	Statistical computing software used for drug sensitivity analysis
R package clusterProfiler	Bioconductor package clusterProfiler	version 3.14.3	R package used for KEGG and functional enrichment analyses
R package survival	CRAN R package survival	version 3.2-7	R package used for Cox regression and Kaplan–Meier survival analysis
R software	R Foundation for Statistical Computing	version 3.6.4	Statistical computing software used for bioinformatics and survival analyses
STRING database	STRING	https://cn.string-db.org/	Database for protein–protein interaction network analysis
TCGA Databases	The Cancer Genome Atlas (TCGA)	http://cancergenome.nih.gov	Large-scale cancer genomics database containing genomic, transcriptomic, and clinical cancer data
TIMER2.0	TIMER2.0	http://timer.cistrome.org/	Platform for immune cell infiltration estimation and correlation analysis in tumors
Tumor Immune Estimation Resource 2.0 (TIMER2) platform	TIMER2.0	http://timer.cistrome.org/	Web platform for systematic analysis of immune infiltration across diverse cancer types
UCSC Xena platform	UCSC Xena	https://xenabrowser.net/	Platform for visualization and analysis of TCGA and other public multi-omics datasets