Identification and Verification of Disulfidptosis Patterns and Characterization of Tumor Microenvironment Infiltration via Multi-Omics Analysis in Lung adenocarcinoma
Authors: Liu Junzhi , Li Huimin , Zhang Nan , Dong Qiuping , Liu Chen , Liang Xinyu , Guo Sitong , liang zheng 
Published: May 2023

Background
Recent research has uncovered a novel contributor to cellular dysfunction called disulfidptosis. However, the regulatory factors of disulfidptosis in lung adenocarcinoma, such as molecular heterogeneity and the tumor microenvironment (TME), remain largely unknown.
Methods
We explored expression profiles and genetic variation features of disulfidptosis regulators and identified their correlation with patient outcomes across cancers. The mRNA expression level of SLC7A11 and SLC3A2 was verified by quantitative reverse transcription polymerase chain reaction. The R package "ConsensusClusterPlus" was utilized to identify two distinct patterns of disulfidptosis, high-disulfidptosis pattern (HDPS) and low-disulfidptosis pattern (LDPS), which we systematically characterized in lung adenocarcinoma using multi-omics data. Single-sample gene set enrichment analysis (ssGSEA) was used to identify enrichment fractions of several signaling pathways. Several immune cell infiltration algorithms, including TIMER, CIBERSORT, QUANTISEQ, MCPCOUNTER, XCELL, EPIC, SVR, and LSEI, were used to compare immune landscapes between subgroups. The R package "oncoPredict" was utilized to identify various subtypes of drug sensitivity. The R package “Maftools” was utilized to compare different mutation patterns between subgroups. An eight disulfidptosis-related gene signature was identified to construct a risk score model using the random survival forest variable hunting (RSFVH) algorithm, stratifying patients into high- and low-risk groups, with TCGA cohort validation.
Results
We investigated the expression profiles and genetic variation characteristics of disulfidptosis genes across different cancer types. Our analysis revealed two distinct patterns of disulfidptosis, high-disulfidptosis pattern (HDPS) and low-disulfidptosis pattern (LDPS), which we systematically characterized in lung adenocarcinoma using multi-omics data. Intriguingly, patients with HDPS had a more favorable prognosis than those with LDPS, indicating that disulfidptosis is a critical factor in shaping the TME and influencing patient outcomes. Furthermore, we found that LDPS was associated with the lowest enrichment of metabolic activities, while HDPS was characterized by immune suppression. To enhance our understanding of the clinical implications of these findings, we developed a novel scoring tool, called DPSig, which predicts the prognosis of lung adenocarcinoma patients based on their disulfidptosis status.
Conclusions
Our study highlights the crucial role of disulfidptosis in shaping the TME in lung adenocarcinoma and emphasizes the need to evaluate the disulfidptosis landscape to guide clinical decision-making. By deepening our understanding of disulfidptosis, we can move closer to fully characterizing the complex landscape of lung adenocarcinoma and developing more effective treatments for this devastating disease.