To the best of our knowledge, this study provides a comprehensive analysis of disulfidptosis patterns in THCA, and genotypes thyroid carcinoma based on disulfidptosis-related genes. Additionally, the study constructs a disulfidptosis signature using multiple machine learning algorithms. Furthermore, the immune landscape of different patient groups is explored based on both bulk and single-cell transcriptome data. This is the first study to investigate the function of disulfidptosis in predicting prognosis and immune landscapes in thyroid carcinoma and providing therapeutic strategies.
Recent studies have found a link between disulfidptosis and disulfide bond reactions occurring between intracellular and extracellular protein molecules. This leads to conformational changes and altered protein function, ultimately resulting in cell death[4]. Metabolic therapy involving glucose transporter (GLUT) inhibitors has been shown to trigger disulfidptosis and inhibit cancer growth, as supported by increasing evidence[5]. In order to investigate THCA in the TCGA database, we classified it into two clusters based on disulfidptosis-related genes using NMF. Our analysis revealed significant differences in survival between the two clusters. Specifically, C1 had the best survival, while C2 had the worst survival. By combining previous subtypes, we found that the proportion of IE subtype and IE/F subtype were the higher in C1, while the proportion of D subtype patients in C2 is significant more than that in C1. IE and IE/F subtype patients were characterized by high infiltration of immune cells and better survival rate. Moreover, it has been discovered that IE and IE/F subtype patients were more sensitive to immunotherapy[15]. Additionally, C1 patients also enriched immune associated pathways (inflammatory response and interferon gamma response pathways), while patients in C2 enriched in cell cycle associated pathways (MTORC1 signaling pathways, Myc targets). All these different pathways interacted with cell death in THCA, thus regulating cancer development[18–20]. Cytokines and their receptors are crucial elements of the tumor microenvironment (TME). Chemokines play a significant role in the migration of immune cells into the TME, while interleukins (ILs) are enriched in the TME as immunomodulatory cytokines. IFNs, on the other hand, are responsible for mediating the antitumor activity of effector T cells[21, 22]. We found that the expression of chemokines, interferons, interleukins, and their receptors were higher in C1 than that in C2. The results suggest that C1 patients have a microenvironment with infiltrated immune cells, which leads to a higher survival rate. Previous studies have shown that immune cells in the TME play a crucial role in the development of various tumors. Various clusters of tumors exhibit distinct immune cell subpopulations, which can vary even within patients of the same pathological type[22, 23]. Using ESTIMATE algorithm, we found that the immune cells infiltrated fraction was higher in C1, while the tumor purity was lower, supported by the higher score of immune score and lower score of tumor purity in C1. We also found C1 showed higher infiltration of immune cells, such as CD8 + T cells, CD4 + T cells, NK cells, B cells, and antigen-presenting cells, but lower M2 macrophages and Endothelial cells. Oppositely, C2 showed lower immune and antigen-presenting cells, such as M1 Macrophage and Dendritic cells, but higher M2 macrophages. According to research[24], M1 Macrophage cells aid in the maturation and activation of dendritic cells, which in turn promotes anti-tumor immunity. On the other hand, M2 macrophages[25] contribute to gene instability, angiogenesis, fibrosis, immunosuppression, invasion, and metastasis, which ultimately enhance tumor progression. Furthermore, tumor endothelial cells are known to provide ample nutrients and energy for the rapid proliferation of cancers[26]. The differences in immune infiltration correspond with the variation in survival rates. Immune checkpoint inhibitors have emerged as a promising therapeutic approach for cancer treatment, particularly for patients who are resistant to chemotherapy[27]. The overexpression of immune checkpoints is known to promote immune evasion by cancer cells, and is also indicative of a better response to immune checkpoint inhibitors. We found that the expression of immune checkpoints (e.g., CD274/PD-L1, HAVCR2/TIM-3, CTLA4, PDCD1/PD1, PDCD1LG2/PD-L2, IDO1/2, LAG3) were higher in C1 than in C2, suggesting that THCA patients in C1 may benefit from immunotherapy. Tumor Immunophenotyping-Derived Signature (IPS) has been shown to have the ability to predict Neoadjuvant Immunotherapeutic with high credibility. Patients with low IPS scores were found to be immune-activated, while patients with high IPS scores were immune-silenced[28]. Furthermore, the IPS score of C1 patients was higher than that of C2 patients. These results indicate that THCA patients in C1 were characterized by a high infiltration of immune cells and were more sensitive to immunotherapy.
In light of the crucial role that chemotherapy combined with immunotherapy plays in cancer treatment[29], we examined the disparity in response to chemotherapy between the two clusters. Our findings showed that THCA patients in C1 demonstrated higher sensitivity to cell cycle related drugs, DNA damage repair inhibitors, signal pathway inhibitors, and histone deacetylase inhibitor. Conversely, THCA patients in C2 exhibited greater sensitivity to tyrosine kinase inhibitors and protein kinase B (Akt) inhibitors. Different drugs have varying functions in treating cancer. For instance, cell cycle related drugs target the cell cycle, hindering the proliferation of cancer cells. DNA damage repair inhibitors, on the other hand, prevent DNA damage repair pathways, leading to cancer cell death. Signal pathway inhibitors work by inhibiting internal signaling pathways, thus impeding the growth and spread of cancer cells. Histone deacetylase inhibitors affect chromatin structure.
Furthermore, we developed a machine learning model to predict the prognosis of THCA. Our model demonstrated satisfactory AUC and C-index values in both the training and test groups. In addition, Kaplan-Meier analysis revealed that patients with high-risk scores had significantly poorer survival rates than those with low-risk scores. Furthermore, our risk score was found to be an independent predictor of prognosis in both univariate and multivariate Cox regression analyses. Our constructed risk model also showed advantages in predicting survival compared with traditional clinical and pathological features. Using GSEA analysis, we found that high-risk score patients were associated with IL6_JAK_STAT3_SIGNALING, HEDGEHOG_SIGNALING, IL2_STAT5_SIGNALING, G2M_CHECKPOINT, MITOTIC_SPINDLE, and TNFA_SIGNALING_VIA_NFKB pathway. All these different pathways were functional in regulating cancer development[18].
The role of immune cells in tumor development has been extensively studied. Different subpopulations of immune cells have been found in various types of tumors, and even among patients with the same type of cancer[30, 31]. To gain further insight, we conducted single cell analysis to determine the proportion of different cell types in patients with low and high risk of developing cancer. Additionally, we investigated the mechanisms of intercellular communication within these cell types. Our study, which utilized cellchat, revealed an increase in intercellular interactions in the high risk group compared to the low-risk group, especially in the interaction between fibroblasts and other cells. Additionally, we found that the communication between HLA and CD8 signaling was significantly activated in low-risk groups. Previous research has shown that HLA can bind to the CD8 receptors on the surface of CD8 + T cells, which helps T cells receive signals and then enter deep tissues to perform cytotoxic functions[32].
In summary, our research investigated the molecular and immune infiltration landscape using disulfidptosis genes and proposed therapeutic strategies for different THCA patients. We also developed a stable and potent disulfidptosis-associated model to assess prognosis, which can be a valuable tool for optimizing decision-making and surveillance protocols for individual THCA patients. However, a major limitation of our study is the lack of data from multicenter trials. Therefore, further validation of the results obtained requires more multi-center randomized controlled trials with high quality, large sample size, and adequate follow-up.