Lung squamous cell carcinoma (LUSC) is a histological subtype of non-small cell lung cancer (NSCLC) with poor prognosis, high treatment difficulty, and high mortality rate mainly due to the lack of effective treatment (22). Despite the available treatment methods for lung squamous cell carcinoma, including chemotherapy, radiotherapy, and targeted therapy, the prognosis for patients with lung squamous cell carcinoma remains unsatisfactory (23). Therefore, it is urgently needed to study new molecular therapeutic targets and prognostic models for the diagnosis and treatment of LUSC. Previous studies have shown that apoptosis plays a crucial role in maintaining the balance between cell death and division, and evading apoptosis can lead to uncontrolled cell proliferation, resulting in various diseases such as cancer (24). The mechanism of apoptosis is complex and involves many pathways. Any defect in any of these pathways can lead to malignant transformation of affected cells, tumor metastasis, and resistance to anticancer drugs. Currently, many new therapeutic strategies targeting apoptosis are feasible (25, 26). Although there is a connection between apoptosis and LUSC, there has not been a systematic study that uses apoptosis-related features as prognostic indicators to predict the prognosis of LUSC patients.
Our study conducted a systematic analysis based on the TCGA-LUSC dataset and apoptosis-related genes to identify differentially expressed ARGs in LUSC and non-tumor tissues. Through multifactor and LASSO Cox regression analysis, we screened for prognostic-related genes and established a risk model to predict the prognosis of LUSC. The genes in the model, including BMP2, GPX3, and JUN, were validated to be associated with the prognosis of LUSC patients through survival analysis and the HPA database.
Bone morphogenetic proteins (BMPs) are multifunctional cytokines, belonging to members of the transforming growth factor-βsuper-family(27). MP can trigger the occurrence and progression of tumors through the signaling mediators of ligands and receptors. At the same time, BMP can promote cell differentiation, including inhibiting the process of epithelial-mesenchymal transition, which can prevent the malignant progression of cancer in the later stages (28). BMP can activate ERK, phosphoinositide 3-kinase (PI3K), protein kinase A (PKA), PKC, and PKD. Through the initiation of these pathways, BMP can induce its effects on cell survival, apoptosis, migration, and differentiation (29). SMAD is the classical pathway mediated by BMP2. Studies have shown that BMP2 is highly overexpressed in human non-small cell lung cancer and is associated with tumor grading and metastasis. Through mouse models, BMP2 has been shown to promote lung cancer metastasis. Depletion of BMP2 or its receptor BMPR2 greatly reduces cell migration and invasiveness, and BMP2/BMPR2-mediated cell migration involves the activation of the SMAD1/5/8 signaling pathway. Depletion of SMAD1/5/8 significantly reduces cell migration by inhibiting SMAD1/5/8 (30). Epigenetic silencing of glutathione peroxidase 3 (GPX3), a member of the important antioxidant selenoprotein family (31), maintains genome integrity by inactivating Reactive oxygen species (ROS) (32). Reactive oxygen species (ROS) are believed to play various roles in cancer development. For example, when the gene expression of important molecules that control cell proliferation, apoptosis, or the cell cycle is abnormal, oxidative stress can lead to persistent DNA damage and may induce cancer (33). Research has shown that GPX3 is associated with ovarian cancer metastasis and cancer progression. In this study, a stable OVCAR3 GPx3 knockdown cell line was constructed using a lentiviral shRNA, and it was found that reducing GPx3 expression inhibited colony formation and anchorage-independent cell survival (34). In a study on thyroid cancer, methylation-specific PCR (MSP), immunohistochemistry staining, Transwell experiments, and siRNA knockdown were used to investigate the role of GPX3. It was found that GPX3 inhibits thyroid cancer metastasis by suppressing the Wnt/β-catenin signaling pathway. Silencing GPX3 expression promotes human thyroid cancer metastasis. (35). c-Jun has been found to be an oncogenic transcription factor in most cancers, and its overexpression plays an important role in various biological functions such as cell apoptosis, proliferation, invasion, and migration (36, 37). A study showed that enforced expression of c-Jun increased anchorage-independent growth of human bronchial epithelial cell lines, and constitutive expression of a significant c-Jun-negative mutant suppressed anchorage-independent but not anchorage-dependent growth of lung cancer cell lines (38). The activity of c-Jun is regulated by post-translational modifications, which are mainly controlled by components of the mitogen-activated protein kinase (MAPK) family of kinases, including c-Jun N-terminal kinase (JNK), extracellular signal-regulated kinase (ERK), and p38 kinase (39). The significant and unique function of JNK is as an activator of c-Jun. Overexpression or activation of c-Jun has been shown to have anti-apoptotic effects in various cancer cell lines, and its targeting may sensitize drug-resistant cancer cells to DNA-damaging agents (40). Therefore, these key apoptotic prognostic-related genes are closely associated with LUSC and its prognosis, demonstrating the validity of establishing a prognostic model based on apoptotic prognostic-related genes in this study.
After modeling, we conducted validation by dividing LUSC patients into different risk groups based on the median risk score. Patients in the high-risk group had significantly worse prognosis. The results of ROC curve analysis showed that the prognostic model had good predictive performance. Furthermore, through analysis of the risk score of the model and other clinical features, the risk score of the model was found to be an independent prognostic indicator, and C-index analysis showed that the risk score had better predictive value than other traditional clinical parameters. Therefore, the good predictive value of the model was confirmed once again.
In addition, we conducted functional enrichment analysis on the apoptotic differentially expressed genes that we screened, exploring the key pathways through which they play their functions. According to GO and KEGG pathway analysis, we found that in cancer, cell apoptosis is mediated by changes in the mitochondrial membrane, which is a multifactorial process involving BCL-2 family proteins, cysteine proteases, and large molecular complexes. Studies have shown that in the pre-initiation stage of mitochondria, different pro-apoptotic signal transduction or damage pathways are activated. When these signals or pathways converge on the mitochondria, the permeability of the inner and outer membranes increases, leading to the execution stage of the apoptosis process (41). The regulation of apoptotic mitochondrial events is achieved through the Bcl-2 family of proteins. The Bcl-2 family consists of more than 30 proteins and belongs to the Bcl-2 superfamily, which includes anti-apoptotic proteins, pro-apoptotic proteins, and BH3-only proteins (42). In the presence of apoptotic stimuli, the expression of BH3 proteins increases, competitively binding to the anti-apoptotic protein Bcl-2 to release Bax/Bak from inhibition. Free Bax and Bak form oligomers, causing cytochrome C to form a channel through the outer membrane of the mitochondria, releasing it from the intermembrane space of the mitochondria into the cytoplasm. The released cytochrome C activates the caspase cascade reaction to induce cell apoptosis (43, 44). Therefore, mitochondria can be considered as the main integrator of the death pathway. Apoptosis depends on the activation of the above different signaling pathways, and these pathways are often dysregulated in cancer, providing a direction for further research on the treatment of apoptosis in lung squamous cell carcinoma.
Furthermore, in our multifaceted study of the immune microenvironment, we found significant differences in TMB and TP5 between high- and low-risk groups. Studies have shown that TMB in blood can be used to evaluate the efficacy of using camrelizumab in combination with chemotherapy in advanced lung squamous cell carcinoma patients. During treatment, blood TMB levels are positively correlated with patient efficacy, indicating that higher TMB leads to better treatment efficacy and longer overall survival (OS) and progression-free survival (PFS) for patients. These findings are consistent with the results of our study (45). In this study, we can see the differences in gene mutations between high and low-risk groups, and the most common type of gene mutation, TP53, has significant differences in mutation types between high and low-risk groups. Previous studies have shown that TP53 is one of the most common mutated genes in lung cancer, and its mutation is closely related to the occurrence and development of lung cancer. The type of TP53 mutation is related to prognosis, and patients with nonsense mutations have a poorer prognosis (46). This is consistent with our research results, where patients in the high-risk group had a poorer prognosis.
Although previous studies have investigated the relationship between apoptosis and cancer, there have been few studies on its relationship with tumor immunity. Stromal cells and immune cells are the main elements of the TME, and an important aspect of our research is to explore the correlation between the risk model of LUSC patients and tumor immunity. We used the ESTIMATE algorithm to calculate these scores and found that the high-risk group had higher immune and stromal scores. This indicates that changes in immune status can also affect the process of cell apoptosis. We found that NK cells and macrophages were highly expressed in the high-risk group, and they have an inherent connection with cell apoptosis. Studies have shown that the mitochondrial apoptosis (mtApoptosis) pathway is crucial for efficient NK killing, and NK cells can pre-activate cancer cells for mtApoptosis. Pre-activated NK cells bind BH3 mimetics to NK cells, synergistically killing cancer cells in vitro and inhibiting tumor growth in vivo (47). This study suggests that mtApoptosis can enhance NK-based immunotherapy. An interesting study found that macrophages are a heterogeneous group of cells in the innate immune system that are crucial for the initiation, progression, and resolution of inflammation. They have significant functional plasticity and can respond to abnormalities and initiate programs to overcome them and restore normalcy (48, 49). The cytokines and intracellular components released by apoptotic cells can activate macrophages, causing them to shift from the M1 to M2 phenotype, thereby promoting tumor growth and metastasis. In addition, apoptotic cells can also induce macrophages to produce growth factors and extracellular matrix components, promoting tumor cell proliferation and invasion. If the interaction between apoptotic cells and macrophages is not disrupted, surviving tumor cells may receive excessive support from the reaction induced by local macrophages in apoptotic tumor cells, thereby enhancing tumor recurrence (50). Indeed, this provides valuable ideas and strategies for researchers to further explore the anti-tumor potential of cell apoptosis and tumor immunity.
Tumor heterogeneity is a significant challenge in cancer treatment, as different subpopulations of cells may exhibit varying sensitivity and resistance to therapeutic drugs (51). Therefore, understanding tumor heterogeneity is of great significance for developing personalized treatment plans and predicting treatment outcomes. Compared to traditional ‘bulk’ RNA-sequencing methods, which average potential differences in cell-specific transcriptomes, single-cell RNA sequencing (scRNA-seq) analyzes the gene expression patterns of each individual cell, providing a clear insight into the entire tumor ecosystem, such as the mechanisms of intra- and inter-tumor heterogeneity (52). Through single-cell sequencing analysis, this study ultimately divided cells into eight subgroups. To further understand the expression patterns and functions of apoptosis-related prognostic genes in different cell subgroups, we compared the expression of prognostic genes in different cell subgroups to determine their functions in different cell subgroups. We found that BMP2 was highly expressed in epithelial cells, which may indicate that BMP2 has important biological functions in epithelial cells. In a study on breast cancer, elevated levels of BMP2 led to excessive activation of the BMPR1B signaling pathway, which is the receptor for BMP2. When BMP2 binds to BMPR1B, it activates the BMPR1B signaling pathway, promoting the transformation of epithelial cells. These transformed epithelial cells have increased proliferation and invasiveness, thereby promoting tumor development (53). Similarly, in a study on lung injury, BMP2 was found to activate the BMP signaling pathway, leading to increased BMP activity and ultimately resulting in the transformation of epithelial cells and the disruption of epithelial barrier function. This study successfully inhibited the increase in BMP activity and the disruption of epithelial barrier function by suppressing the expression of BMP2, thereby avoiding lung injury (54). This provides a good explanation of the role of prognostic genes in tumor development at the cellular level.
There are different subpopulations of cells within tumors, which have different gene expression profiles, leading to heterogeneity within the tumor. Sequencing multiple tumor regions can reveal the evolutionary pattern of the tumor, where tumor cells have different gene mutations and expression profiles at different time and space points, forming a branched evolutionary tree structure (55). Pseudotime analysis is a method for inferring trajectories from scRNA-seq data. It sorts cells along a trajectory based on the similarity of their expression patterns and determines the lineage structure by identifying branching events (56). In this study, we determined trajectories with different differentiation states based on scRNA-seq data of non-small cell lung cancer, and located the selected prognostic genes on the cell differentiation trajectories. We observed the changes of prognostic genes in different clusters and the same cell types. Combining pseudotime analysis and the localization of cell types in different clusters over time, we were able to intuitively witness the evolution of cells in non-small cell lung cancer. This provides new insights into the mechanisms and driving factors of tumor evolution.
Although the prognostic model established in this study has good predictive performance and is a positive prognostic indicator for LUSC patients, and provides more accurate information for the treatment and prognosis evaluation of LUSC patients by combining single-cell sequencing analysis, there are still some limitations that need to be considered. The analysis conducted in this study was based on retrospective data from the TCGA and GEO databases, and due to the scarcity of scRNA-seq data in the GEO database, we could not obtain complete single-cell sequencing results for LUSC samples. Our results need to be further functionally validated. Despite these limitations, the research in this paper can serve as a valuable concept validation study, identifying biomarkers and targets for future research and providing meaningful references for personalized treatment of LUSC patients.