Lung cancer has become the leading malignancy threatening human health. Among its primary pathological subtypes is lung adenocarcinoma. Despite advances in modern medicine, early detection, diagnosis, and treatment of malignant tumors remain a challenge. As a result, more than half of individuals are often diagnosed with lung cancer at an advanced stage due to the insidious symptoms of the disease. Millions of people still die from lung cancer every year. With the development of precision medicine and the deepening of research on the development process of cancer, the focus of treatment has shifted towards personalized treatment, with a greater emphasis on driving gene research. Consequently, searching for effective tumor therapeutic targets is an essential part of the current task.With the deepening of research on programmed cell death, more and more studies have confirmed that the three apoptosis modes of cell pyroptosis, necroptosis, and apoptosis generally exhibit a dynamic balance. Therefore, based on this phenomenon, the concept of pan apoptosis was established5. Rajendra et al. found that Interferon regulatory factor 1 (IRF1) could inhibit colon tumor development by activating pan-apoptosis12. TNF-α and IFN-γ could inhibit the growth of various tumors by inducing pan-apoptosis13. Therefore, more and more experts and scholars believe that pan-cell apoptosis has excellent potential in preventing and treating tumours.At present, there is relatively little research on lung adenocarcinoma. Immunotherapy is an emerging method for treating various types of tumors. However, its benefits vary from person to person. In addition, the relationship between pan-apoptosis and immunotherapy, clinical features, and prognosis has yet to be reported.This document compiles the PANoptosis gene collection from the literature that has been published before. We are the first to use TCGA data to study patient prognostic expression data. We identified a total of 50 genes exhibiting different expression levels. The proteins CASP1, CASP3, CASP8, CYCS, TNF, MLKL, FADD, and IL1B could be vital in the progression of tumors, as depicted in the PPI network diagram. Further investigation was conducted on the correlation between and patient survival rates. The study’s findings unequivocally showed that BAK1, CASP10, CYCS, FADD, HMGB2, IL1A, MLKL, TGDP1, TICAM1, TRAF2, UACA, YWHAG, and YWHAE significantly influenced the prognosis of patients.These genes have been found to be indispensable in tumor development in previous literature. The miRNA-125b affects the outcome of breast cancer treatment by targeting and regulating BAK114. Zihang et al. found that Gelsolin increase CASP10 expression to inhibit colon cancer cell proliferation15. In glioblastoma, upregulation of CYCS can be used to inhibit tumor cell proliferation16. FADD is the most interesting of these genes. It mainly participates in post-translation modifications and has different meanings in different organizations. In lung cancer and cervical cancer, this usually indicates poor prognosis, but in thyroid cancer, the opposite is true17. Variations in HMGB2 expression are observed across multiple cancer types, such as skin cancer and hepatocellular carcinoma, and hold significance for predicting patient outcomes. Therefore, some have proposed that targeting this gene may be a feasible treatment method. Research has shown that HMGB2 plays a role as an oncogene through pp38MAPK in renal cell carcinoma and is associated with poor prognosis18. Tianke et al. used comprehensive bioinformatics analysis to determine that IL1A may be a potential target for oral cancer and is associated with poor overall prognosis in patients19. Sofie et al. described that the oncogenic or pro-oncogenic effects of MLKL are dependent on the tumor type and the tumor microenvironment, for example, with completely opposite effects between melanoma and pancreatic cancer20. Zhiyi et al. compared the survival rates of renal blastoma patients with high and low expression of TICAM1 and found that the overall survival rate of patients in the high expression group was significantly higher than that in the low expression group, which is considered a potential prognostic marker21. Yawei et al. discovered a high expression of TRAF2 in individuals with clear cell nephroblastoma, potentially linked to unfavourable outcomes22. The underlying process could be attributed to the role of TRAF2 in advancing tumors through the control of regulating macrophage polarization, migration and angiogenesis. It was proposed that UACA could be a possible focus of miR-30a-3p, serving as an independent prognostic factor for pancreatic cancer23. Jian et al. revealed that LncRNACERS6-AS1 promotes pancreatic cancer through YWHAG proliferation and metastasis24. Yi-Fang et al. deduced that the heightened expression of YWJAE enhances the growth and movement capabilities of breast cancer cells, whereas knockdown the YWHAE levels improves the effectiveness of chemotherapy for breast cancer25.Next, we used cluster analysis on these genes and divided them into two groups based on the results, with k set to 2. There was a significant difference in the prognosis of the patients between the two groups. By comparison, the prognosis of cluster B was found to be much worse than that of cluster A. For cluster B, activated B cells and eosinophils are more abundant in cluster A, while other immune cells are more abundant or undifferentiated in cluster B. This indicates that the infiltration of tumor immune cells (such as CD56 natural killer cells and regular T cells) is more remarkable, leading to a poorer prognosis in cluster B. Daniela et al. described the function of neutrophils in detail and mentioned that there were five neutrophil states in the tumor cells and that infiltration of neutrophils predicted a poorer prognosis26. RegTcells were found to be associated with poorer prognosis of patients, advanced staging and metastasis of lung cancer. Regulary T cells may inhibit anti-tumor function through a variety of mechanisms26. For example, they exhibit suppression of anti-tumor function by expressing soluble immunosuppressive cytokines27,28. In mouse models, it has been found that tumor immune suppression can also be achieved by inhibiting natural killer cells29,30.Then perform functional enrichment analysis to clarify the differences between their potential functions and the pathways involved. KEGG enrichment analysis revealed the central involvement of microRNAs in cancer. Different miRNAs have different effects on the prognosis of lung adenocarcinoma patients. Hongshuang et al.31 found that miR-1260b, miR-21-3p and miR-92a-3p were associated with early recurrence and metastasis in 160 samples of lung adenocarcinoma patients. Differences between the two clusters in the pathways revealed that PANoptosis genes play an essential role in the transformation of lung adenocarcinoma into non-small cell lung cancer. Statistically, less than 10 percent of patients with EGFR-mutated non-small cell lung cancer, especially lung adenocarcinoma, will transform into small cell lung cancer. This process usually occurs within 18 months of diagnosis. The primary mechanism of pathogenesis is unclear and may be related to p53 and its mechanism pathways. A common precursor transformation theory exists to explain this phenomenon. The reason for this is that small-cell lung cancer and non-small-cell lung cancer may originate from the same alveolar type II cells32. PANoptotic genes mainly control the cell cycle and their role in transformation deserves further research to discover. GO enrichment analysis revealed that it is mainly involved in nuclear division, organelle fission, chromosome region, extracellular matrix or structural organization in biological processes (BP), mainly in collagen-containing extracellular matrix, chromosomal region, condensed chromosome, chromosome, centromeric region (CC), and ATP hydrolysis activity in molecular function (MF). The above results suggest that it plays a vital role in cell growth, proliferation, and differentiation, further indicating its precise role in tumor development. Next, we calculated the risk score. Further validation was performed based on the median risk score with TCGA data as the train group and GEO as the test group for external data. We found a very consistent survival relationship in the experimental group, test group, and all data.After comparing the survival data between the high-risk and low-risk groups based on clinical data such as lymph node metastasis, age, and gender, significant differences were found between the high-risk and low-risk groups. Then, we visualized the survival rates of samples from the high-risk and low-risk groups, and it was evident that the survival rates of patients in the high-risk group were lower than those in the low-risk group. A heat map was plotted for the expression of the selected genes, where ADM, FLNC, PITX3, LING02, and FSIP2 were expressed in the high-risk group, while C11ofF16 and MS4A1 were more abundantly expressed in the low-risk group.Next, forest plots were used to demonstrate that risk score and stage are factors that affect patient prognosis. Then, based on the clinical information of the patient, the ROC curve and C-index curve were plotted, and it was found that the risk score had high accuracy in predicting patient prognosis. Subsequently, we created a column chart based on the clinical information of the patient and found that the expected and actual outcomes were closer in predicting patient prognosis. These column charts provide a more practical tool to assist clinicians in developing appropriate personalized treatment plans for LUAD patients, which can help improve clinical outcomes.Further research was conducted on the immune-related differences between the two groups of patients. In contrast, we found significant differences in immune checkpoints between the high-risk and low-risk groups. In the high-risk group, the expression levels of CD276, CD20, CD27, and TNFSF9 are higher. There were also significant differences in TMB between the two groups. Meanwhile, it is not difficult to observe from the K–M curve that the prognosis of patients in the high TMB group is significantly better than that of patients in the low TMB group.Next, we analyzed the tumor microenvironment between the two groups and found that although there was no difference in estimated scores between the two groups, there was a difference in immune scores. Meanwhile, we compared the immune cell differences between the two groups and found that plasma cell, T cell cd4 memory resting, DC resting, and mast cell resting differed between the two groups. Simultaneously, the low-risk group was higher than the high-risk group. Lastly, we evaluated TIDE between the two groups and found that the high-risk group had a higher likelihood of immune escape. According to the Imvigor database, there was a significant difference in OS between the two groups.We finally screened patients in the high-risk group for sensitive drugs. Many sensitivity drugs were detected to to be more sensitive for patients in the high-risk group like Axitinib, Doramapimod, and leflunomide. Benjamin et al.33 found that Axitinib may benefit some advanced-stage patients in terms of survival. Shitong et al.34 found that Doramapimod may play a better role in cervical cancer. However, there are fewer studies on lung cancer. Rui et al.35 revealed the cancer-inhibitory effect of leflunomide. They found that leflunomide inhibited the growth of tumor cells and organized the metastasis of lung cancer cells through multiple pathways and modalities.Although this study remedies a gap area of previous research. The PRGs in the signature could refine the prediction performance of LUAD survival outcome, and evaluate the immune conditions and forecast the immune checkpoints of LUAD patients. The PRG identified in this study may provide potential therapeutic targets or prognostic factors for LUAD patients. Our research has several limitations. This is a retrospective study based on gene expression profiles and some clinical variables in the TCGA database. Some specific clinical information related to lung cancer may not be available.
