1. Expression and Mutation of Necroptosis-Related Genes in Lung Adenocarcinoma
We obtained 17 Necroptosis-Related Genes (RIPK1, RIPK3, MLKL, TLR2, TLR3, TLR4, TNFRSF1A, PGAM5, ZBP1, NR2C2, HMGB1, CXCL1, USP22, TRAF2, ALDH2, EZH2, NDRG2) from previous literature reviews. IHC staining results provided levels of 9 of the 17 necroptotic proteins between LUAD and normal lung tissues (Supplementary Figure S1). Fig.1A demonstrated the mutation of NRG in lung adenocarcinoma. Gene mutations occurred in 17.29% of LUAD samples, of which TLR4, TLR2, EZH2, RIPK1, and NDRG2 were the five genes with the highest mutation frequency. The mutation frequency of TLR4 accounted for 10%, which was significantly higher than the other genes. The different colors in the legend below the image represent different types of genetic mutations. As can be seen from the graph, the most frequent type of mutation is MISSENSE MUTATION.
In the CNV analysis we found that the copy number of necroptosis genes were both amplified and deleted. Among them, ZBP1, RIPK1, and TRAF2 genes were significantly amplified, while HMGB1, TLR3, and ALDH2 were very significantly deleted (Fig.1B). Fig.1C showed the position of the genes on the chromosome. The red nodes represent gene amplification. The blue nodes represent gene deletion.
2. Identification and Validation of NRG Prognostic Signature
We randomized 902 patients included in the study into the Train and Test groups. A total of 11 NRGs significantly associated with prognosis were obtained by univariate Cox regression analysis between the expression of NRG and patients' overall survival. After that, based on the prognosis-related NRG expression, LASSO regression analysis was performed on the Train group samples to construct a prognostic signature that included five NRGs (Fig.2A-B). These five genes and their correlation coefficients in the signature were shown in Table 1. Then we divided the entire sample into low-risk and high-risk subgroups based on the median risk score, and survival curves were plotted using Kaplan-Meier analysis. The curves showed that the OS of the high-risk subgroup is much lower than that of the low-risk subgroup in both the Train and Test groups (Fig.2C-E，p＜0.001).
To assess the prognostic predictive performance of the signature, we plotted ROC curves (Fig.2F-H) and the AUC values in the Test group reached 0.763, 0.717, and 0.729 at year 1, 2, and 3, respectively, demonstrating the good performance of the signature in assessing prognosis.
Principal components analysis (Fig.2I-K) showed a significant bivariate distribution of patients in these two subgroups.
3. Risk Score Has Independent Prognostic Significance
Fig.3A demonstrated the relationship between risk score, patient survival and gene expression of necroptosis regulator. The heat map showed that RIPK3, TLR2 and ALDH2 were lowly expressed in the high-risk subgroup, which corresponds to their correlation coefficients in the predictive signature(Table.1).
We performed univariate and multivariate Cox analyses to test whether the 5-gene signature was an independent predictor of OS in patients with LUAD. Univariate Cox regression analysis(Fig.3B) showed a significant association between risk score and OS. (Train group: HR 2.089, 95% confidence interval [CI] 1.657-2.633, p < 0.001; Test group: HR 6.481, 95% CI 3.095-13.572, p < 0.001). After adjusting for other confounding variables, the five-gene signature remained an independent indicator of OS in multivariate Cox regression studies (Fig.3C) (Train group: HR 2.048, 95% CI 1.583-2.650, p < 0.001; Test group: HR 5.643, 95% CI 2.495-12.7643, p < 0.001).
The Sankey diagram (Fig.3D) illustrated the correspondence between the risk score, pathological stage, gender and survival status in LUAD patients.
4. Nomogram Could Predict Patients’ Prognosis
We developed a nomogram containing risk scores to quantitatively predict the prognosis of LUAD patients (Fig.4A). Gender, age and tumor stages were also included in the nomogram. The AUCs of patients at years 1, 2 and 3 were 0.729, 0.727 and 0.736, respectively (Fig.4B). Combined with the calibration curves of the nomogram shown in Fig.4C, the results show that the nomogram model has very good predictive performance for prognosis.
5. GO Analysis and KEGG Analysis
To explore the preliminary function of the signature composed of these 5 genes, we did GO functional analysis and KEGG pathway enrichment analysis using ClusterProfiler R package (adjusted p < 0.05, |logFC| > 1). GO analysis showed significant enrichment of genes in programmed necrotic cell death, necrotic cell death, I-kappaB kinase/NF-kappaB signaling, regulation of DNA-binding transcription factor activity and other functions (Fig.5A). In KEGG pathway analysis, we learned that these genes are mainly concentrated in Necroptosis, Salmonella infection and TNF signaling pathway (Fig.5B).
6. Immune Landscape Analysis
We analyzed the correlation between risk score and immune cell infiltration in LUAD. The results showed a significant positive correlation between risk score and CD8 T cells, Macrophage M0, activated CD4 memory T cells, etc.; and a significant negative correlation between risk score and resting Dendritic cells, etc. (Fig.6A-C). Notably, risk scores were positively correlated with activated Mast cells and CD4 memory T cells, and negatively correlated with inactivated both types of cells (Fig. 6D-G). For NK cells, risk scores were significantly positively correlated with such cells in either the activated or inactivated state (Fig.6H-I).
Fig.7A showed the results of the ESTIMATE analysis, and we can see that the immune score and ESTIMATE score were significantly lower in the high-risk subgroup.
7. Immunotherapy-related analysis
We performed a TMB correlation analysis, Fig.7B-C, showing that the Tumor Mutation Burden differed significantly between the two subgroups of high and low risk according to the risk score. Risk score and TMB were positively correlated, r=0.3. Considering that patients in high- and low-risk subgroups may respond differently to immunotherapy, we further investigated the response to ICI therapy represented by CTLA4/PD-1 inhibitors in both subgroups by ImmunoPhenoScore (IPS) (Fig.7D-G). Regardless of the CTLA4 and PD-1 status being positive or negative, patients in the high-risk subgroup had lower IPS than those in the low-risk subgroup. This suggests that patients in the high-risk subgroup do not show a significant clinical benefit from anti-CTLA4 and/or anti-PD-1 therapy. Our results, taken together, clearly indicate that our risk score is associated with non-response to immunotherapy and that immunotherapy is not recommended for the high-risk subgroup.
In addition to this, we also conducted a study on the relevance of stem cell therapy. Fig.7H showed a corresponding rise in the stem cell index as the risk score increased, which also implies that the higher the content of latent tumor stem cells in the tumor tissue, the more likely the tumor will recur. Because tumor development is associated with the presence of cancer stem cells (CSCs) within the tumor, which play an important role in metastasis and other malignant phenotypes8.
8. High-risk subgroups are more sensitive to chemotherapy
Finally, we tested the sensitivity of patients in high- and low-risk subgroups to familiar drugs. The R package pRophetic9,10 allows us to calculate IC50s for common chemotherapeutic agents in the cohort, including cisplatin, paclitaxel, doxorubicin, rapamycin, etc. The IC50 values suggested that patients with LUAD in the high-risk subgroup were significantly more sensitive to common chemotherapeutic agents such as cisplatin, paclitaxel, docetaxel, doxorubicin and rapamycin (Fig. 8A-E). In contrast, the low-risk subgroup had higher sensitivity to some EGFR-TKIs (Fig. 8F-H), suggesting that targeted therapy may provide benefit to these patients.