1. Identification of cuproptosis-related lncRNAs and construction of prognostic signature
By |R|>0.4 and P < 0.001 criteria, 2244 cuproptosis -related lncRNAs were identified from 16,876 incRNAs and 19 cuproptosis-related genes, co-expression relationships with cuproptosis-related genes and cuproptosis-related lncRNAs were visualised using Sankey diagram (Fig. 1). In the training group, LASSO COX regression analysis was used to identify cuproptosis-related lncRNAs, univariate Cox regression analysis identified 37 lncRNAs and then, multivariate COX analysis identified 16 lncRNAs as independent prognostic factors, The risk score for each sample was then calculated based on the expression levels of the 16 lncRNAs(Fig. 2a-2c) risk score =(-0.79670054952258*AC016747.2)+(0.454977942296554*LINC00205)+(0.971038537693743*AC006947.1)+(0.466282143262505*LINC00592)+(0.580816870667386*AC020634.2)+(-0.364069056069612*AC026355.2)+(-2.94320726773469*LINC02848)+(-0.760200155891508*ZNF571-AS1)+(0.429955081610087*CRIM1-DT)+(-1.0655839243752*SEPSECS-AS1)+(0.272457956361295*HIF1A-AS3)+(1.56215847973464*AC013267.1)+(-0.364308918322871*LINC02635)+(1.84725474576194*AL162632.3)+(0.646354490290833*AC004832.5)+(1.01912906171161*AC032011.1). The correlation heatmap also shows the relationship between cuproptosis-related genes and lncRNAs (Fig. 2d).
2. Survival analysis of the signature
According to the median value of the risk scores as the cutoff value, the patients were divided into a low risk group and a high risk group. We found that Overall survival (OS) and Progression-free survival (PFS) were significantly shorter in the high-risk group than in the low-risk group in both the training, testing groups, and all groups, respectively (Fig. 3). As shown in the Fig. 4, the risk curves reflect the relationship between riskscore and survival status in LUAD patients, and we found that mortality was higher in high-risk patients than in low-risk patients. Heatmap shows high and low risk levels for 16 lncRNAs, for example, LINC00205, LINC00592, AL162632.3S are high risk IncRNAs, AC026355.2, LINC02848, ZNF571-AS1 are low risk lncRNAs.
3. Independent analysis of prognostic factors
Univariate Cox regression and multivariate Cox regression analyses were used to determine whether the signature we constructed could be used as independent prognostic factors, independent of other clinical characteristics. Multivariate Cox regression results showed that stage (HR = 1.553, 1.342–1.798, P < 0.05) and risk score (HR = 1.028, 1.016–1.040, P < 0.05) were independently associated with OS, indicating that prognostic signature is an independent prognostic factor for patients with LUAD (Fig. 5a-5b). Next, we used receiver operating characteristic (ROC) curves to assess the predictive accuracy of the risk score. As shown in fig, the area under the ROC curve (AUC) for the riskscore was 0.756, which was better than age (0.536), gender (0.596), and stage (0.712). Similarly, the area under the ROC curve (AUC) for 1, 3 and 5 years were 0.756, 0.739 and 0.759 respectively (Fig. 5c), suggesting that the prognostic signature has good diagnostic significance.
4. Construction of a predictive nomogram and principal component analysis
We constructed a nomogram using the Age, Gender, Stage, T, risk score, N from the signature, and the nomogram can reliably predict the 1-, 3- and 5-years survival of patients(Fig. 6a-6b). Next, we constructed C-Index curves to compare the consistency indices of risk score with other clinical characteristics (Age, Gender, Stage), and we also explored whether there were differences in patient survival over time(Fig. 6c). This suggests that the signature we constructed not only has high predictive accuracy, but can also be used to compare the survival of patients across different periods. Finally, we did principal components analysis (PCA) to observe the distribution of patients for all genes, cuproptosis-related genes, cuproptosis–related IncRNAs, and risk IncRNAs, and the results showed a clear distribution of risk IncRNAs, demonstrating that these IncRNAs can be reliably used to construct the signature(Fig. 7). As shown in the figure, we found that the C-index values of risk scores were higher than those of other clinical characteristics, and the overall survival of patients in the low-risk group in stages I-II and III-IV was significantly better than that of patients in the high-risk group(Fig. 6d-6e).
5. Functional enrichment analysis and immune-related functional analysis
GO results showed that the cuproptosis-related IncRNAs enriched in negative regulation of proteolysis, regulation of pepridase activity, negative regulation of hydrolase activity (Fig. 8a). KEGG analysis showed that these IncRNAs may be related to the Cytokine − cytokine receptor interaction, Neutrophil extracellular trap formation, and MAPK signaling pathways, suggesting these incrnas are involved in the process of tumor development (Fig. 8b). Furthermore, We preformed immune-related functions to analysis the immune status of low-risk group and high-risk group, the results showed that type-III-IFN-response was significantly more active in the low-risk group than in the high-risk group, with no significant differences in other immune functions (Fig. 8c).
6. Tumor mutation burden analysis and drug sensitivity analysis
We used the maftools algorithm to observe mutations in the high and low risk groups and showed that for most genes, the frequency of mutations was higher in the high risk group than in the low risk group (TP53: low risk,43%;high risk 52%. TTN: low risk, 44%; high risk, 47%. MUC16: low risk, 38% ; high risk,42%) (Fig. 9a). Furthermore, we explored whether there was a difference in tumor mutation burden between the high and low risk groups, however, no significant difference was observed (P = 0.84) (Fig. 9b), the reasons for which need to be further explored, we then investigated whether there was a difference in survival between patients with high and low TMB. As shown in the Fig. 9c, OS was significantly better in the low TMB than in the high TMB group (P༜0.05). In addition, the difference in sensitivity to immunotherapy between patients in the high-risk and low-risk groups was further investigated using the TIDE algorithm. We found higher TIDE in the low risk group compared to the high risk group (Fig. 10a), suggesting that patients in the low risk group had a lower potential for immune escape and that immunotherapy was better. Finally, we used the pRRophetic packages to screen potentially effective anti-tumor drugs, including Masitinib, Tipifarnib, Bexarotene, 5-Fluorouracil, Midostaurin, Vinorelbine, Etoposide, Doxorubicin, and the indication results are shown in the Table 1. We then further analyzed the sensitivity of these drugs and we found that patients in the high risk group had lower IC50 values, representing a higher sensitivity of the drugs in the high risk patients (Fig. 10b-10i).
Anti-tumour drugs and indications
lung cancer, lymphoma, pancreatic cancer.
cutaneous T-cell lymphoma.
liver cancer, stomach cancer.
Lung cancer, Haematological tumours.
Non-small cell lung cancer, breast cancer, malignant lymphoma.
lung cancer, malignant lymphoma, malignant germ cell tumour.