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Genetic characteristics and transcriptional changes in ICD-related genes in LUAD

Summarized 34 ICD-related genes were identified through a large-scale meta-analysis11. The expression of 34 ICD genes in LUAD samples and normal samples was first analyzed (Figure S1A), and most of the ICD genes expressions were significantly different except for ATG5, IL10, CD8A, and CD8B. Secondly, the location of ICD-related genes in the human genome was analyzed (Figure S1B). the variation of ICD-related genes in LUAD patients in the TCGA cohort was also assessed. The results showed that approximately 69.63% (188/270) of LUAD patients had mutations in ICD-related genes, and the top 20 mutations in ICD-related genes were displayed in the study, with the highest frequency of mutations in TLR4 and NLRP3 (Figure S1C and Figure S1D).

The study also performed GO enrichment analysis of ICD-related genes (Figure S1E), which showed that, in terms of biological processes, the main enrichment was in various receptor activities. In terms of cellular components, the main enrichment was in the cytolytic granule and inflammasome complex. In terms of molecular functions, the main enrichment was in the biological processes of interleukin. In addition, KEGG enrichment analysis showed that ICD-related genes were enriched in the NOD-like receptor signaling pathway, Toll-like receptor signaling pathway, and Necroptosis. (Figure S1F).

A total of 1367 characteristic lncRNAs were selected by matching the training dataset with validation datasets for in-depth analysis. We employed consensus cluster analysis to partition the TCGA-LUAD dataset into two groups based on the high-expression and low-expression of ICD-related genes. Subsequently, 473 lncRNAs were identified by conducting differential expression analysis (Fig.2A and B). These lncRNAs were then compared with the 300 lncRNAs obtained by Pearson correlation analysis (Fig.2C) to identify 176 ICD-related lncRNAs (Fig.2D). As a result, 24 ICD-related lncRNAs were ultimately identified by univariate Cox regression analysis (Supplementary Table 2).

(A) Heatmap displaying 34 ICD gene expression profiles among normal and LUAD samples in the TCGA cohort. (B) The location of ICD-related genes in the human genome. (C) Single Nucleotide Polymorphism analysis of ICD-related genes in the TCGA cohort. (E) Bar plot displaying Gene Ontology analysis based on 34 ICD genes. (F) Bar plot displaying KEGG analysis based on 34 ICD genes.

A total of 24 ICD-related lncRNAs were inputted into a comprehensive machine-learning model, which encompassed the 10 aforementioned methodologies for creating prognostic signatures. Figure3A illustrated the acquisition of a total of 101 prognostic models. The predictive signature created by the combination of RSF+Ridge had the greatest mean C index of 0.674, as determined by analyzing the training and test cohorts. This signature was identified as the ICDI signature, (Fig.3A and B). The obtained equation is as follows (see Supplementary Table 3 for detail):

$${text{ICDIscore}} = min Vert beta x - y Vert_{2}^{2} + {uplambda } Vert beta Vert _{2}^{2}$$

(A) A total of 101 combinations of machine learning algorithms for the ICDI signature via a tenfold cross-validation framework based on the TCGA-LUAD cohort. The C-index of each signature was calculated across validation datasets, including the GSE29013, GSE30219, GSE31210, GSE3141, and GSE50081cohort. (B) 24 ICD-related lncRNAs importance ranking in the RSF algorithm and 19 lncRNAs enrolled in the ICDI signature coefficient finally obtained in the Ridge algorithm. (C) KaplanMeier survival curve of OS between patients with a high score of ICDI signature and with a low score of ICDI signature in TCGA-LUAD, GSE29013, GSE30219, GSE31210, GSE3141, and GSE50081 cohort. (D) Receiver operator characteristic (ROC) analysis for ICDI signature in TCGA-LUAD, GSE29013, GSE30219, GSE31210, GSE3141, and GSE50081 cohort.

As the elastic net mixing parameter, was limited with 01. The is defined as (uplambda =frac{1-alpha }{2}{Vert beta Vert }_{2}^{2}+alpha {Vert beta Vert }_{1}).

LUAD patients were categorized into two groups based on their ICDI score: a high-score group and a low-score group. The median value was used as the cut-off point. Consistent with expectations, LUAD patients with low ICDI scores exhibited higher overall survival rates in the TCGA-LUAD, GSE29013, GSE30129, GSE31210, GSE3141, and GSE50081 datasets (Fig.3C).

The AUC values of 1-, 2-, 3-, 4-, and 5-year for the ICDI signature in the TCGA-LUAD cohort were estimated as 0.709, 0.678, 0.697, 0.716, and 0.660, respectively (Fig.3D), demonstrating that ICDI signature has promising predictive value for LUAD patients. It was validated in the GSE30219 cohort (0.891, 0.758, 0.744, 0.700, and 0.716), GSE31210 cohort (0.750, 0.691, 0.653, 0.677 and 0.718), GSE3141 cohort (0.690, 0.716, 0.819, 0.801 and 0.729), GSE50081 cohort (0.685, 0.694, 0.712, 0.638, and 0.639), and GSE3141 cohort (0.639, 0.697, 0.794, 0.670, and 0.521) (Fig.3D). As a result of insufficient survival data, the GSE29013 cohort only computes the AUC values for 2-, 3-, and 4-year periods. Still, it possesses strong predictive capability (Fig.3D).

In addition, we compared the predictive value of the ICDI signature with other clinical variables (Fig.4A). The C-index of the ICDI signature was significantly higher than other clinical variables, covering staging, age, gender, etc.

(A) The C-index of the ICDI signature and other clinical characteristics in the TCGA-LUAD, GSE29013, GSE30219, GSE31210, GSE3141 and GSE50081 cohorts. (B) The C-index of the ICDI signature and other signatures developed in the TCGA-LUAD, GSE29013, GSE30219, GSE31210, GSE3141 and GSE50081 cohorts.

Gene expression analysis based on machine learning can be leveraged to predict the outcome of diseases, which in turn can facilitate in early screening of diseases, as well as in researching new therapeutic modalities. Substantial predictive signatures have emerged in recent years. To compare the ICDI signature with published signatures, we searched for LUAD-related disease prediction model articles. Excluding articles with unclear prediction model formulas and missing corresponding gene expression data in the training and validation groups, 102 LUAD-related predictive signatures were finally enrolled (Supplementary Table 4). These signatures contained various kinds of Biological processes, such as cuproptosis, ferroptosis, autophagy, epithelial-mesenchymal transition, acetylation, amino acid metabolism, anoikis, DNA repair, fatty acid metabolism, hypoxia, Inflammatory, N6-methyladenosine, mitochondrial homeostasis, and mTOR, which was established in TCGA-LUAD, GSE29013, GSE30219, GSE31210, GSE3141, and GSE50081 and compared with the C-index of ICDI, it can be seen that the ICDI signature outperformed the majority of signatures in each cohort (Fig.4B).

To investigate the contribution of ICDI features in the LUAD TIME, we evaluated the correlation of ICDI features with immune infiltrating cells and immune-related processes. Based on TIMER algorithm, CIBERSORT algorithm, quantiseq algorithm, MCPcounter algorithm, xCell algorithm, and EPIC algorithm, the ICDI signature was correlated with most immune infiltrating cells except for a few (such as activated NK cells and CD8+naive T cells) (Fig.5A). Based on the ssGSEA algorithm, the ICDI signature was significantly correlated with most immune-related processes (Fig.5B). Based on the ESTIMATE algorithm, the ICDI signature was negatively correlated with StromalScore, ImmuneScore, and ESTIMATEScore, and positively correlated with TumorPurity (Fig.5C), as expected.

(A) Heatmap displaying the correlation between the ICDI signature and 13 immune-related processes. (B) Heatmap displaying the correlation between the ICDI signature and immune infiltrating cells. (C) Box plot displaying the correlation between the ICDI signature and The ESTIMATE Immune Score, ImmuneScore, StromalScore, and TumorPurity. (D) Box plot displaying the correlation between the ICDI signature and immune modulators.

In addition, the study also evaluated the relationship between ICDI signature and known immune modulators (CYT, TLS, Davoli_IS, Roh_IS, Ayers_expIS, TIS, RIR, and TIDE) (Fig.5D). The values of most of the immune modulators (CYT, TLS, Davoli_IS, Roh_IS, Ayers_expIS, and TIS) were significantly higher in the low ICDI signature scores group. The RIR values and TIDE score were all significantly higher in the high ICDI signature scores group, which suggested a higher potential for immunological escape (Fig.5D) All of these displayed ICDI signature was a potential immunotherapeutic biomarker.

To further investigate the potential of ICDI signature as an immunotherapeutic biomarker, the study calculated ICDI scores for each immunotherapy cohort respectively to appraise its predictive valuation. The findings indicated that those with a low ICDI score were more prone to derive advantages from immunotherapy. (Fig.6A) The receiver operating characteristic (ROC) analysis conducted in the study showed that the ICDI signature exhibited a consistent ability to predict the efficacy of immunotherapy-based treatment. This finding was further supported by the analysis of immunotherapy datasets, including cohort Melanoma-GSE78220, STAD-PRJEB25780, and GBM-PRJNA482620, which yielded ROC values of 0.771, 0.671, and 0.723, respectively (Fig.6B).

(A) Box plot displaying the correlation between the ICDI signature and immunotherapy response in the immunotherapy dataset (Melanoma-GSE78220, STAD-PRJEB25780, and GBM-PRJNA482620). (B) ROC curves of ICDI signature to predict the benefits of immunotherapy in the immunotherapy dataset (Melanoma-GSE78220, STAD-PRJEB25780, and GBM-PRJNA482620). (C) Box plot displaying the correlation between the ICDI signature and chemotherapy drugs.

Chemotherapy resistance is a significant barrier to the effectiveness of chemotherapy and targeted therapy in treating advanced lung cancer. We analyzed to determine the drug sensitivities of various chemotherapeutics in living organisms. We then compared the drug sensitivities using the ICDI signature. Individuals with low ICDI scores exhibited a notable rise in sensitivity to erlotinib, gefitinib, docetaxel, and paclitaxel. However, there was no significant variation in sensitivity to cisplatin and 5-fluorouracil. (Fig.6C) The study offers instructions on the administration of chemotherapeutic medications in individuals with LUAD.

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