Gene co-expression networks capture global transcriptional organization and can be interrogated for structural robustness under progressive edge removal. We compared LUAD tumor (TCGA; n=513) and normal lung (GTEx; n=578) co-expression networks built from log-transformed TPM values using Pearson correlation thresholding (positive correlations only). Using a matched joint gene set (top 4,000 by combined variance), we tracked the fraction of nodes in the giant connected component (GCC) across correlation thresholds and defined the critical threshold r* by three complementary criteria: first crossing of GCC<0.9, first crossing of GCC<0.5, and the threshold of maximal negative slope (knee proxy). Across all metrics, tumor networks fragmented at lower correlation thresholds than normal lung. Bootstrap resampling over random gene subsets (60 replicates, 2,000 genes/replicate) confirmed robustness (P(tumor r*< normal r*) = 0.85 for the knee metric) and revealed higher tumor variability, consistent with increased heterogeneity and reduced network stability in LUAD. Keywords: LUAD, gene co-expression, percolation, network robustness, GCC, TCGA, GTEx Introduction Cancer transcriptomes exhibit widespread changes in gene regulation and co-regulation. Gene co-expression networks represent genes as nodes and co-expression relationships as edges; their large-scale topology can be probed by progressively increasing a correlation threshold, effectively removing weaker edges. The resulting decline of the giant connected component (GCC) provides a direct, interpretable measure of network-level robustness. Here we test whether LUAD (lung adenocarcinoma) tumor networks show reduced structural stability relative to normal lung tissue. We quantify fragmentation across thresholds using multiple complementary definitions of the critical threshold r* and validate robustness via tumor-only percolation, gene-set size sensitivity analysis, and bootstrap resampling. Methods Data and preprocessing LUAD primary tumor RNA-seq expression (n=513) and normal lung expression (n=578) were analyzed using TPM values with log transformation (X = log(1+TPM)). Genes with zero variance were removed. Matched (joint) gene set selection For cross-cohort comparison, analyses were restricted to a matched node set: the top 4,000 genes ranked by combined variance across tumor and normal cohorts (computed on the jointly filtered gene universe). This “joint set” design ensures apples-to-apples comparison of network topology. Network construction Pairwise Pearson correlations were computed between genes across samples. Only positive correlations were retained. For a threshold r, an undirected edge was present if Cij ≥ r. Connectivity and critical threshold definitions For each r, GCC(r) was computed as the fraction of nodes in the largest connected component. The critical threshold r* was defined using three criteria: (i) first crossing of GCC<0.9, (ii) first crossing of GCC<0.5, and (iii) the threshold of maximal negative slope (knee proxy). Tumor-only percolation and gene-set size robustness To characterize intrinsic LUAD behavior, tumor-only percolation was evaluated using the top 8,000 tumor genes by variance. Gene-set size robustness was assessed by repeating tumor-only analysis for 4,000–16,000 genes. Bootstrap validation Bootstrap resampling (60 replicates) randomly sampled 2,000 genes without replacement per replicate from the joint gene pool. For each replicate, r* values were recomputed for tumor and normal networks. Results Tumor networks fragment earlier than normal lung (matched joint gene set) Using the joint top-4,000 gene set, LUAD tumor networks lost global connectivity at lower thresholds than normal lung. The first crossing of GCC below 0.9 occurred at tumor r* = 0.420 ± 0.006 versus normal r* = 0.584 ± 0.006 (Δ ≈ 0.164). Under GCC<0.5, tumor r* = 0.634 ± 0.005 versus normal r* = 0.730 ± 0.002 (Δ ≈ 0.096). The knee proxy (maximum slope) remained lower in tumors (0.732 ± 0.031) than normal lung (0.767 ± 0.013; Δ ≈ 0.035).