Q-omics provides the consensus-scored CLGN profile across patient tissues and cancer cell-line models. CLGN expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, CLGN is differentially expressed in 13, with the highest sampling consensus in KICH. Additionally, CLGN RNA expression shows 17,344 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight LIHC, KICH, and TGCT as cancer lineages where CLGN shows reproducible signals across survival, tumor–normal expression, and patient cross-omics analyses.
Every result is evaluated using two consensus scores. Sampling consensus measures how consistently a finding is reproduced within a cancer lineage across different conditions. Lineage consensus measures how broadly the result is shared across cancer types, distinguishing pan-cancer signals from lineage-specific patterns.
Premium analyses for CLGN — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes CLGN survival associations across molecular data types. CLGN RNA expression shows survival associations in the most cancer types (22), followed by mutation status (5) and mass-spec protein abundance (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible CLGN RNA expression–survival associations across cancer types. High CLGN expression shows unfavorable associations in LIHC, STAD, UVM, KICH and ESCA, but favorable associations in LGG. The LIHC Kaplan–Meier curve shows clear separation, with the high-expression group declining faster, consistent with the unfavorable association (log-rank p < 0.001). Together, the overview and detailed table identify LIHC as the clearest survival context for CLGN RNA expression.
This table summarizes CLGN tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 3. The strongest signals are observed in KICH for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for CLGN. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. CLGN shows lower tumor expression in HNSC and COAD and higher tumor expression in KICH, KIRP, KIRC and BRCA. The KICH box plot shows higher CLGN RNA expression in tumor versus normal tissue (log2 FC = +4.660, t-test p < 0.001).
This table shows molecular features associated with CLGN in patient tissues and cancer cell lines. In patient samples, CLGN shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, CLGN RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in BONE and BLOOD_Leukemia.
