Q-omics provides the consensus-scored MCM9 profile across patient tissues and cancer cell-line models. MCM9 expression is associated with patient survival in 20 of 34 cancer types, with the highest sampling consensus in KIRC. Among the 18 cancer types available for tumor–normal comparison, MCM9 is differentially expressed in 11, with the highest sampling consensus in KICH. Additionally, MCM9 RNA expression shows 20,951 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight KIRC, KICH, and ACC as cancer lineages where MCM9 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 MCM9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MCM9 survival associations across molecular data types. MCM9 RNA expression shows survival associations in the most cancer types (20), followed by mutation status (4) and mass-spec protein abundance (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MCM9 RNA expression–survival associations across cancer types. High MCM9 expression shows favorable associations in KIRC, SKCM, READ, SCLC, UCS and THYM. The KIRC Kaplan–Meier curve shows clear separation, with the low-expression group declining faster, consistent with the favorable association (log-rank p < 0.001). Together, the overview and detailed table identify KIRC as the clearest survival context for MCM9 RNA expression.
This table summarizes MCM9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 11, while mass-spec protein shows differences in 5. The strongest signals are observed in HNSC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for MCM9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MCM9 shows lower tumor expression in KICH and THCA and higher tumor expression in HNSC, BLCA, CHOL and STAD. The KICH box plot shows higher MCM9 RNA expression in normal versus tumor tissue (log2 FC = −1.084, t-test p < 0.001).
This table shows molecular features associated with MCM9 in patient tissues and cancer cell lines. In patient samples, MCM9 shows the broadest associations at the RNA and protein expression levels, with ACC recurring as the lineage with the largest associated feature set. In cancer cell lines, MCM9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in KIDNEY, while CRISPR and shRNA rows add functional-dependency signals in CNS and BLOOD_Leukemia.
