Q-omics provides the consensus-scored MPC2 profile across patient tissues and cancer cell-line models. MPC2 expression is associated with patient survival in 22 of 34 cancer types, with the highest sampling consensus in ESCA. Among the 18 cancer types available for tumor–normal comparison, MPC2 is differentially expressed in 9, with the highest sampling consensus in LIHC. Additionally, MPC2 RNA expression shows 18,899 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ESCA, LIHC, and ACC as cancer lineages where MPC2 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 MPC2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes MPC2 survival associations across molecular data types. MPC2 RNA expression shows survival associations in the most cancer types (22), followed by mass-spec protein abundance (4). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible MPC2 RNA expression–survival associations across cancer types. High MPC2 expression shows unfavorable associations in ESCA, KICH, LGG and ACC, but favorable associations in COAD and KIRC. The ESCA 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 ESCA as the clearest survival context for MPC2 RNA expression.
This table summarizes MPC2 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 9, while mass-spec protein shows differences in 6. The strongest signals are observed in LIHC for RNA and COAD for protein.
This table ranks reproducible tumor–normal expression differences for MPC2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. MPC2 shows lower tumor expression in KICH and COAD and higher tumor expression in LIHC, BRCA, THCA and PRAD. The LIHC box plot shows higher MPC2 RNA expression in tumor versus normal tissue (log2 FC = +1.256, t-test p < 0.001).
This table shows molecular features associated with MPC2 in patient tissues and cancer cell lines. In patient samples, MPC2 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, MPC2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Myeloma, while CRISPR and shRNA rows add functional-dependency signals in SOFT_TISSUE and UPPER_AERODIGESTIVE_TRACT.
