Q-omics provides the consensus-scored PLEKHM2 profile across patient tissues and cancer cell-line models. PLEKHM2 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, PLEKHM2 is differentially expressed in 11, with the highest sampling consensus in KIRC. Additionally, PLEKHM2 protein abundance shows 22,004 significant protein co-abundance associations, with the highest sampling consensus in HNSC. Together, these results highlight ACC, KIRC, and HNSC as cancer lineages where PLEKHM2 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 PLEKHM2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes PLEKHM2 survival associations across molecular data types. PLEKHM2 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (9) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible PLEKHM2 RNA expression–survival associations across cancer types. High PLEKHM2 expression shows unfavorable associations in ACC, LUSC, LIHC, BLCA, LGG and OV. The ACC 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 ACC as the clearest survival context for PLEKHM2 RNA expression.
This table summarizes PLEKHM2 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 6. The strongest signals are observed in KIRC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for PLEKHM2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. PLEKHM2 shows lower tumor expression in KICH and higher tumor expression in KIRC, HNSC, LIHC, KIRP and THCA. The KIRC box plot shows higher PLEKHM2 RNA expression in tumor versus normal tissue (log2 FC = +0.748, t-test p < 0.001).
This table shows molecular features associated with PLEKHM2 in patient tissues and cancer cell lines. In patient samples, PLEKHM2 shows the broadest associations at the RNA and protein expression levels, with HNSC recurring as the lineage with the largest associated feature set. In cancer cell lines, PLEKHM2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SKIN, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and LARGE_INTESTINE.
