Q-omics provides the consensus-scored EXOSC9 profile across patient tissues and cancer cell-line models. EXOSC9 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in LIHC. Among the 18 cancer types available for tumor–normal comparison, EXOSC9 is differentially expressed in 14, with the highest sampling consensus in HNSC. Additionally, EXOSC9 protein abundance shows 34,314 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight LIHC, HNSC, and GBM as cancer lineages where EXOSC9 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 EXOSC9 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes EXOSC9 survival associations across molecular data types. EXOSC9 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and mass-spec protein abundance (12). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible EXOSC9 RNA expression–survival associations across cancer types. High EXOSC9 expression shows unfavorable associations in LIHC, UVM, KICH, LGG, HNSC and ACC. 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 EXOSC9 RNA expression.
This table summarizes EXOSC9 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14, while mass-spec protein shows differences in 11. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for EXOSC9. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. EXOSC9 shows higher tumor expression in HNSC, KIRC, STAD, LIHC, COAD and CHOL. The HNSC box plot shows higher EXOSC9 RNA expression in tumor versus normal tissue (log2 FC = +0.838, t-test p < 0.001).
This table shows molecular features associated with EXOSC9 in patient tissues and cancer cell lines. In patient samples, EXOSC9 shows the broadest associations at the RNA and protein expression levels, with GBM recurring as the lineage with the largest associated feature set. In cancer cell lines, EXOSC9 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in SOFT_TISSUE, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and BREAST.
