Q-omics provides the consensus-scored C9orf43 profile across patient tissues and cancer cell-line models. C9orf43 expression is associated with patient survival in 21 of 34 cancer types, with the highest sampling consensus in SCLC. Among the 18 cancer types available for tumor–normal comparison, C9orf43 is differentially expressed in 14, with the highest sampling consensus in KICH. Additionally, C9orf43 RNA expression shows 17,788 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight SCLC, KICH, and UVM as cancer lineages where C9orf43 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 C9orf43 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes C9orf43 survival associations across molecular data types. C9orf43 RNA expression shows survival associations in the most cancer types (21), followed by mutation status (6). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible C9orf43 RNA expression–survival associations across cancer types. High C9orf43 expression shows unfavorable associations in ACC and LIHC, but favorable associations in SCLC, LUSC, LAML and READ. The SCLC 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 SCLC as the clearest survival context for C9orf43 RNA expression.
This table summarizes C9orf43 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 14. The strongest signals are observed in KICH for RNA.
This table ranks reproducible tumor–normal expression differences for C9orf43. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. C9orf43 shows lower tumor expression in KICH, THCA and KIRC and higher tumor expression in COAD, HNSC and UCEC. The KICH box plot shows higher C9orf43 RNA expression in normal versus tumor tissue (log2 FC = −1.520, t-test p < 0.001).
This table shows molecular features associated with C9orf43 in patient tissues and cancer cell lines. In patient samples, C9orf43 shows the broadest associations at the RNA and protein expression levels, with UVM recurring as the lineage with the largest associated feature set. In cancer cell lines, C9orf43 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in BLOOD_Leukemia, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Lymphoma.
