gap junction protein beta 3Genealiases: CX31 · DFNA2 · DFNA2B · EKV · EKVP1
Q-omics provides the consensus-scored GJB3 profile across patient tissues and cancer cell-line models. GJB3 expression is associated with patient survival in 19 of 34 cancer types, with the highest sampling consensus in MESO. Among the 18 cancer types available for tumor–normal comparison, GJB3 is differentially expressed in 12, with the highest sampling consensus in THCA. Additionally, GJB3 RNA expression shows 15,679 significant gene co-expression associations, with the highest sampling consensus in TGCT. Together, these results highlight MESO, THCA, and TGCT as cancer lineages where GJB3 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 GJB3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes GJB3 survival associations across molecular data types. GJB3 RNA expression shows survival associations in the most cancer types (19), followed by mutation status (9) and mass-spec protein abundance (1). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible GJB3 RNA expression–survival associations across cancer types. High GJB3 expression shows unfavorable associations in MESO, LUAD, PAAD, KIRC, ACC and UVM. The MESO 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 MESO as the clearest survival context for GJB3 RNA expression.
This table summarizes GJB3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 12, while mass-spec protein shows differences in 2. The strongest signals are observed in THCA for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for GJB3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. GJB3 shows lower tumor expression in KIRC and KICH and higher tumor expression in THCA, HNSC, LUSC and LUAD. The THCA box plot shows higher GJB3 RNA expression in tumor versus normal tissue (log2 FC = +4.372, t-test p < 0.001).
This table shows molecular features associated with GJB3 in patient tissues and cancer cell lines. In patient samples, GJB3 shows the broadest associations at the RNA and protein expression levels, with TGCT recurring as the lineage with the largest associated feature set. In cancer cell lines, GJB3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in CNS, while CRISPR and shRNA rows add functional-dependency signals in OVARY and BLOOD_Lymphoma.
