Q-omics provides the consensus-scored YJEFN3 profile across patient tissues and cancer cell-line models. YJEFN3 expression is associated with patient survival in 28 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, YJEFN3 is differentially expressed in 10, with the highest sampling consensus in LIHC. Additionally, YJEFN3 RNA expression shows 16,272 significant gene co-expression associations, with the highest sampling consensus in ACC. Together, these results highlight ACC, and LIHC as cancer lineages where YJEFN3 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 YJEFN3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes YJEFN3 survival associations across molecular data types. YJEFN3 RNA expression shows survival associations in the most cancer types (28), followed by mutation status (3). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible YJEFN3 RNA expression–survival associations across cancer types. High YJEFN3 expression shows unfavorable associations in ACC, KIRC, COAD, MESO and LIHC, but favorable associations in PAAD. 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 YJEFN3 RNA expression.
This table summarizes YJEFN3 tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 10. The strongest signals are observed in LIHC for RNA.
This table ranks reproducible tumor–normal expression differences for YJEFN3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. YJEFN3 shows lower tumor expression in THCA and higher tumor expression in LIHC, HNSC, UCEC, KIRC and LUSC. The LIHC box plot shows higher YJEFN3 RNA expression in tumor versus normal tissue (log2 FC = +0.561, t-test p < 0.001).
This table shows molecular features associated with YJEFN3 in patient tissues and cancer cell lines. In patient samples, YJEFN3 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, YJEFN3 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 LUNG_SCLC and SOFT_TISSUE.
