Q-omics provides the consensus-scored TXLNA profile across patient tissues and cancer cell-line models. TXLNA 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, TXLNA is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, TXLNA protein abundance shows 20,138 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where TXLNA 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 TXLNA — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes TXLNA survival associations across molecular data types. TXLNA RNA expression shows survival associations in the most cancer types (21), followed by mutation status (4) and mass-spec protein abundance (7). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible TXLNA RNA expression–survival associations across cancer types. High TXLNA expression shows unfavorable associations in ACC, LIHC, KICH, LGG and KIRC, but favorable associations in HNSC. 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 TXLNA RNA expression.
This table summarizes TXLNA tumor–normal expression differences by data type. RNA shows broader differences across cancer types, with a lineage consensus of 13, while mass-spec protein shows differences in 7. The strongest signals are observed in HNSC for RNA and CCRCC for protein.
This table ranks reproducible tumor–normal expression differences for TXLNA. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. TXLNA shows higher tumor expression in HNSC, LIHC, STAD, COAD, KIRP and BLCA. The HNSC box plot shows higher TXLNA RNA expression in tumor versus normal tissue (log2 FC = +0.692, t-test p < 0.001).
This table shows molecular features associated with TXLNA in patient tissues and cancer cell lines. In patient samples, TXLNA 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, TXLNA RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LIVER, while CRISPR and shRNA rows add functional-dependency signals in BLOOD_Leukemia and LARGE_INTESTINE.
