Q-omics provides the consensus-scored SDF2 profile across patient tissues and cancer cell-line models. SDF2 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in ACC. Among the 18 cancer types available for tumor–normal comparison, SDF2 is differentially expressed in 12, with the highest sampling consensus in HNSC. Additionally, SDF2 protein abundance shows 22,907 significant protein co-abundance associations, with the highest sampling consensus in GBM. Together, these results highlight ACC, HNSC, and GBM as cancer lineages where SDF2 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 SDF2 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SDF2 survival associations across molecular data types. SDF2 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (1) and mass-spec protein abundance (5). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SDF2 RNA expression–survival associations across cancer types. High SDF2 expression shows unfavorable associations in ACC, STAD, LIHC, SKCM and HNSC, but favorable associations in LUSC. 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 SDF2 RNA expression.
This table summarizes SDF2 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 7. The strongest signals are observed in HNSC for RNA and HNSC for protein.
This table ranks reproducible tumor–normal expression differences for SDF2. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SDF2 shows lower tumor expression in KICH and KIRC and higher tumor expression in HNSC, LIHC, BRCA and BLCA. The HNSC box plot shows higher SDF2 RNA expression in tumor versus normal tissue (log2 FC = +0.822, t-test p < 0.001).
This table shows molecular features associated with SDF2 in patient tissues and cancer cell lines. In patient samples, SDF2 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, SDF2 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in PANCREAS, while CRISPR and shRNA rows add functional-dependency signals in LIVER and BLOOD_Lymphoma.
