Q-omics provides the consensus-scored SH2B3 profile across patient tissues and cancer cell-line models. SH2B3 expression is associated with patient survival in 27 of 34 cancer types, with the highest sampling consensus in UVM. Among the 18 cancer types available for tumor–normal comparison, SH2B3 is differentially expressed in 13, with the highest sampling consensus in HNSC. Additionally, SH2B3 RNA expression shows 19,977 significant gene co-expression associations, with the highest sampling consensus in UVM. Together, these results highlight UVM, and HNSC as cancer lineages where SH2B3 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 SH2B3 — synthetic lethality, tumor antigen, and pembrolizumab response.
This table summarizes SH2B3 survival associations across molecular data types. SH2B3 RNA expression shows survival associations in the most cancer types (27), followed by mutation status (4) and mass-spec protein abundance (2). The rightmost column indicates the cancer type with the highest sampling consensus for each molecular layer.
This table ranks reproducible SH2B3 RNA expression–survival associations across cancer types. High SH2B3 expression shows unfavorable associations in UVM, KIRP, MESO and LAML, but favorable associations in KIRC and HNSC. The UVM 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 UVM as the clearest survival context for SH2B3 RNA expression.
This table summarizes SH2B3 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 2. The strongest signals are observed in KIRC for RNA and LSCC for protein.
This table ranks reproducible tumor–normal expression differences for SH2B3. A negative fold-change indicates higher expression in normal tissue than in tumor tissue. SH2B3 shows lower tumor expression in LUSC, LUAD and UCEC and higher tumor expression in HNSC, KIRC and STAD. The HNSC box plot shows higher SH2B3 RNA expression in tumor versus normal tissue (log2 FC = +1.687, t-test p < 0.001).
This table shows molecular features associated with SH2B3 in patient tissues and cancer cell lines. In patient samples, SH2B3 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, SH2B3 RNA and mutation anchors are most strongly linked to RNA-expression features, especially in LUNG_SCLC, while CRISPR and shRNA rows add functional-dependency signals in URINARY_TRACT and BLOOD_Lymphoma.
