| Literature DB >> 35887124 |
Pouya Javadian1, Chao Xu2, Virginie Sjoelund2, Lindsay E Borden1, Justin Garland3, Doris Mangiaracina Benbrook1,3.
Abstract
Racial disparities in incidence and survival exist for many human cancers. Racial disparities are undoubtedly multifactorial and due in part to differences in socioeconomic factors, access to care, and comorbidities. Within the U.S., fundamental causes of health inequalities, including socio-economic factors, insurance status, access to healthcare and screening and treatment biases, are issues that contribute to cancer disparities. Yet even these epidemiologic differences do not fully account for survival disparities, as for nearly every stage, grade and histologic subtype, survival among Black women is significantly lower than their White counterparts. To address this, we sought to investigate the proteomic profiling molecular features of endometrial cancer in order to detect modifiable and targetable elements of endometrial cancer in different racial groups, which could be essential for treatment planning. The majority of proteins identified to be significantly altered among the racial groups and that can be regulated by existing drugs or investigational agents are enzymes that regulate metabolism and protein synthesis. These drugs have the potential to improve the worse outcomes of endometrial cancer patients based on race.Entities:
Keywords: endometrial cancer; molecular profiling; proteomic; racial disparity
Mesh:
Substances:
Year: 2022 PMID: 35887124 PMCID: PMC9318530 DOI: 10.3390/ijms23147779
Source DB: PubMed Journal: Int J Mol Sci ISSN: 1422-0067 Impact factor: 6.208
Comparison of patient characteristics between groups.
| Black ( | White ( | American Indian ( | Asian ( |
| |
|---|---|---|---|---|---|
| Age (yrs) | 63.6 ± 13.5 | 59.2 ± 6.2 | 58.1 ± 10.5 | 64.1 ± 12.5 | 0.45 |
| BMI | 39.2 ± 10.3 | 35.4 ± 7.1 | 42.2 ± 8.7 | 38.6 ± 8.4 | 0.27 |
| Smoking | 2 | 7 | 2 | 1 | 0.29 |
| Hypertension | 5 | 9 | 7 | 4 | 0.012 |
| Alcohol use | 1 | 2 | 1 | 0 | 0.84 |
| NSAID use | 1 | 0 | 1 | 0 | 0.15 |
| Cardiovascular disease | 3 | 3 | 5 | 2 | 0.21 |
| Autoimmune disease | 1 | 0 | 0 | 0 | 0.44 |
| Diabetes | 8 | 3 | 7 | 2 | 0.011 |
Figure 1Heat map of log2 normalized levels for each protein found to be present at significantly different levels across the four races.
Figure 2Comparison of protein differences across all racial groups.
List of gene IDs for proteins significantly different in endometrial cancer specimens from Black, American Indian and Asian racial groups compared to the White racial group.
| Race | Gene IDs | |
|---|---|---|
| Higher Concentration | Lower Concentration | |
|
| AIF1, AGRN, ASS1, CUL3, DAG1, DPYSL2, EHD1, EIF4A2, EIF4G2, EPS15, F13A1, GMFG, GFM1, HK2, HTATSF1 IFI16, MAPK3, NES, NPEPL1, OXSR1, PTPN6, PTGDS, PFAS, RAB5B, RAD50, SCRN1, SNX1, SNTB1, SERPINB9, SARS2, TPP2, UBR4, USP47, WDR5, YWHAQ, | ATP2A2, APOC1, MAP1S, RPL4, RPL23, RPS16, SERPINA1, STT3A, ZMYND8 |
|
| AGRN, ASS1, AIF1, CLINT1, CALD1, DAG1, EIF4G2, EPS15, F13A1, GFM1, HK2, HMGN2, KRT19, NPEPL1, SNX1, SNTB1, SARS2, UBR4, USP47, TPP2, WDR5 | DX3X, MAP1S, PFAS |
|
| APOC1, CKB, EIF4G2, F13A1, GBP2, GFM1, HMGN2, KRT19, NPEPL1, RAB5B, SNTB1, SERPING1, SARS2, SERPINA1, UBR4, USP47, VIM, WDR5 | CLINT1, DDX3X, EIF3E, GBAS, IFI16, PTPN6, OXSR1, RPL5, SLC25A3, STRBP |
Figure 3IPA-identified canonical pathways associated with 44 proteins present at significantly different levels in endometrial cancer specimens from Black, African American or Asian compared to the White race. B: Black, AI: American Indian, A: Asian, W: White.
Figure 4Association of differential proteins with endometrial cancer. These pathways provide a visualization of fold change increase (red) or decrease (green) in the specific racial group compared to the White racial group.
Targetable differential proteins that have reported survival and prognostic value.
| Gene Symbol | Expression in Endometrial Cancer vs. Normal Tissue ¥ [ | Biomarker-Driven Therapy | Disease or Use | Clinical Trial Phase | Impact on Endometrial Cancer Patient Survival (TCGA) † [ | Expression in Our Study Cohort |
|---|---|---|---|---|---|---|
|
| Lower ( | Rapamycin/ | Multiple cancers | Rapamycin Approved/Phase 2 | Worse outcome with higher expression | AI (High) |
|
| Lower | RGX-202 [ | Gastrointestinal cancer | Phase 1 | No | A (High) |
|
| No significant difference | Zotatifin [ | Solid tumors | Phase 1–2 | Worse outcome with higher expression | B (High) |
|
| Higher | 2-DG and analogs [ | Prostate cancer, PET imaging | Phase 2 | No | B and AI (High) |
|
| Lower | Ulixertinib [ | Solid tumors | Phase 2 | No | B (High) |
|
| No significant difference | Lutein [ | Oral cancer | Preclinical | No | B (High) |
|
| Higher | Acivicin [ | Liver cancer | Preclinical | No | B (High) |
|
| Higher | Sodium stibogluconate [ | Melanoma | Phase 1 | Worse outcome with higher expression | B (High) |
|
| Lower | Trastuzumab [ | Breast cancer | Phase 2 (Exploratory Biomarker) | No | A and AI (High) |
B: Black, AI: American Indian, A: Asian, W: White—with the highest expression indicated by (High) and the lowest expression indicated by (Low); PET: positiron emission tomography, ¥ based on proteomic expression in the CPTAC dataset [11,25], † survival based on the TCGA dataset [11,25].
Figure 5Illustration of functions and available drugs or drug candidates for identified targetable proteins.