Proteins in the blood could warn people of cancer more than seven years before it is diagnosed

  • May 18, 2024
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Proteins in the blood could warn people of cancer more than seven years before it is diagnosed

In a recent study published in Nature Communications, researchers from the United Kingdom (UK) investigated associations between 1,463 plasma proteins and 19 cancers, using observational and genetic approaches in participants of the UK Biobank. They found 618 protein-cancer associations and 317 cancer biomarkers, which included 107 cases detected over seven years before the diagnosis of cancer.

Study: Identifying proteomic risk factors for cancer using prospective and exome analyses of 1463 circulating proteins and risk of 19 cancers in the UK Biobank. Image Credit:


Proteins are pivotal in most biological processes, including the development of cancer, with some serving as known cancer risk factors or biomarkers. While previous studies have identified individual cancer-related proteins, new multiplex proteomics techniques allow for simultaneous assessment of proteins at a high-scale, especially those that remain unexplored in the cancer risk context.

Prospective studies face challenges owing to confounding and biases, but genetic variations that affect protein levels offer complementary evidence. Genetic predictors, especially cis-pQTLs (short for cis protein quantitative trait loci), provide robust insights into protein-cancer associations. Integrating observational and genetic approaches enhances the identification of proteins potentially causally linked to cancer development and progression.

This combined methodology aids in understanding cancer biology, identifying therapeutic targets and discovering diagnostic biomarkers. Therefore, in the present study, researchers employed an integrated multi-omics strategy merging prospective cohort and exome-variant studies to reveal the proteins potentially implicated in the etiology of cancer.

About the study

The present study utilized data from the UK Biobank, a prospective cohort comprising 44,645 adults (post-exclusion) aged 39 to 73 years, followed up for a median of 12 years. Participants underwent assessments, including a questionnaire, anthropometric measurements, and blood sample collection. Plasma samples were analyzed using the Olink Proximity Extension Assay to quantify 1463 proteins. Cancer registration and death data were obtained through record linkage to national registries. Exome sequencing data were used to explore genetic associations with protein levels.

Results and discussion

Observational analyses showed 4921 incident cancer cases at a median age of 66.9 years. Individuals who developed cancer were found to be older, had higher rates of addictions, and family history of cancer compared to the total analysis sample. Women with cancer tended to have fewer children, earlier menarche, higher rates of postmenopausal status, hormone replacement therapy use, and no oral contraceptive pill use.

A total of 371 proteins showed significant associations with the risk of at least one cancer, resulting in 618 protein-cancer associations. A total of 304 of these associations were linked to proteins enriched in messenger ribonucleic acid (mRNA) expression in tissues or candidate cells of cancer origin. While many associations were observed for proteins related to hematological cancers with high mRNA expression in B-cells or T-cells, associations were also found for proteins with high mRNA expression in various other tissues such as the liver, kidneys, brain, stomach, lung, colorectum, esophagus, and endometrium.

Hematological malignancies, including non-Hodgkin lymphoma (NHL), diffuse large B-cell non-Hodgkin lymphoma (DLBCL), leukemia, and multiple myeloma, accounted for more than half of the identified associations.

Notable associations included TNFRSF13B and SLAMF7 with multiple myeloma risk, PDCD1 and TNFRSF9 with NHL risk, and FCER2 and FCRL2 with leukemia risk. Additionally, associations were found for liver cancer (e.g., IGFBP7 and IGFBP3), kidney cancer (e.g., HAVCR1 and ESM1), lung cancer (e.g., WFDC2 and CEACAM5), esophageal cancer (e.g., REG4 and ST6GAL1), colorectal cancer (e.g., AREG and GDF15), stomach cancer (e.g., ANXA10 and TFF1), breast cancer (e.g., STC2 and CRLF1), prostate cancer (e.g., GP2, TSPAN1, and FLT3LG), endometrial cancer (e.g., CHRDL2, KLK4, and WFIKKN1), and ovarian cancer (e.g., DKK4 and WFDC2).

Lower evidence of associations was found for pancreatic, thyroid, melanoma, or lip and oral cancers. Pathway analyses suggested that adaptive immune response may play a role in hematological cancers. Minimal heterogeneity was observed after stratifying the associations by sex.

A total of 107 protein-cancer associations remained valid seven years after the blood draw, while genetic analyses supported 29. Additionally, four associations were supported by both long time-to-diagnosis (>7 years) and analyses involving cis-pQTL and exome-wide protein genetic scores (exGS): NHL was associated with CD74 and TNFRSF1B, leukemia with ADAM8, and lung cancer with SFTPA2. The findings revealed 38 proteins associated with cancer risk that are also targeted by currently approved drugs, suggesting potential for therapeutic intervention to reduce cancer risk.

Although this is the largest cohort study investigating circulating proteins and cancer so far, the analysis was restricted to baseline protein levels, potentially underestimating risks due to regression dilution bias. It also showed limited power for rare cancer sites and underrepresented populations, warranting further research in diverse cohorts.


In conclusion, the study found several links between blood proteins and cancer risk, with many detectable over seven years before the diagnosis of cancer. Genetic analyses supported their potential role in cancer development. Additionally, the findings could identify proteins that may help detect early cancer stages in individuals at risk, offering promising biomarkers for early diagnosis and improved patient outcomes.

Disclaimer: This story is auto-aggregated by a computer program and has not been created or edited by menshealthfits.
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