Applications of Proteomics in Medicine

Proteomics is a rapid and sensitive method that offers extensive proteome coverage.

FREMONT, CA: Proteomics is a novel technology utilized in medicine, including developing drugs and biomarkers. Proteomics can find and monitor biomarkers by studying the proteins in bodily fluids such as urine, serum, exhaled breath, and spinal fluid. In addition to facilitating medication development, proteomics can provide a detailed map of protein interactions linked with disease processes.

Biomarker research

A biomarker is a measurable indicator of a body's normal or abnormal biological state. In clinical settings, cancer biomarkers are used to assess the progression of cancer and its response to treatment. 2D-PAGE is utilized to identify biomarkers. In addition, it may compare the protein profiles of healthy and diseased cells, such as tumor tissues and bodily fluids. Based on their functions, cancer biomarkers are categorized into three categories: predictive, prognostic, and diagnostic. Predictive biomarkers can predict the therapeutic response. Activation and positivity of human epidermal growth factor receptor two can expect the response to trastuzumab in breast cancer, for example. In addition, mutation of the Kirsten rat sarcoma virus gene can predict epidermal growth factor receptor inhibitor resistance in colorectal cancer (e.g., cetuximab).

On the other hand, prognostic biomarkers can assist clinicians with clinical outcome predictions. For node-negative, tamoxifen-treated breast cancer, the 21-gene repetition mark predicts relapse and entire survival. The third category of biomarkers is diagnostic biomarkers, which show whether a patient has a particular disease. For instance, a stool DNA test is utilized in colorectal cancer as a diagnostic biomarker. Tissues, serum, blood, and urine all contain these indicators. Thus, the sample of bodily fluids for proteomics is less intrusive and inexpensive. Numerous disorders, including acquired immune deficiency syndrome, cardiovascular diseases, diabetes, cancer, and renal diseases, have advanced in the development of biomarkers. However, highly complex protein mixtures and various protein movements are obstacles to fluid sampling for proteomics. According to disease conditions, each sort of sample is utilized differently. In renal disease, for instance, the urine sample is used to evaluate urine proteins, which reflect changes in kidney function. 

Blood is used for biomarker discovery in other human disorders as well. There are some obstacles to employing plasma in biomarker identification, including the dynamic nature of proteins, patient variance, and the low abundance of biomarkers in plasma. These obstacles in biomarker discovery have not yet been resolved. Due to their clinical significance, most biomarker discovery research centers on cancer-related disorders. For example, numerous biomarkers are related to malignancies and can be utilized to monitor patients.

Oncology

Oncoproteomics is the application of proteomics to cancer research. Oncoproteomics can be utilized to find anticancer medicines and to individualize cancer treatment. Proteins in cancer can be classified via microarrays and laser capture microdissection (LCM) of tumor tissue. There are numerous applications of oncoproteomics in numerous tissues, including the colon, the breast, the rectum, the prostate, and the brain. In addition, proteomics can be used for cancer diagnosis and discovering novel therapies. Numerous proteomics approaches, such as aptamer-based molecular probes, cancer immunomics, tissue microarrays, nano-proteomics (to isolate signatures of autoantibodies), and antibody microarrays, can be utilized to find biomarkers in cancer.

LCM and MS imaging (MSI) are two techniques that can be utilized in tumor proteomics. Before MS analysis, LCM can separate the target proteins from the tumor regions. By using the protein chip, this method can also identify proteins that correspond with tumor growth in the early and late phases of the disease. Due to the technical hurdles and limited throughput associated with tumor tissues, less research utilizes them than serum.

The second method employs MSI. This direct tissue approach permits the placement of a small amount of MALDI matrix mixed with a fresh tumor sample. This method can facilitate the 3D mapping of tiny compounds and proteins. This strategy involved mapping eight normal lung tissues with 42 lung cancers. In addition, MSI can forecast the diagnosis, classify lung cancer histology, and organize 85 percent of the nodal linkages.