Biomarkers in Cancer Detection, Diagnosis, and Prognosis

Abstract

Biomarkers are vital in healthcare as they provide valuable insights into disease diagnosis, prognosis, treatment response, and personalized medicine. They serve as objective indicators, enabling early detection and intervention, leading to improved patient outcomes and reduced costs. Biomarkers also guide treatment decisions by predicting disease outcomes and facilitating individualized treatment plans. They play a role in monitoring disease progression, adjusting treatments, and detecting early signs of recurrence. Furthermore, biomarkers enhance drug development and clinical trials by identifying suitable patients and accelerating the approval process. In this review paper, we described a variety of biomarkers applicable for cancer detection and diagnosis, such as imaging-based diagnosis (CT, SPECT, MRI, and PET), blood-based biomarkers (proteins, genes, mRNA, and peptides), cell imaging-based diagnosis (needle biopsy and CTC), tissue imaging-based diagnosis (IHC), and genetic-based biomarkers (RNAseq, scRNAseq, and spatial transcriptomics).

Keywords: CT needle biopsy; DNA; MRI; PET; RNA; RNAseq; biomarkers; lipids; proteomics.

Figure 6

(A) Genes and haplogroups are depicted on a plot of the mitochondrial genome. Genes are depicted within the circle, whereas haplogroups are depicted outside. The following acronyms are provided inside the circle for tumors where alterations in mitochondrial genome have been mentioned: Co, colon cancer; H&N, head and neck cancer; Pa, pancreatic cancer; Ov, ovarian cancer; Br, breast cancer; Th, thyroid cancer; Bl, bladder cancer [79]. (B) Graphical representations of Hsa circ 0013958 levels in seven lung adenocarcinoma (LAC) cell lines which were analyzed using PCR [109]. The normalization was done with respect to BEAS-2B cell line. Here, ** p < 0.01 and *** p < 0.001. (C) Schematic representation of the diagnostic performance of TSPAN1-positive extracellular vesicles in plasma. (a) The encapsulated anti-CD63 antibody was utilized to trap TSPAN1-positive small extracellular vesicles in plasma, and the anti-TSPAN1 antibody was applied to detect them. (b) In the plasma of healthy controls (HC, n = 30) and colon cancer patients (CC, n = 37) TSPAN1-positive small extracellular vesicles were detected. The Mann–Whitney test was performed to determine significance. **** p < 0.0001. (c) The ROC curves for distinguishing between healthy controls (HC) and colorectal cancer patients were evaluated. The TSPAN1’s AUC, specificity, and sensitivity are presented [111].

Figure 1

Difficulties related to the identification of tumors in their early stages. Due to their tiny size and the difficulties in transferring biomarkers from the tumor microenvironment to the bloodstream, early-stage cancers are challenging to detect. This is brought on by difficulties with biomarker transfer, dilution, and the kidneys’ quick degradation and filtration processes. Only a few tumor-associated biomarkers can be found in a typical blood sample of 5–10 mL, which is a small part of the overall blood volume.

Figure 2

The diagram shows the analytical sensitivity and detection times of various biosensing techniques.

Figure 3

(A) The TNM staging of a patient diagnosed with stage IV non-small-cell lung cancer (T2 N0 M1) revealed the following: (a) a T2 tumor in the lung detected through CT imaging, (b) no signs of involvement in local lymph nodes based on PET-CT scans, and (c) the presence of brain metastases as observed in MRI scans. (B) (a) An individual with cervical cancer (T3b N0 M0) initially came with a sizable main tumor (shown inside the circle). (b) Nevertheless, following chemoradiation therapy, the patient showed a full recovery, and the cervix was returned to normal (the location of the remnant tumor is shown with the arrow) [51].

Figure 4

Commonly used non-invasive techniques for examining biomarkers in solid tumors.

Figure 5

DNA from free cells and malignant cells in circulation. Circulating tumor cells (CTC) spread throughout the blood vessels after escaping from original locations and forming metastases in the distal organs. Dead cancer cells or expanding tumor cells release cell-free DNAs (cf-DNAs) into the bloodstream. RBC = red blood cell; WBC = white blood cell [88].

Figure 7

Identification of possible protein biomarkers for pancreatic cancer using bodily fluids. Bodily fluids that include cancer-derived proteins include bile, blood, pancreatic juice, urine, and pancreatic cyst fluid. For the management of pancreatic cancer patients, these proteins have a high potential as tumor biomarkers and a variety of clinical applications, including screening in high-risk populations for pancreatic cancer, early diagnosis, disease staging, the evaluation of tumor resection and prognosis, the prediction of therapy response to inform treatment decisions, and real-time patient monitoring [133].

Figure 8

Distribution of different immune topological characteristics among various cancer types [186]. (A–F) Six distinct types of immune cells are analyzed in distinct tumor types such as lung squamous carcinoma (LUSC), lung adenocarcinoma (LUAD), melanoma (MEL), bladder (BCLA), stomach adenocarcinoma (STAD), head and neck squamous carcinoma (HNSC), esophageal squamous carcinoma (ESCA), colorectal liver metastasis (COAD-MET), colorectal primary (COAD-PRI), and ovarian (OV) cancer. All n = 965 tissue samples from n = 177 subjects were included in this analysis.

Figure 9

CEBPA-AS1 activity in human gastric cancer (GC). (A) CEBPA-AS1 activity in 40 patients’ GC tissues relative to paired neighboring tissues. (B) CEBPA-AS1 expression in plasma exosomes of GC patients versus healthy subjects. ** p < 0.01 [329].

Figure 10

(A) Distinguishing PDAC patients (T = Tumor, red) from healthy controls (N = Normal, blue) and pancreatitis patients (Pan, green) via lipidomic profiling of human serum using various mass spectrometry methods. (a) Phase I involved analyzing 364 samples (262T + 102N) with ultrahigh-performance supercritical fluid chromatography/ mass spectrometry (UHPSFC/MS), shotgun MS (LR), and matrix-assisted laser desorption/ionization (MALDI-MS). These samples were divided into training (213T + 79N) and validation (49T + 23N) sets. (b) Phase II extended to 554 samples (444T + 98N + 12 Pan), divided into training (328T + 82N + 12 Pan) and validation (116T + 16N) sets. (c) In Phase III, 830 samples (546T + 262N + 22 Pan) were examined using UHPSFC/MS. These samples were split into training (430T + 246N + 22 Pan) and validation (116T + 16N) sets. LR = low resolution; HR = high resolution; RP = reversed-phase. (B) Representative box plots showing the concentration of lipids normalized using NIST documentation measured in patients with pancreatic ductal adenocarcinoma (PDAC) (443T) and control participants (95N) among both males and females: (a) sphingomyelins (SM) 41:1, (b) lysophosphatidylethanolamine (LPC) 18:2, and (c) ceramides (Cer) 41:1 [349].

Figure 11

(A) A visual representation of the numerous imaging methods that can be used to diagnose breast cancer. (B) New targeted medicines approved by the FDA for the treatment of molecular subtypes of breast cancer [383].

Figure 12

Liquid biopsy analysis [413]. (A) Liquid biopsy analysis involves the examination of circulating cancer cells, circulating tumor DNA (ctDNA), and extracellular vesicles containing proteins, RNA, ctDNA, and cell-free DNA (cfDNA) from both primary and secondary tumor sites. This approach is considered a potential cancer biomarker, enabling the quantification of ctDNA levels and the detection of (epi)genetic alterations. (B) Methods employed for ctDNA analysis encompass real-time PCR, BEAMing (beads, emulsion, amplification, and magnetics), coamplification at lower denaturation temperature PCR (COLD-PCR), digital PCR, and next-generation sequencing.