J Gastric Cancer.  2019 Sep;19(3):254-277. 10.5230/jgc.2019.19.e29.

Biomarkers for Evaluating the Inflammation Status in Patients with Cancer

• Guner A
• Kim HI

• KMID: 2458827
• DOI: http://doi.org/10.5230/jgc.2019.19.e29

Abstract

Inflammation can be a causative factor for carcinogenesis or can result from a consequence of cancer progression. Moreover, cancer therapeutic interventions can also induce an inflammatory response. Various inflammatory parameters are used to assess the inflammatory status during cancer treatment. It is important to select the most optimal biomarker among these parameters. Additionally, suitable biomarkers must be examined if there are no known parameters. We briefly reviewed the published literature for the use of inflammatory parameters in the treatment of patients with cancer. Most studies on inflammation evaluated the correlation between host characteristics, effect of interventions, and clinical outcomes. Additionally, the levels of C-reactive protein, albumin, lymphocytes, and platelets were the most commonly used laboratory parameters, either independently or in combination with other laboratory parameters and clinical characteristics. Furthermore, the immune parameters are classically examined using flow cytometry, immunohistochemical staining, and enzyme-linked immunosorbent assay techniques. However, gene expression profiling can aid in assessing the overall peri-interventional immune status. The checklists of guidelines, such as STAndards for Reporting of Diagnostic accuracy and REporting recommendations for tumor MARKer prognostic studies should be considered when designing studies to investigate the inflammatory parameters. Finally, the data should be interpreted after adjusting for clinically important variables, such as age and cancer stage.

Keyword

Inflammation; Cancer; Biomarkers; Immune system; Outcome assessment

MeSH Terms

Biomarkers*
C-Reactive Protein
Carcinogenesis
Checklist
Enzyme-Linked Immunosorbent Assay
Flow Cytometry
Gene Expression Profiling
Humans
Immune System
Inflammation*
Lymphocytes
Biomarkers
C-Reactive Protein

Figure

• 

  Fig. 1 Relationship among parameters. Parameters with continuous values (Green boxes) and with categorical values (Orange boxes). Detail of values are presented in Appendix 1. BMI = body mass index; MUST = malnutrition universal screening tool; NRS = nutritional risk screening; NRI = nutritional risk index; AST = aspartate transaminase; CRP = C-reactive protein; GPS = Glasgow prognostic score; CAR = C-reactive protein-to-albumin ratio; CONUT = controlling nutritional status; PI = prognostic index; PNI = prognostic nutritional index; WBC = white blood cell; APRI = aspartate aminotransferase/platelet count ratio index; NLR = neutrophil-to-lymphocyte ratio; NMLR = neutrophil-monocyte-to-lymphocyte ratio; LMR = lymphocyte-to-monocyte ratio; SII = systemic immune-inflammation index; GLR = granulocyte-to-lymphocyte ratio; PLR = platelet-to-lymphocyte ratio; COP-NLR = combination of platelet count and neutrophil-to-lymphocyte ratio.

• 

  Fig. 2 Relationship of albumin level with age according to stage. Each point represents age and albumin level of a patient. Linear regression of each stage group shows that mean albumin level at same age decrease as the disease progresses. Dataset from Lee et al. [115].

• 

  Fig. 3 Associations of interventions with response patterns INF = interferon; IL = interleukin; TNF = tumor necrosis factor; NK = natural killer; HLA-DR = human leukocyte antigen-DR isotype; ERAS = enhanced recovery after surgery; Th = T helper; DC = dendritic cell; CIK = cytokine-induced killer; GM-CSF = granulocyte-macrophage colony-stimulating factor.

• 

  Fig. 4 Unsupervised clustering of inferred immune fraction using leukocyte signature matrix in the cancer genome atlas stomach adenocarcinoma (TCGA-STAD) transcriptomic dataset (n=450). Integrative transcriptomic analysis enables the identification of distinct immune landscapes associated clinical phenotypes using the fraction of infiltrated immune subsets in each sample as well as the evaluation of oncogenic pathway specific to each sample.

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