Evaluation and prediction of Ki-67 expression in hepatocellular carcinoma

Abstract

Hepatocellular carcinoma (HCC) is a primary malignancy of the liver with hepatocellular differentiation. The diagnosis and prognosis of HCC are significantly influenced by tumor markers. Ki-67 as an immunohistochemical marker is a nuclear antigen related to the level of cell proliferation activity. Ki-67 is an important prognostic markers of HCC. In order to help clinicians assess patient outcomes, Ki67 can function as an independent prognostic indicator. In HCC, elevated Ki67 expression is linked to a worse prognosis and a higher chance of recurrence. It is considered a significant marker of clinical, histopathological, and immunological status and prognosis in HCC. Preoperative evaluation and prediction of Ki-67 expression in HCC with machine learning models based on intratumoral and peritumoral radiomic features now have a significant role in patient management and care and will provide a more definite direction for subsequent research. This article aims to evaluate the role of Ki-67 and various other biomarkers in HCC.

Key Words: Tumor biomarkers; Radiomic; Hepatocellular carcinoma; Prognostic indicator; Ki67

Core Tip: The incidence and mortality of liver cancer continue to rise. The various screening methods for hepatocellular carcinoma (HCC) are radiomics, serum biomarkers, immunohistochemistry, genetic and molecular studies. Ki-67 protein in the nucleus is associated with cell proliferation, and indicates the aggressiveness of tumors. Evaluation and prediction of Ki-67 expression in HCC now have a significant role in the diagnosis, treatment, and prognosis of HCC. Elevated levels of Ki67 expression are associated with a poor prognosis and higher risk of HCC recurrence. Recently, radiomics, using machine learning-based ultrasomics, has demonstrated the capability to predict Ki-67 expression as a potential noninvasive tool for preoperative assessment in HCC.

TO THE EDITOR

The most prevalent type of primary liver cancer is hepatocellular carcinoma (HCC). It ranks as the third leading cause of cancer-related mortality globally[1]. The known etiological factors which are associated with the development of HCC are persistent alcoholism, chronic liver disease, persistent hepatoviral infection such as chronic viral hepatitis B, hepatitis C, or nonalcoholic fatty liver diseases. Approximately 80% of HCC cases arise in cirrhotic liver[2,3].

In HCC, tumor markers play a crucial role in the diagnosis, clinical course, prognosis, treatment and recurrence (Table 1). Recent research studies showed potential biomarkers for HCC from diverse sources such as serological components, genetic materials, cells, and vesicles. Promising serological diagnostic biomarkers for HCC are serum alpha-fetoprotein (AFP), AFP-L3, des-carboxy-prothrombin glypican 3, glutamine synthase, PIVKA II, etc.[4,5].

Table 1 Significance of various biomarkers in hepatocellular carcinoma.

Serial No.	Clinical phase of HCC	HCC biomarkers	Significance of biomarkers in HCC	Ref.
1	HCC occurrence, evaluation, progression, recurrence	AFP, AFPL3, Ki67 indexing	HCC early detection, progression, recurrence rates, overall survival disease-free survival, and relapse-free survival, response to adjuvant hepatic arterial chemoembolization	Schmilovitz-Weiss et al[4], Ramos-Santillan et al[5]
2	Predicting HCC occurrence, progression, evaluation	AFP, AFP-L3, DCP, GALAD, microRNAs, long non-coding RNAs	HCC early detection, risk stratification, monitoring progression of tumor, and predicting response to therapy	Schmilovitz-Weiss et al[4], Koike et al[6] Wang et al[7], Huang et al[13]
3	Predicting therapy efficacy	TGF-β, AFP levels, PD1 signaling	Predicting response to immunotherapy, monitoring treatment response, and identifying patients at risk of resistance	Huang et al[13]
4	Predicting metastasis	CTCs, circulating DNA, CK19	Identifying high-risk patients, guiding treatment decisions, predicting recurrence, monitoring treatment responses	Zhang et al[8]
5	Predicting post-surgical prognosis	Prealbumin, NLR, PLR, OV6, CD68, MVI	Predicting long-term outcomes, identifying patients at risk of recurrence, and guiding treatment decisions	Wang et al[7]
6	Diagnosis of HCC	Osteopontin	Survival, migration, invasion, and metastasis of tumor cells	Khattab et al[9]
7	Tumor prognosis, clinical stage	miR-221	Clinical TNM stage, tumor capsular infiltration and poor prognosis	Li et al[10]
8	HCC progression, recurrence, survival parameters	Ki-67 (MKi-67 gene)	Survival parameters, disease stage and progression, adverse histological criteria, and tumor microenvironment proliferative characteristics	Ramos-Santillan et al[5]
9	Diagnostic biomarker for HCC	Serum AFP, methylation of RASSF1A	Monitoring progression of tumor, drugs, targeted therapies, immunotherapies etc.	Shi et al[11], Jagtap et al[12], Huang et al[13]
10	Advance stages	Methylation, proteins, lncRNA, miRNA, metabolomics	Resistance, survival, drug, targeted therapies, immunotherapies	Shi et al[11], Tateishi et al[14]

AFP: Alpha-fetoprotein; DCP: Des-gamma carboxyprothrombin; GALAD Score: Gender, Age, AFP-L3, AFP and Des-carboxy-prothrombin, AFP-L3: Alpha-fetoprotein L3; TGF-β: Transforming growth factor-beta; PD1: Programmed death receptor 1; CTCs: Circulating tumor cells; CK19: Cytokeratin19; NLR: Neutrophil-to-lymphocyte ratio; PLR: Platelet-lymphocyte ratio; OPN: Osteopontin; lncRNAs: Long noncoding RNAs; miRNA: Micro ribonucleic acid.

Non-invasive methods such as liquid biopsy with circulating free tumor DNA in serum or plasma samples, microRNA, and extracellular vesicles are used to detect HCC[6,7]. Squamous cell carcinoma antigen, fibroblast growth factor 3/4, cytokeratin 19, osteopontin, midkine, Golgi protein 73, Annexin A2, and tissue biopsy are immunohistochemistry biomarkers[8,9]. In addition to being based on artificial intelligence and imaging data, genomic profiling, DNA methylation, multiomics, and radiomics research in HCC are innovative approaches[10-13]. AFP (> 20 ng/mL), is a common biomarker for the diagnosis and surveillance for HCC as the sensitivity of AFP is excellent. The diagnostic utility of AFP is relatively concerning in clinical practice due to the low sensitivity at higher cutoff values (200 ng/mL). The serum marker des-γ-carboxy prothrombin (DCP) did not offer substantial advantages compared with AFP .and is not used for early detection of HCC[6]. A prospective cohort study from North America observed that sensitivity and specificity greatly varied between AFP, AFP-L3%, and DCP but when used in combination, the sensitivity increased to 77%[14].

Ki-67 expression can be utilized as a predictive biomarker to reveal the biological activity of HCC as it is an independent prognostic factor for survival and recurrence that has achieved both clinical and statistical significance. One of the most extensively researched markers of tumor biology is Ki-67, commonly referred to as MKi67 and MIB1. Ki-67 is a common proliferative and prognostic factor in HCC[4].

The radiomics models and a clinical-radiomics nomogram fusion model study were found to be superior to the single-region models. Highly proliferative HCC can be identified by the expression of the Ki67 gene (MKi67), a more accurate and objective technique than immunohistochemistry[5]. Expression of the Ki67 gene aids in the identification of highly proliferative HCC and has been linked to the immune cell population in the tumor microenvironment, tumor development, and oncologic prognosis. Tumor size and histological grade were associated with MKi67 expression. The HCC carcinomatous sequence from normal liver, cirrhotic liver, early HCC, and advanced HCC showed an increase in MKi67 expression.

RECENT EVALUATION AND PREDICTION OF KI-67 EXPRESSION IN HEPATOCELLULAR CARCINOMA

HCC is the most prevalent primary liver cancer. In terms of global cancer incidence, HCC is ranked sixth.

HCC is an aggressive malignancy that commonly presents in advanced stages and therefore these patients have poor prognosis[15]. The prognosis of HCC is strongly correlated with variables such as tumor grade, microvascular invasion, and Ki-67 expression[16]. Following liver resection, the 5-year recurrence rate is between 50% and 70%[17]. In order to develop and improve HCC treatments and prognosis, it is crucial to identify prognostic variables.

Ki-67 is an antigen associated with cell proliferation, which is expressed during the G1, S, G2, and mitotic phases of the cell cycle. The Ki-67 proliferation index is frequently utilized as a prognostic marker in several malignancies. In HCC patients, elevated Ki-67 expression correlates with aggressive tumor traits and unfavorable prognoses. During routine analysis, tissue sections are immunohistochemically stained with a monoclonal mouse anti-human Ki-67 antibody, and Ki-67 expression is assessed[18]. Immunoreactive cells are identified based on their Ki-67 immunochemistry results, which are categorized as either low proliferation index Ki-67 expressing (≤ 10% immunoreactivity) or high proliferation index Ki-67 expressing (> 10% immunoreactivity)[19].

Recently, radiomics has emerged as a cutting-edge technique for identifying Ki-67 in HCC[20]. A computed tomography model is used to explore the utility of radiomics to predict Ki-67 expression in HCC. Radiomics, a new technology that has the capacity to change physiological and pathological data, has shown promise in the diagnosis, prognosis, and prediction of HCC as well as in clinical decision support, categorization, and staging. Radiomics and machine learning offer advantages such as increased speed, objectivity, and reproducibility compared to manual assessment, potentially leading to more consistent and reliable Ki-67 scoring.

Without requiring a tumor biopsy, radiomics has been demonstrated to be helpful in determining tumor pathology data in medical imaging, such as tumor grade, progression, and gene expression[21]. There are notable benefits to using a clinic-radiologic-radiomic model to predict Ki-67 expression in HCC prior to surgery. Peritumoral tissue radiomic analysis offers important insights into the development and spread of tumors. Ki-67 expression in malignancies can be predicted[22]. Additionally, it was believed that peritumoral tissue was linked to microvascular invasion (MVI) and invasiveness in HCC. The idea of radiomics and machine learning techniques at intratumoral and extratumoral regions of the tumor in clinical studies, provides important steps in the diagnosis and treatment of HCC.

Along with the advances in artificial intelligence and computing equipment, radiomics has a significant role in HCC. Radiomics uses numerous quantitative parameters that are converted from visual pictures to ascertain the biological properties of malignancies. It is anticipated that the temporal and spatial heterogeneity of lesions would be quantified in a non-invasive, thorough, and dynamic manner[23].

In HCC, a physiologically invasive phenotype is frequently implied by high Ki-67 expression. High HCC Ki-67 expression suggests that the cancer cells are showing excessive proliferation which is worse in relation to increased MVI and poor histological differentiation. According to a recent meta-analysis of 54 studies involving 4996 patients, Luo et al[15] found that a high Ki-67 Labeling index was linked to larger tumor size, higher histological grade, more lymph node metastases, cirrhosis, vascular invasion, and distant metastases in patients with HCC.

Despite advancements in surgical resection, HCC still has a high risk of metastasis and recurrence, which contributes to a poor prognosis. Patients with HCC may benefit from more suitable treatment options, such as targeted therapies alone or in conjunction with locoregional therapy, if Ki-67 can be predicted prior to surgery. A precise assessment of Ki-67 expression status aids in directing adjuvant treatment and surveillance plans for clinicians. As Ki-67 is rarely found in normal cells but is abundantly expressed in the majority of malignant cells, it is thought to represent a unique therapy target for HCC. Higher gene expression was found to be positively connected with survival parameters, disease stage and progression, adverse histological criteria, and tumor microenvironment proliferative characteristics, according to a study conducted by Ramos-Santillan et al[5] on the expression of the MKi-67 gene in 473 HCC samples. Recurrence rates, overall survival, disease-free survival, and relapse-free survival are all markedly worse for patients with HCC who show high levels of Ki-67[16]. Patients with HCC often have a poor prognosis, with an overall 5-year survival rate of approximately 18%[24].

Trans-arterial chemoembolization, liver transplantation, and surgical resection are the three methods used to treat HCC. Adjuvant hepatic arterial chemoembolization has been demonstrated to increase overall survival and reduce the chance of tumor recurrence following liver tumor excision in individuals with elevated Ki-67 expression[25].

Analysis of the advantages and disadvantages of evaluation and prediction of Ki-67 expression in HCC can be conducted to provide a more definite direction for subsequent research. The specific clinical applications of high Ki-67 expression suggest a more aggressive HCC, potentially influencing treatment choices like surgery, liver transplantation, or targeted therapies.

Ki-67 has a direct link to cell proliferation, and both established clinical use and relative simplicity make it a more useful and clinically relevant biomarker for predicting tumor aggressiveness and prognosis in HCC. The recent study by Qian et al[26] observed that, the ultrasound radiomics model that utilizes both intratumoral and peritumoral tissue information is better for accurate prediction of Ki-67 expression in HCC patients. The miRNAs are important regulators in HCC; however, Ki-67 expression is easily evaluated by immunohistochemistry. It is a relatively simple and widely available technique. While miRNA analysis is more sophisticated, it is more complex and requires specialized equipment and expertise. The use of miRNAs as biomarkers in cancer is still regarded as mostly confined to research programs but they have future promise as clinical biomarkers[27].

In future, the emerging role of miRNAs as novel biomarkers for HCC in clinical evaluation, risk prediction, early diagnosis and determining the appropriate therapeutic target, requires further research[28,29].

CONCLUSION

Early and accurate evaluation of HCC with the relevant histopathological features, Ki-67 expression, molecular and radiomics studies is crucial for refining treatment approaches and enhancing patient outcomes. New approach strategies incorporating preoperative evaluation and prediction of Ki-67 expression in HCC will assist better treatment and patient care. Also, it will help in decision-making regarding adjuvant therapy in HCC. The identification of readily measurable biomarkers for HCC treatment or early identification is necessary. Future studies should create practical HCC biomarkers for early diagnosis, therapy activity tracking, and treatment resistance detection.