The role of HGF, CEA, IL-8, and prolactin in progression of colorectal cancer, their diagnostic potential, and as therapeutic targets

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INTRODUCTION

Cancer-related deaths are declining, most likely due to earlier detection and more advanced treatments [1]. Anti-cancer medications often have a variety of unfavorable side effects that reduce quality of life in general. These include immunological dysfunction [2, 3] and chronic, non-resolving inflammation [4, 5]. Persistent inflammation and compromised cellular immunity are significant problems because they both promote a pro-tumor milieu that may hasten the disease's progression [6]. Finding ways to improve immune response performance while lowering inflammation is still essential for cancer survival.

All malignancies, including colorectal cancer, are systemic diseases that change the general makeup and function of the immune system. Immunotherapy has revolutionized cancer treatment, but its efficacy is still restricted in most clinical settings [7]. Although this method is less popular, immune cell count and function offer conclusive evidence of immunity in cancer survivors [13]. On the other hand, circulating biomarkers are often investigated since they can be conveniently collected during clinic visits [8]. Generally speaking, cytokines can either increase or decrease inflammation, while some have two functions [9]. Indicators are often aggregated and analyzed collectively to provide insight into the body's overall inflammatory state, even if classifying biomarkers in a binary form is oversimplifying [9].

Chronic inflammation promotes the progression and spread of tumors [10]. Cancer and inflammation appear to be linked, and cytokines often regulate the growth, differentiation, and signaling of these malignancies [10, 11]. The subtype, diagnosis, and prognosis of the tumor may be revealed by the expression of biomarkers. To put it another way, different cancer types have different rates of biomarker expression [12]. Cytokines mediate important interactions between immune and non-immune cells in tumor microenvironment (TME) [13]. These cytokines may stop tumors from growing, but they may also cause chronic inflammation. They may be used as biomarkers to detect cancers, forecast the progression of illnesses, and direct treatment choices because of their presence in the blood. It has actually been suggested that studying circulatory cytokines in conjunction with cancer-specific biomarkers can improve and speed up cancer diagnosis and prediction, especially with blood samples that require little to no invasion [14, 15]. Furthermore, circulatory immune cells' cytokine signaling pathways are changed in cancer, even in individuals with little tumors, and they may be useful cancer biomarkers [13, 14]. Although the potential therapeutic benefit of lymphocyte infiltration at TME has been evaluated in CRC, the roles of circulating cytokines remain poorly understood.

Tumor-associated proteins that have been identified as clinically significant have been identified as tumor markers in cancer patients [14]. 

While a number of tumor markers are routinely employed, they often fail to provide correct information for diagnosis. Serum cytokines show promise as indicators of treatment response, prognosis, and tumor stage [15]. While a number of tumor markers are commonly employed, they often fail to provide precise information for diagnosis or cancer progression. Serum cytokines show potential as markers of therapy response, prognosis, and tumor stage [15]. The aim of this study is to evaluate the role of HGF, CEA, IL-8, and prolactin in progression of colorectal cancer, their diagnostic potential, and as therapeutic targets.

MATERIALS AND METHODS

 

Study Area

The cross-sectional study recruited CRC patients at the University Clinical Hospital No. 1 of I.M. Sechenov First Moscow State Medical University, situated at Bolshaya Pirogovskaya Street, Building 6, Building 1, Moscow, under the Ministry of Health of the Russian Federation (Figure 1). The analysis of patient biosamples was conducted at the Institute of Personalized Oncology, Biomedical Science and Technology Park, Sechenov First Moscow State Medical University, Ministry of Health of the Russian Federation (Sechenov University).

Figure 1. Map of study area
Рисунок 1. Карта исследуемой территории

 

Sample Size Determination

The sample size was calculated using the formula of Naing et al. [16].

Sample size N = Z2 P (1-P)

                                     D2

Using a prevalence rate of 10% according to WHO Fact sheet. [17]

Z = Statistics for the level of 95% confidence interval (1.96)

P  =  Prevalence

D  = Desired degree of accuracy; here taken to be 0.05 Sample size, 

N  =    Z2 x P (1-P)

                    D2

N  =    3.842 x 0.100 (1-0.100)

                 0.05 x 0.05

N =     3.842 x 0.100 (0.900)

                       0.0025

 N = 138.312

Thus, to raise the power of the sample size, a total of 191 subjects were recruited into the study.

Subjects 

The study involved 191 participants. Ninety-one (91) colorectal cancer patients, who received medical treatment at University Clinical Hospital No. 1 of the I.M. Sechenov First Moscow State Medical University of the Ministry of Health of the Russian Federation (Sechenov University) from December 2024 to March 2025, were chosen at random, along with 100 healthy control subjects who underwent physical examinations during this time. The study was authorized by Sechenov University's Institutional Ethics Board. To participate in the study, each subject provided written informed consent. Two skilled pathologists at our institute determined the postoperative pathological results, and all surgical procedures were carried out by highly skilled surgeons. The stages of CRC were identified as I, II, III, and IV. The tumor site and type, as well as the patient's clinical and demographic details, were noted. The Union for International Cancer Control (UICC) TNM staging approach was used for tumor staging; tumor localization, UICC stage, and tumor grade were categorized as previously reported by Sobin, and Compton [18].

Criteria for inclusion 

(1): Colorectal cancer with histological confirmation

(2) No prior history of biologic-targeted treatment, chemotherapy, or radiation;
(3) One week before to surgery, serum cytokine levels were assessed.
Criteria for exclusion

(1) Individuals with a history of various forms of cancer
(2) Patients with metastatic colorectal cancer, those who are nursing or pregnant, and those who suffer from psychiatric disorders. 

Sample collection 

Using a normal venipuncture technique, fasting blood samples were obtained from 37 males and 54 females with colorectal cancer as well as from 48 males and 52 females in the healthy control group. After allowing the blood to coagulate for half an hour at room temperature, the serum was separated by centrifugation at 1000xg for a duration of ten minutes. The serum was then kept at -20°C until analysis. Before analysis, more than two freeze/thaw cycles were avoided.

Reagents 

Assay Buffer; 10X Wash Buffer; Human Circulating Cancer Biomarker Panel 1 Detection Antibodies; Human Circulating Cancer Biomarker Panel 1 Standard; Human Circulating Cancer Biomarker Panel 1 Quality Controls 1 and 2; Serum Matrix, 96-Well Plate with two Sealers; Streptavidin-Phycoerythrin; and Bead Diluent; were used for the sample analysis and were stored at 2 – 8°C.

 

Cytokine profiling 

The Luminex xMAP 200 platform (Luminex Corporation, Austin, TX, USA) was used for the measurement and analysis of a total of 24 molecules, including interleukins, chemokines, growth factors, interferon (IFN), and tumor necrosis factor (TNF). 

Before being utilized in the test, all of the reagents were allowed to be brought to room temperature (20–25°C). To put it simply, to each well in a 96-well plate was added 200 µL of assay buffer. The plate was sealed and shaken on a plate-shaker for 10 minutes at room temperature (20–25°C). By turning the plate over and gently tapping it several times into absorbent cloths, the assay buffer was decanted and the remaining amount was removed from each well. Background wells were filled with 25 µL of assay buffer. Each Standard or Control was introduced to the corresponding wells at a volume of 25 µL. Serum matrix (25 µL) was added to the Background, Standard, and Control wells. Assay buffer (25 µL) was added to each sample well. 

The corresponding wells were filled with 25 µL of a 1:6 diluted serum sample. The serum sample was diluted using the Serum Matrix included in the kit. Each well received 25 µL of the Mixed Beads after the mixing bottle was vortexed. The bead bottle was periodically shook to prevent settling while the beads were added. The plate was covered with foil, sealed with a plate sealer, and incubated at 4°C for 16–18 hours on a plate shaker. After carefully removing the contents of the well, the plate was cleaned three times in accordance with the kit's instructions. Each well received a volume of 25 µL of Detection Antibodies. The plate was sealed, wrapped in foil, and shaken on a plate shaker for an hour at room temperature (20–25°C). Each well containing 25 µL of Detection Antibodies was filled with 25 µL of Streptavidin-Phycoerythrin. The plate was sealed, covered with foil, and shaken at room temperature (20–25°C) for half an hour using a plate shaker.

Following the kit's instructions, the well's contents were carefully removed and the plate was cleaned three times. To each well was added a volume of 100 µL of Drive Fluid. For five minutes, the beads were suspended on a plate shaker. The plate was operated using xPONENT software on MAGPIX®. Analyte concentrations in samples were determined by saving and analyzing the Median Fluorescent Intensity (MFI) data using a 5-parameter logistic approach. The samples were analysed twice.

Statistical analysis

IBM SPSS version 27.0 was used to analyze all of the data. Descriptive analysis or frequencies were used to describe the features of the patients, and qualitative data reported as n (%). The mean and standard deviation (SD) for each cytokine are reported for all group staging, while the statistical comparison was done using Mann-Whitney U test and Kruskal-Wallis H test. Diagnostic potential of the markers for CRC was examined using receiver operating characteristic (ROC) curve. The maximal index served as the cut-off value, and the area under the curve (AUC) was employed as a metric to forecast diagnostic efficacy. At p < 0.05, differences were considered statistically significant. 

RESULTS

The study recruited 91 patients with colorectal cancer (CRC) [37 males (40.7%) and 54 women (59.3%); mean age, 68.3 years; range, 37 - 79 years]. A histological analysis revealed a similar distribution of CRC stages (I: n = 8 (8.8%) and II: n = 24 (26.4%), III: n = 52 (57.1%), and IV: n = 7 (7.7%). Table 1 summarizes the clinicopathological data and patient characteristics. 

Table 1: Clinical and demographic characteristics of colorectal cancer patients.

Таблица 1: Клинико-демографические характеристики пациентов с колоректальным раком.

Variable	CRC patients	Healthy control subjects
Sample n	91	100
Age (Mean±SD)	68.3±8.97	57.9± 5.82
Gender n (%)
Male	37 (40.7 %)	48 (48 %)
Female	54 (59.3%)	52 (52 %)
Stage n (%)
I	8 (8.8 %)	-
II	24 (26.4 %)	-
III	52 (57.1 %)	-
IV	7 (7.7 %)	-

By comparing cytokine serum concentrations in CRC patients to the control subjects, the heatmap in Figure 2 demonstrates immune system changes associated with CRC. CRC patients showed higher levels of pro-tumorigenic and inflammatory mediators such as CEA, HGF, IL-8, TNF-α, VEGF, SCF, and prolactin compared to healthy control subjects. However, markers such as leptin appear lower in colorectal cancer patients. 

Table 2 presents the results of a statistical comparison of the serum cytokine profiles of CRC patients and control subjects. The mean and standard deviation (SD) for each cytokine are shown for both groups, along with statistical data (p-value and Mann-Whitney U test). The indicator, CEA,  show significant higher levels in CRC patients as compared with health control subjects except Leptin which decreased significantly(p< 0.05), suggesting their potential as diagnostic biomarkers. 

Figure 2. A heatmap showing the serum levels of cytokines in CRC patients and healthy controls. The heatmap shows the levels of cytokines in the blood of CRC patients and healthy controls (HC). Rows show individual biomarkers, and columns show subjects. The color intensity shows how much of each cytokine is present. Different cytokine levels in CRC patients and control subjects are shown by different clustering patterns, which suggest a systemic cytokine signature linked to CRC. The clustering suggests that colorectal cancer has a systemic inflammatory environment that is full of growth factors. This condition is linked to angiogenesis, immune system control, and tumor growth.

Рисунок 2. Тепловая карта, показывающая уровни цитокинов в сыворотке крови у пациентов с колоректальным раком (КРР) и в контрольной группе. Тепловая карта показывает уровни цитокинов в крови пациентов с КРР и у здоровых лиц (контрольная группа). Строки показывают отдельные биомаркеры, а столбцы — испытуемых. Интенсивность цвета показывает количество каждого цитокина. Различные уровни цитокинов у пациентов с КРР и контрольных лиц показаны различными паттернами кластеризации, что предполагает системную цитокиновую сигнатуру, связанную с КРР. Кластеризация предполагает, что колоректальный рак имеет системную воспалительную среду, насыщенную факторами роста. Это состояние связано с ангиогенезом, контролем иммунной системы и ростом опухоли.

Table 3 shows the results of the statistical comparison of serum cytokine profiles among the different stages of colorectal cancer (I, II, III, and IV). For each cytokine, the mean and standard deviation (SD) are given for all group stages, along with statistical information (Kruskal-Wallis H test and p-value). The results show that the levels of the biomarkers change as the disease gets worse. For example, CEA levels go up a lot as the cancer stage goes up. This helps figure out which cytokine is most closely related to the growth of colorectal cancer.

 

Table 2. Comparison of serum cytokine profiles among colorectal cancer patients and healthy controls.

Таблица 2. Сравнение профилей сывороточных цитокинов у пациентов с колоректальным раком и контрольной группы.

Variable	CRC patients Mean ± SD N=91	Healthy control Mean ± SD N=100	U test	p-value
AFP	915.2±571.9	833.9±543.4	4132.0	0.273
Total PSA	65.8±84.2	45.6±65.5	4174.0	0.320
CA15-3	3..9±8.7	2.4±1.4	4410.5	0.304
CA19-9	46.4±56.8	33.3±31.6	4101.5	0.240
MIF	19.4±4.7	17.8±1.8	4216.0	0.381
TRAIL	35.2±16.2	30.4±13.2	3863.5	0.072
Leptin	3765.7±4709.0	6244.4±6246.8	3341.5	0.002*
IL-6	0.79±0.82	0.64±0.91	1851.5	0.330
SFasL	9.4±8.6	6.8±6.6	2819.5	0.024*
CEA	361.2±813.6	52.0±49.2	2100.5	<0.01*
CA125	1.16±0.07	0.96±0.39	3835.5	0.061
IL-8	9.6±5.2	5.9±5.8	2183.5	<0.01*
HGF	307.7±251.1	139.3±57.6	1756.0	<0.01*
sFas	604.9±664.3	552.1±600.2	3797.0	0.062
TNFa	2.2±1.1	1.7±0.7	3195.0	0.002*
Prolactin	3461±3296.8	2503.3±1835.7	3400.0	0.004*
SCF	25.2±9.3	21.4±4.8	3285.5	0.001*
CYFRA 21-1	2090.8±1379.6	1632.2±848.2	3631.5	0.021*
OPN	7805.2±7926.4	6475.3±7436.5	3903.5	0.090
FGF-2	126.0±54.4	116.9±67.9	3852.5	0.075
bHCG	0.26±0.09	0.24±0.04	3984.0	0.108
HE4	1544.1±842.3	1249.7±580.3	3345.5	0.002*
TGFa	11.7±9.3	8.6±4.1	3615.0	0.014*
VEGF	106.7±117.7	72.7±133.4	3309.5	0.003*

Table 3. Comparison of serum cytokine profiles among colorectal cancer group staging.
Таблица 3. Сравнение профилей сывороточных цитокинов в зависимости от стадии колоректального рака.

Variable	Stage I Mean ± SD N=8	Stage II Mean ± SD N=24	Stage III Mean ± SD N=52	Stage IV Mean ± SD N=7	H test	p-value
AFP	871.1±402.2	882.9±657.1	938.3±577.7	533.9±50.3	6.112	0.106
Total PSA	90.5±47.7	34.0±65.9	73.2±83.9	90.9±111.9	5.535	0.137
CA15-3	1.8±1.4	2.5±2.2	5.2±11.2	1.2±0.6	2.726	0.431
CA19-9	33.0±27.5	49.6±79.6	48.5±50.9	35.2±28.3	0.690	0.876
MIF	19.6±5.0	19.4±4.8	19.8±4.9	16.2±0.6	3.183	0.042*
TRAIL	42.7±19.3	34.4±15.9	35.6±16.2	25.9±11.4	4.328	0.223
Leptin	3551.7±3104.4	4728.4±6520.1	3680.4±4752.4	1362.7±1086.9	3.922	0.270
IL-6	0.23±0.56	0.72±0.64	0.84±0.98	0.81±0.56	0.544	0.904
SFasL	9.1±11.1	10.2±9.2	9.2±8.7	8.2±4.5	1.111	0.326
CEA	34.2±14.8	156.1±191.5	438.6±929.4	665.3±982.3	30.937	<0.01*
CA125	1.1±0.5	1.1±0.6	1.23±0.13	1.2±0.4	1.421	0.701
IL-8	5.8±3.3	10.6±2.4	9.7±5.2	8.9±3.6	5.040	0.169
HGF	438.3±418.6	377.6±360.9	255.1±140.4	309.2±122.6	1.609	0.157
SFas	1266.4±2009.3	526.6±249.7	550.3±340.6	523.4±206.9	1.454	0.193
TNFa	1.9±0.7	2.3±1.5	2.2±1.0	2.0±0.9	0.560	0.905
Prolactin	2172±1151.9	3971.7±3394.0	3523.1±3639.0	3731.1±923.2	2.607	0.456
SCF	29.0±16.7	25.3±12.2	35.0±6.6	22.1±2.9	1.863	0.601
CYFRA 21-1	2041.2±1415.9	2096±0.6	2038.5±1353.9	2574.9±3032.3	0.437	0.932
OPN	5637.1±2673.8	4369.9±2406.2	8036.7±8884.8	10055.3±6278.2	2.611	0.456
FGF-2	131.1±60.3	119.7±34.9	129.3±30.1	117.4±27.3	3.690	0.297
Bhcg	0.28±0.15	0.25±0.08	0.27±0.09	0.23±0.05	2.626	0.453
HE4	1860.9±6.6	1511.0±711.1	1549.5±667.5	1255.9±493.2	1.632	0.652
TGFa	11.7±6.6	11.1±12.7	12.4±8.5	8.3±2.6	3.400	0.334
VEGF	93.1±43.4	94.3±86.6	120.1±141.3	59.8±38.4	1.305	0.728

 

Figure 3A shows the HGF (Hepatocyte Growth Factor) profile at different points in the progression of colorectal cancer. Figure 3B shows a breakdown of the staging image of colorectal cancer. All stages of cancer have higher levels of HGF in the blood, but Stage I and Stage II have the highest average levels. These levels slowly drop in later stages. Even though HGF levels are still higher than in healthy controls, the changes between stages are not statistically significant (P>0.05). When staging is broken down into smaller groups, HGF doesn't go up steadily with each stage; instead, it stays high across all subgroups. This shows that HGF alone doesn't work well for stage-dependent discrimination.

Figure 3. HGF profile of patients with colorectal cancer showing cancer staging.  Serum HGF levels in stages I–IV of colorectal cancer. Higher HGF levels are seen at all stages of the disease without any clear stage-dependent pattern, suggesting early and continuous HGF participation in colorectal carcinogenesis. 

Рисунок 3. Профиль HGF у пациентов с колоректальным раком с указанием стадий заболевания. Уровни HGF в сыворотке крови на стадиях I–IV колоректального рака. Более высокие уровни HGF наблюдаются на всех стадиях заболевания без какой-либо четкой зависимости от стадии, что указывает на раннее и непрерывное участие HGF в колоректальном канцерогенезе.

Figure 4 shows the levels of carcinoembryonic antigen (CEA) in the blood at different stages of colorectal cancer. The findings indicate that CEA levels rise as CRC advances. Patients with stage III and IV cancer showed significantly higher CEA levels than controls and patients with early-stage cancer. The data analysis revealed a substantial correlation between CEA levels and cancer stage. A consistent rise in CEA levels indicates its effectiveness in monitoring disease progression. 

Figure 4. CEA profile of patients with colorectal cancer showing cancer staging.  Serum CEA levels in stages I–IV of colorectal cancer. Higher CEA levels are seen with progression of the disease. With its highest concentration at the later stages of the disease; this shows its strong association with colorectal cancer burden and progression. 

Рисунок 4. Профиль CEA у пациентов с колоректальным раком с указанием стадий заболевания. Уровни CEA в сыворотке крови на стадиях I–IV колоректального рака. Более высокие уровни CEA наблюдаются по мере прогрессирования заболевания. Наибольшая концентрация CEA отмечается на поздних стадиях заболевания, что свидетельствует о его сильной связи с тяжестью и прогрессированием колоректального рака.

 

Figure 5 shows how the levels of IL-8 in the blood change at different stages of CRC. The results show that CEA levels went up at all stages of CRC, but this rise was not statistically significant at any one stage. High levels of IL-8 are a sign of inflammation and angiogenesis in tumors, but they may not be very useful for accurately staging cancer. The results of the current study support these findings.

Figure 5. IL-8 profile of patients with colorectal cancer showing cancer staging.  Serum IL-8 levels in stages II and III of colorectal cancer. Higher IL-8 levels are seen with progression of the disease. IL-8 levels were elevated across the various stages of the disease, however, it did not show a stage-dependent statistical significance.

Рисунок 5. Профиль ИЛ-8 у пациентов с колоректальным раком с указанием стадий заболевания. Уровни ИЛ-8 в сыворотке крови на II и III стадиях колоректального рака. Более высокие уровни ИЛ-8 наблюдаются по мере прогрессирования заболевания. Уровни ИЛ-8 были повышены на разных стадиях заболевания, однако статистически значимой зависимости от стадии не выявлено.

 

Figure 6 shows the levels of prolactin in the blood at different stages of colorectal cancer. The findings indicate that prolactin levels rise as colorectal cancer advances. Individuals with stage III and IV cancer demonstrated significantly increased prolactin levels relative to healthy controls and patients with early-stage cancer. The data analysis revealed a significant association between prolactin levels and cancer stage. A consistent rise in prolactin levels may indicate an auxiliary biomarker role, illustrating endocrine-immune interactions in the advancement of colorectal cancer.

Figure 6. Prolactin profile of patients with colorectal cancer showing subdivided cancer staging. Higher prolactin levels are seen with progression of the disease. With its highest concentration at the later stages of the disease; this shows its strong association with colorectal cancer burden and progression

Рисунок 6. Профиль пролактина у пациентов с колоректальным раком, демонстрирующий подразделение стадий заболевания. Более высокие уровни пролактина наблюдаются по мере прогрессирования заболевания. Наибольшая концентрация пролактина отмечается на поздних стадиях заболевания, что свидетельствует о его сильной связи с тяжестью колоректального рака и его прогрессированием.

 

Figure 7 illustrates the Receiver Operating Characteristics (ROC) curve for the cytokines. It focuses on HGF, CEA, IL-8, and prolactin, showing how these marker strongly identify colorectal cancer patients from the healthy control subjects.

 

Figure 7. ROC curve. Serum HGF, CEA, IL-8, and prolactin curve. The findings show that overall discrimination is enhanced by combined serum cytokine profiling. The most accurate diagnostic marker is HGF, which is closely followed by CEA and IL-8.

Рисунок 7. ROC-кривые. Кривая для сывороточных HGF, CEA, IL-8 и пролактина. Результаты показывают, что общая дифференциация улучшается при комбинированном профилировании сывороточных цитокинов. Наиболее точным диагностическим маркером является HGF, за которым следуют CEA и IL-8.

 

Table 4 presents the Area Under Curve (AUC) for the individual cytokines in identifying colorectal cancer patients from healthy control subjects.  Markers with higher AUC values such as HGF (0.780), CEA (0.733), IL-8 (0.729) and prolactin (0.708), are preferable diagnostic biomarker. The AUC helps to identify which markers are most useful for screening or monitoring colorectal cancer. It provides insight into the immunological and hormonal changes linked to colorectal cancer, strongly supporting the use of these biomarkers for accurate diagnosis and staging. 

Table 4. Area under the curve for serum cytokine profiles in colorectal cancer patients.

Таблица 4. Площадь под кривой для профилей сывороточных цитокинов у пациентов с колоректальным раком.

Test result variable(s)	Area
CEA	0.733
IL-8	0.729
HGF	0.780
Prolactin	0.708

 

DISCUSSION

 

The need for earlier detection and more sophisticated treatments is highlighted by the heterogeneous character of colorectal cancer (CRC). However, anti-cancer treatments typically include a number of unfavorable side effects, such as extended, persistent inflammation and diminished immunological function, which result in a general deterioration in quality of life [15]. The primary goals of cancer research continue to be advancements in standardized comprehensive therapy and early detection of a range of aggressive malignancies [15, 19]. Tumor-associated proteins that are clinically significant are known as tumor markers in cancer patients [14]. Even though a number of tumor markers are frequently employed, they don't always offer accurate diagnostic data. Serum cytokines show promise as indicators of tumor stage, prognosis, and treatment response. In fact, a number of cytokines have been suggested as possible biomarkers for a number of malignancies [15]. It has actually been suggested that studying circulatory cytokines in conjunction with cancer-specific biomarkers can improve and speed up cancer diagnosis and prediction, especially with blood samples that require little to no invasion. 

In order to identify prognostic and diagnostic markers, this study examined the serum cytokine profiles of 91 patients with colorectal cancer and 100 healthy controls. Utilizing ROC analysis, statistical tables, and heatmap to highlight important biomarkers. 

The heatmap in Figure 2 illustrates the differences in cytokine serum concentrations between patients with colorectal cancer and healthy controls; biomarkers are represented by rows, subjects are represented by columns, and relative levels are shown by color intensity. While pro-tumorigenic and inflammatory mediators such as HGF, IL-8, CEA, TNF-α, VEGF, SCF, and prolactin are elevated in patients with colorectal cancer, leptin appears to be decreased. A systemic cytokine signature in colorectal cancer (CRC) characterized by inflammation and growth-factor excess that promotes angiogenesis, immunological dysregulation, and tumor progression is shown by distinct clustering patterns. According to current colorectal cancer immunology, tumor microenvironments create pro-inflammatory changes to elude host defenses and promote growth [20]. 

Table 1 gives Mann-Whitney U test comparisons of mean ± SD cytokine levels, demonstrating substantial rises in colorectal cancer for CEA, HGF, IL-8, leptin (though directionally lower), sFasL, TNF-α, prolactin, SCF, CYFRA 21-1, HE4, TGF-α, and VEGF. Non-significant markers like AFP, total PSA, and CA19-9 show poor diagnostic usefulness in this sample. These findings show a pro-tumorigenic environment in colorectal cancer, with HGF and IL-8 driving epithelial-mesenchymal transition and neutrophil recruitment, respectively, whereas CEA's rise represents glycoprotein shedding from neoplastic cells [21]. Prolactin's surge implicates endocrine-immune interaction, potentially via prolactin receptor activation on tumor cells, boosting proliferation.

Table 2 applies Kruskal-Wallis H tests across colorectal cancer stages, indicating CEA as highly stage-dependent. Other markers like HGF, IL-8, and prolactin indicate trends but lack relevance. Non-varying cytokines such as CA19-9 and emphasize uneven progression patterns. CEA's monotonic growth confirms its role in monitoring disease burden, correlating with tumor size, lymph node involvement, and metastasis, as per TNM staging guidelines [22]. Elevated early HGF implies its involvement in carcinogenesis beginning, but stage-independent inflammation (e.g., TNF-α) leads to chronic immune suppression throughout colorectal cancer progression.

Figure 3 demonstrate HGF levels across stages and subgroups, with peaks in stages I/II dropping non-significantly in III/IV (p>0.05), however all exceed controls. This trend demonstrates HGF's early carcinogenic involvement, boosting hepatocyte-like development in colorectal epithelia via c-MET receptor activation, fostering invasion without strict stage correlation. According to recent study, HGF-induced angiogenesis and tumor growth can be inhibited by an anti-HGF monoclonal antibody [23]. Subgroup analysis reinforces non-monotonic patterns, reducing HGF's solo predictive utility while showing its interaction with other markers in multi-biomarker panels. Literature supports HGF's autocrine/paracrine loops in colorectal cancer, where stromal fibroblasts enhance tumor signaling, contributing to chemoresistance [24]. 

The increasing elevation of CEA throughout the stages is seen in Figures 4, with notable elevations in III/IV compared to early stages and controls, indicating statistical significance. Since CEA, a member of the glycosylated immunoglobulin superfamily, is overexpressed in adenocarcinomas and aids in the identification of distant metastases, this tendency corresponds with the development of colorectal cancer. Subgroup increases in advanced illness highlight the importance of CEA surveillance after resection, as serial monitoring in high-burden people predicts recurrence with 70–90% sensitivity [25]. In contrast to HGF, CEA is a key component of clinical guidelines such as NCCN because of its stability and tumor-specific production.

Figure 5 indicate IL-8 elevations (peaks in II/III), moving upward but without stage significance. IL-8 (also called CXCL8) levels, can affect a range of immune and nonimmune cells, and reveal important details about tumors, such as their size and likelihood of responding to immunotherapy. This is due to the fact that tumor-produced IL-8 can stimulate angiogenesis and attract immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs) and neutrophils, and promote epithelial-to-mesenchymal conversions, which are a step before metastasis   [26]. As a CXC chemokine, IL-8 stimulates angiogenesis and immune evasion in colorectal cancer microenvironments by drawing endothelial cells and myeloid-derived suppressor cells [27]. Persistent levels throughout stages point to chronic inflammation as a feature of colorectal cancer; however, for greater specificity and diagnostic precision, CEA/HGF must be used. These findings are consistent with studies linking IL-8 polymorphisms to an increased incidence of colorectal cancer and poor prognoses [28].

Prolactin's stage-associated rise (I–IV) is shown in Figure 6, indicating its potential as a biomarker despite questionable staging significance. Beyond breastfeeding, prolactin functions as a cytokine via JAK2/STAT5, increasing the survival and spread of colorectal cancer cells via receptor-mediated PI3K/AKT activation [29]. Hormone-influenced colorectal cancer subtypes should be investigated since elevated late-stage levels indicate endocrine regulation of tumor growth.

Figure 7 and Table 3 show the ROC curves and AUC values that show how well the biomarkers worked as tests to find diseases. The AUC for HGF was the highest of all the biomarkers. Prolactin, IL-8, and CEA came next. It also performed better than markers such as MIF and leptin. These findings indicate that colorectal cancer encompasses a diverse array of cytokines, and that multi-marker panels are superior for its diagnosis compared to single analytes. HGF was very good at finding diseases early on because it was very good at diagnosing them. CEA, on the other hand, had a more even sensitivity and specificity profile, which means it might still be useful for watching diseases. The best cut-off settings for multiplex panels have a sensitivity and specificity of over 80%. This shows that liquid biopsy methods have improved a lot and that serum-based multiplex tests work better than single biomarkers. One of the main things that makes colorectal cancer inflamed is cytokine dysregulation. Panels containing HGF, CEA, IL-8, and prolactin exhibited elevated AUCs, thereby endorsing their application in high-risk populations. Using CEA's known role in staging diseases to help make decisions about adjuvant treatment, especially for drugs that target angiogenic pathways like bevacizumab, may help improve risk stratification.

STUDY LIMITATIONS 

This study has limitations despite its strengths. The study comprised a cohort size of 91 colorectal cancer patients, and stage imbalance, where stage III was prominent over stages I, II and IV, necessitating validation in diversified population., Future studies should incorporate genomes for personalized panels. 

CONCLUSION 

Chemokines and cytokines have pleiotropic effects, and different tumors can employ them in different ways which promote growth, metastasis, and survival. Pro- or anti-inflammatory processes are actively mediated by cytokines. Understanding the connections between inflammation and cancer is crucial, as is thoroughly evaluating the mediators of cancer-related cytokines that may be involved in early cancer detection. The current study has highlighted potential diagnostic indicators for CRC. HGF, IL-8, CEA, and prolactin are potential diagnostic markers linked to colorectal cancer progression and staging in patients who may benefit from serum HGF, IL-8, CEA, and prolactin monitoring and the therapeutic target of their genes.

About the authors

Ikenna Kingsley Uchendu

1. Кафедра медицинской лабораторной диагностики, факультет здравоохранения и технологий, медицинский колледж, Университет Нигерии, кампус в Энугу

2. ФГАОУ ВО Первый МГМУ им. И.М. Сеченова Минздрава России (Сеченовский Университет), Москва, Россия

Email: uchenduikenna1@gmail.com
ORCID iD: 0000-0002-1503-3759

Angelina Vladimirovna Zhilenkova

Sechenov First Moscow State Medical University

Author for correspondence.
Email: av.zhilenkova@gmail.com
ORCID iD: 0000-0002-0060-2197

младший научный сотрудник Института персонализированной онкологии Научно-технологического парка биомедицины

Russian Federation

Ekaterina Vadimovna Orlova

ФГАОУ ВО Первый МГМУ им. И.М. Сеченова Минздрава России (Сеченовский Университет), Москва, Россия

Email: orlovaderm@yandex.ru

Natalia Mihailovna Nikitina

ФГАОУ ВО Первый МГМУ им. И.М. Сеченова Минздрава России (Сеченовский Университет), Москва, Россия

Email: nikitina_n_m@staff.sechenov.ru
ORCID iD: 0000-0003-3311-3290
Russian Federation

Alexander Rozhkov

Email: visperianlol@gmail.com

Leonid Nikolaevich Bagmet

ФГАОУ ВО Первый МГМУ им. И.М. Сеченова Минздрава России (Сеченовский Университет), Москва, Россия

Email: Bagmetln@mail.ru
ORCID iD: 0000-0001-6990-0291

Marina Igorevna Sekacheva

ФГАОУ ВО Первый МГМУ им. И.М. Сеченова Минздрава России (Сеченовский Университет), Москва, Россия

Email: sekach_rab@mail.ru
ORCID iD: 0000-0003-0015-7094

References

Supplementary files