Unraveling the influence of TGF-β rs9282871 and miRNA let-7c relative expression on TGF-β production in hepatocellular carcinoma patients.

1
Introduction
Hepatocellular carcinoma (HCC) poses a severe health problem and risk globally.1 It is ranked the sixth most common type of cancer in incidence and the third in mortality rate worldwide.2 HCC epidemiology is directly linked to the incidence of chronic liver illnesses, the most common of which are caused by hepatitis B virus (HBV) and hepatitis C virus (HCV) infections, alcohol consumption, and metabolic abnormalities. HCC prevalence varies significantly around the world; the highest rates were found in Asia, Europe, and Africa, respectively.3 In Asian and African countries, HCC is attributed directly to the endemicity of both HBV and HCV.4, 5 In Western countries, the incidence of HCC has been growing, owing to rising rates of alcohol abuse, addiction, and the presence of steatosis, besides hepatitis B and C infections.6, 7 Moreover, HCC can develop due to the presence of some metabolic disorders, including nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH), which create a precancerous liver environment through steatosis, oxidative stress, and fibrosis.8 Also, the presence of metabolic dysfunction, such as insulin resistance, type II diabetes, and obesity, is associated with NAFLD and NASH. Additionally, lipid dysregulation and bile acid metabolism disrupt hepatocyte homeostasis, thereby promoting carcinogenesis.9, 10, 11
In Egypt, HCC is ranked the first and highest type of cancer in incidence, about 18.6% and mortality, about 28.3% of total cancer types.12, 13, 14, 15 The prevalence of HCC in Egypt is attributed mainly to the endemic HCV infection that has lasted for decades, which leads to chronic liver fibrosis and cirrhosis. Recently, after the introduction of HCV treatment (sofosbuvir, ledipasvir, and daclatasvir), direct-acting antiviral drugs (DAAs). Several studies found an increased risk of HCC incidence after DAAs treatment, which contributes to the high incidence rates of HCC in Egypt.16, 17, 18 Proposed mechanisms include rapid viral clearance leading to impaired immune surveillance, altered cytokine and natural killer cell activity, and remodeling of the liver microenvironment that sustains carcinogenesis.19, 20 Although DAAs eradicate HCV, the epigenetic alterations induced during chronic infection often persist. These alterations, combined with host genetic variants such as TGF-β polymorphisms, may enhance profibrogenic signaling and persistent TGF-β overproduction, creating a tumor-promoting liver microenvironment.21, 22 miRNAs, particularly let-7c, act as epigenetic regulator, and its dysregulation disrupts the oncogenic networks and TGF-β pathways. Therefore, the interaction between TGF-β SNPs and altered let-7c expression may sustain oncogenic signaling, contributing to HCC risk even after successful HCV treatment.6, 23, 24, 25
Therefore, focusing on genetic/epigenetic factors and HCC progression is crucial for developing and enhancing genetic therapeutic targets for HCC patients.26, 27 This study aimed to explore the TGF-β production in HCC patients by investigating the influence of single-nucleotide polymorphisms in the TGF-β gene and miR-let-7c expression.
TGF-β is a cytokine that controls many cellular processes.28, 29 Dysregulation of the TGF-β signaling pathway affects cellular homeostasis and HCC progression and development.18 In the liver, TGF-β signaling participates in all the stages of disease progression from initial liver injury to hepatocellular carcinoma (HCC).13, 16 Several studies revealed that the role of TGF-β in liver cells begins with extracellular matrix formation, leading to liver fibrosis and then cirrhosis, which increases the risk of HCC development and progression.30, 31 TGF-β can alter the response of the cells comprising the tumor microenvironment, which may also contribute to HCC growth and induce immune evasion of cancer cells.32 TGF-β causes HCC cells to undergo phenotypic alterations associated with Epithelial-Mesenchymal Transition (EMT), which increases the tumor cells' invasive and migratory capabilities and gives them metastatic characteristics.33, 34, 35 On the other hand, several researchers focused on the role of epigenetic factors and cancer progression, like HCC.36
MiRNA expression has attracted attention recently due to its role in carcinogenesis.37, 38 These small 18–22 nucleotide molecules can play a dual role in cancer progression; some act as oncomiRNAs, while others act as tumor suppressor miRNAs.39, 40, 41 The role of any miRNA depends on the target genes and signaling pathways it targets.42, 43 Moreover, several studies have revealed that the expression of miRNAs is affected by ethnicity and race, as well as tumor type. miR-let-7c is differentially expressed in various types of cancer and different populations, where it can act as either a tumor suppressor or an oncomiRNA.44, 45, 46, 47
Once loaded into RISC, mature miRNAs exert their regulatory function through sequence-specific binding, primarily to complementary sites within the 3′ untranslated region (3′UTR) of target messenger RNAs (mRNAs). This interaction leads to translational repression and/or mRNA degradation, thereby fine-tuning gene expression at the post-transcriptional level. Beyond canonical biogenesis, accumulating evidence indicates that miRNA maturation and activity are subject to extensive post-transcriptional control, including RNA-binding proteins and context-dependent processing mechanisms. Moreover, chemical modifications of miRNAs, such as nucleotide editing and methylation, have emerged as critical modulators of miRNA stability, target selection, and silencing efficiency, with significant implications in cancer and other human diseases. Dysregulation at any stage of miRNA biogenesis or function can profoundly disrupt cellular homeostasis and contribute to oncogenic transformation and tumor progression.
In hepatocellular carcinoma, aberrant miRNA expression patterns reflect both genetic and epigenetic alterations within the tumor microenvironment, highlighting miRNAs as key regulators of oncogenic signaling pathways, including the TGF-β axis.48
Beyond their biological relevance, miRNAs have emerged as powerful molecular tools for disease diagnostics and therapeutic engineering. Advances in miRNA profiling technologies have enabled the development of RNA-based platforms that exploit endogenous miRNA signatures for highly specific disease detection and intervention.49 In particular, synthetic mRNA circuits and miRNA-responsive switches have been engineered to sense cell-type–specific miRNA expression patterns, allowing precise identification and separation of diseased cells from healthy counterparts. These systems leverage miRNA-mediated regulation to achieve orthogonal control of gene expression, thereby improving diagnostic resolution and minimizing off-target effects.50
Recent synthetic biology approaches have further expanded the utility of miRNAs by integrating pre- and post-translational regulatory modules into split RNA switches and self-feedback mRNA circuits, enabling robust and tunable miRNA sensing within complex cellular environments. Collectively, these platforms illustrate how aberrant miRNA expression profiles, such as those observed in hepatocellular carcinoma, can be translated into programmable diagnostic tools with high specificity and sensitivity.51
Some researchers have found that miR-let-7c acts as a tumor suppressor in gastric, lung, breast, and colon cancer. On the other hand, some studies have revealed that the let-7 family plays a role in the progression of hematological malignancies.52, 53, 54 TGF-β plays a dual role in HCC, initially suppressing tumor development but later promoting fibrosis, invasion, and metastasis. While miR-let-7c has been identified as a tumor suppressor microRNA that regulates oncogenic pathways, including the TGF-β signaling cascade. Dysregulation of miR-let-7c may therefore enhance TGF-β1 overproduction, sustaining oncogenic signaling and accelerating HCC progression. Therefore, we aimed to investigate its expression in HCC patients with the single-nucleotide polymorphism of the TGF-β gene to determine its effect on the production of circulating TGF-β in the serum of HCC patients.

2
Materials and methods
2.1
Subjects
A total of 200 participants were registered in this study (150 HCC patients and 50 controls). The research was approved by the Medical Research Ethics Committee at the National Research Centre, with registration number 25024162023, in accordance with the Helsinki Declaration of 2008. Informed consent was obtained from all participants prior to their enrollment in the study. All patients were admitted to the Faculty of Medicine, Cairo University (El-Qasr EL-Ainy hospital), Cairo, Egypt. The histories, clinical examinations, and laboratory records of all patients were reviewed; HCC patients were included according to the specified inclusion and exclusion criteria. The inclusion criteria were patients with HCC aged 18 years or older. HCC patients with hepatocellular carcinoma diagnosed by histopathology or standard radiologic criteria. Exclusion criteria included the presence of other malignancies other than HCC, patients who had undergone liver transplantation, active systemic infections, autoimmune or chronic inflammatory disorders, or current use of immunosuppressive therapy. Patients who had undergone major surgery, chemotherapy, radiotherapy, or locoregional HCC therapy were also excluded, as well as individuals with chronic alcohol abuse. Pregnant or breastfeeding women were excluded. Controls were recruited from the same geographic and ethnic background as patients, in addition to being age- and sex-matched. Also, they are clear of any malignancy, liver diseases, and Normal liver function tests (AST, ALT, albumin, total and direct bilirubin) within the normal references. Normal complete blood picture (hemoglobin, red blood cells, leucocytes, and platelets) was within normal range, with no history of liver injury, and no viral hepatitis (HCV ab, HBVsAg, HAV), and does not have any other metabolic dysfunctions (normal lipid profile and blood sugar), bacterial, or viral infection. Finally, visually hemolyzed samples were excluded.

2.2
TGF-β genotyping assay rs 9282871
DNA was first extracted from all samples using the manufacturer's instructions for the QiaAMP DNA Blood Kit (Qiagen, Germany). Then, quantification of DNA and purity were measured by a spectrophotometer, NanoDrop Q-5000 (Fisher Scientific, USA). TaqMan genotyping assay for allelic discrimination for TGF-β C/T rs 9282871, with Assay ID: C__25600775_20, Thermo-Fisher Scientific, USA. A total volume of 20 µl, including 10 µl master mix,0.5 µl of assay 40X, 2 µl of DNA, and 17.5 µl of RNase-free water. Reactions were run on the Quant Studio™ 5 Real-Time PCR System for Human Identification, 96-well, Fisher Scientific, USA.

2.3
Quantitation of circulating transforming growth factor (TGF-β) serum levels
Serum quantitation of TGF-β was assessed using the DRG TGF-β ELISA kit, EIA-1864 (GmbH), via enzyme immunoassay measurement. Acid activation was performed before ELISA, in accordance with the manufacturer’s instructions, to quantify total (latent + active) TGF-β. Spectrophotometric measurement by ELISA plate Reader Statfax Chromate 4300, USA, of optical density (OD) was done at 450 nm.

2.4
Quantitation of circulating MiR-let-7c
Following the manufacturer's instructions, the RNeasy Serum/Plasma Kit from Qiagen, Germany, was used to extract the total RNA from all serum samples. The extracted miRNA was quantified and stored at −20°C for further cDNA synthesis by miCURY LNA RT Kit, Qiagen, Germany. The RT-PCR reaction was performed according to the manufacturer's instructions for the miRCURY LNA miRNA PCR assay kit, and the U6 housekeeping gene was used to normalize the relative expression levels of miR-let-7c. Rotor-Gene Q (Qiagen, Germany) was used for the RT-PCR reaction. The reaction was performed as follows: a mixture of 2 μl of cDNA, 2 μl of uni-primer, 2 μl of a particular miR primer, and 8 μl of miScript Syber green mix, the final amount was 20 μl. The reaction was started with initial incubation at 95°C for 15 min, followed by 40 cycles of amplification consisting of denaturation at 94°C for 15 s, annealing at 55°C for 30 s, and extension at 70°C for 30 s. The reaction was performed on the Rotor-Gene PCR (Qiagen).55 The relative expression was calculated using the formula (2-ΔΔCT).55 The variations in relative expression were assessed as fold-change and compared to the mean of the healthy controls.
hsa-let-7c-3p Assay
GeneGlobe ID: YP00205202
Catalog Number: 339306
miRNA sequence (pre-miR) GCAUCCGGGUUGAGGUAGUAGGUUGUAUGGUUUAGAGUUACACCCUGGGAGUUAACUGUACAACCUUCUAGCUUUCCUUGGAGC
Mature miRNA sequence CUGUACAACCUUCUAGCUUUCC
U6 snRNA (v2) miRCURY LNA miRNA PCR Assay
GeneGlobe ID: YP02119464
|Cat. No.: 339306
|miRCURY LNA miRNA PCR Assays.

2.5
In silico analyses
In silico analyses were conducted to investigate the genomic features, interaction networks, protein structure, pathway involvement, and prognostic relevance of TGF-β (TGFB1) in hepatocellular carcinoma (HCC). The Ensembl genome database was used to map the chromosomal location and genomic structure of the TGF-β gene. Genomic alterations and mutation patterns of TGF-β were explored using the cBioPortal for Cancer Genomics, and visualized through a lollipop diagram. The three-dimensional protein structure of TGF-β was retrieved from the AlphaFold Protein Structure Database. Gene–gene interaction networks were analyzed using GeneMANIA, while STRING was employed to assess protein–protein interaction (PPI) networks. Interaction networks obtained from these databases were visualized using Cytoscape software (version 3.10.2). To further explore the functional relevance of circulating miRNAs and their regulatory role in HCC, QIAGEN’s Ingenuity Pathway Analysis (IPA) software was used to identify predicted target genes, affected signaling pathways, and subcellular localization, as well as to evaluate their potential biomarker value. The underlying algorithms and analytical framework of IPA have been described by Krämer et al.56. In addition, the prognostic significance of TGF-β expression in HCC was evaluated using Kaplan–Meier survival analysis. Overall survival was analyzed using the Kaplan–Meier Plotter (KMplot) online database, which integrates publicly available RNA-sequencing survival data from liver cancer cohorts, including The Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC) dataset. Patients were stratified into high- and low-expression groups based on TGFB1 expression levels, and survival differences were assessed using the log-rank test. Because members of the let-7 family share conserved seed sequences and overlapping targets, pathway analyses may display the most highly annotated family member depending on database coverage; such networks were used to illustrate shared pathway connectivity, while experimental quantification was performed using hsa-let-7c-3p.

2.6
Statistical analysis
All biostatistical analyses were computed using Prism Graph Pad v9.0 (Pad Software, Inc., Boston). Categorial data were represented in numbers and percentages. The χ2 2test was used for categorical data, computing the frequency of genotypes and alleles in patients and controls. Continuous data were represented as mean and SD. The Mann-Whitney test was used to compare the non-parametric data between the studied groups. To test the diagnostic efficacy, the receiver operating characteristic (ROC) analysis was performed for TGF-β and miR-let-7c circulating levels to detect the best cut-off, sensitivity, and specificity values. Groups and genotypes were compared using a t-test. Multiple logistic regression analysis was used to study the effect of transforming growth factor genotypes on TGF-β serum levels and miR-let-7c expression levels. P-values less than 0.05 were considered statistically significant with a confidence interval of 95%.

3
Results
3.1
Clinical data of the studied group
Table 1 summarizes the subjects’ clinicopathological and demographic data of the control group (n = 50) and the patient group (n = 150). There was a significant difference in hemoglobin levels, platelets, liver enzymes (ALT and AST), and albumin between the control and HCC groups, reflecting impaired liver function and hematological alterations in HCC patients. There is no significant difference between age, Sex, BMI, and Bilirubin. The average age of the control group was 41.27 years, and the HCC patients’ group was 43.8 years, indicating that both groups were age-matched.

3.2
Genotyping of TGF-β rs9282871 frequencies among the studied groups
Allelic and genotypic distributions of TGF-β rs9282871 between the HCC patients and healthy control groups are demonstrated in Table 2. Based on the genotypes distribution and frequency, our results revealed that HCC patients had a genotype TT represents (59%), CT (22%), CC (19%), while the control group TT genotype represents (16%,) CT (46%), CC (38%), with high significance differences between control and HCC patients, P = 0.0001. Furthermore, the frequency of the T allele in HCC patients is 87% compared with 13% in the control group, while the C allele represents 60% in HCC and 40% of the control group, P = 0.001. Moreover, the dominant model CT + TT is dominant in the HCC group and represents 80%, compared with the control group is 20%, P = 0.0074, as shown in Table 2 and Fig. 1. These findings indicate that the T allele and TT genotype of TGF-β rs9282871 are strongly associated with increased susceptibility to HCC.

3.3
Circulating levels of serum TGF-β among the studied groups
Circulation of serum TGF-β levels was assessed in both HCC patients and the control group. The TGF-β was significantly elevated in the HCC group compared to controls, with a mean level ± SD of HCC patients was 366 ± 111.8 pg/mL compared with the control group 100 ± 26.57 pg/mL, P < 0.0001, as shown in Fig. 2. This suggests that TGF-β overproduction is linked to HCC pathogenesis and may serve as a diagnostic biomarker.

3.4
miRNA let-7c relative expression among the studied groups
The relative expression of circulating miR-let-7c was significantly downregulated in HCC patients compared to the control group. The mean fold change of HCC patients was 0.77 ± 0.78, while the mean fold change of the control group was 1.2 ± 0.78, P < 0.0001, as shown in Fig. 2. This indicates that loss of tumor-suppressive miR-let-7c expression is associated with HCC development.

3.5
The diagnostic assessment of both serum TGF-β and miR let-7c to discriminate between HCC patients and healthy controls
ROC curve analysis was conducted to evaluate the ability of both serum TGF-β and miR-let-7c to distinguish between HCC patients and healthy controls. Our results showed that the Area under the curve (AUC) was 0.9907 for TGF-β, with a sensitivity of 97.33%, specificity of 98%, a confidence interval (CI) of 93.34–98.69%, and a P-value of 0.0001. For miR-let-7c, the AUC was 0.7172, with a sensitivity of 66.67%, specificity of 68%, a CI of 54.19–79.24%, and a P-value of 0.0001, as shown in Fig. 3. These results highlight TGF-β as a highly reliable biomarker, whereas miR-let-7c may serve as a supportive biomarker for HCC detection.

3.6
Association of TGF-β genotyping with the serum expression levels of TGF-β protein and the circulating miR let-7c expression
Multiple logistic regression analysis was performed to determine if there is a relationship between the TGF-β genotypes and the expression of both TGF-β protein and circulating miR-let-7c. Our results showed that TGF-β genotypes had a significantly strong association with the serum TGF-β protein levels, OR = 1.021, CI (1.012–1.034), and P < 0.0001. On the other hand, TGF-β genotypes had a significantly strong negative association with relative expression of miR-let-7c serum levels, OR = 0.06790, CI (0.01096–0.2772), and P < 0.0001 as shown in Table 3. These results indicate that the T allele/TT genotype contributes to both increased TGF-β production and downregulation of miR-let-7c in HCC patients.

3.7
In silico analyses
In silico analyses were performed to characterize the genomic features, interaction networks, pathway involvement, and prognostic relevance of TGF-β (TGFB1) in hepatocellular carcinoma. The chromosomal location and genomic structure of the TGFB1 gene are shown in Fig. 4A, while mutation profiling revealed the distribution and frequency of TGFB1 genomic alterations across HCC samples (Fig. 4B). Structural analysis demonstrated the predicted three-dimensional conformation of the human TGF-β protein (Fig. 4C). Gene–gene interaction analysis indicated that TGFB1 is centrally connected to multiple regulatory genes involved in cell signaling and tumor progression (Fig. 4D). Consistently, protein–protein interaction analysis highlighted extensive interactions between TGF-β and key signaling proteins associated with cancer-related pathways (Fig. 4E). Gene Ontology enrichment analysis further confirmed the involvement of TGF-β–associated proteins in biological processes related to cell proliferation, differentiation, and signaling regulation (Fig. 4F). To evaluate the clinical relevance of TGF-β expression, Kaplan–Meier overall survival analysis was performed. As shown in Fig. 4G, patients with high TGFB1 expression exhibited significantly poorer overall survival compared with those with low expression (hazard ratio = 1.49, 95% confidence interval: 1.05–2.12; log-rank P = 0.024). This survival analysis was derived from publicly available liver cancer RNA-sequencing datasets integrated within the Kaplan–Meier Plotter platform, including the TCGA-LIHC cohort. Pathway-level analysis illustrated the central role of TGF-β signaling within cancer-associated pathways (Fig. 4H). Comparative expression analyses demonstrated variable TGFB1 expression across different cancer types (Fig. 4I–J), while mutation count analysis showed heterogeneous alteration frequencies of the TGFB1 gene among diverse malignancies (Fig. 4K).

4
Discussion
Hepatocellular carcinoma (HCC) is a malignancy that inflammatory cytokines can induce. In the HCC microenvironment, dysregulated TGF-β signaling contributes to inflammation, fibrogenesis, and immunomodulation. This study analyses the TGF-β single-nucleotide polymorphism and its impact on TGF-β production in HCC. In addition, screening the role of miR-let-7c as an epigenetic factor that affects or targets the TGF-β pathway. Indeed, the TGF-β signature could serve as a biomarker for personalized immunotherapy in HCC. Understanding the mechanisms underlying liver cancer immunogenicity, including the role of TGF-β in driving resistance to immunotherapy, is crucial for developing biomarker-based combination immunotherapies for HCC. One hundred fifty HCC patients and 50 healthy controls participated in this study. Circulating microRNAs represent a distinct and biologically meaningful subset of miRNAs that are detectable in various body fluids, including serum and plasma. These extracellular miRNAs originate from both passive release during cell injury, apoptosis, or necrosis, as well as from active, regulated secretion by living cells. Actively exported miRNAs are packaged into membrane-bound vesicles such as exosomes and microvesicles, or are associated with RNA-binding proteins (e.g., Argonaute 2) and lipoprotein complexes, which collectively protect them from enzymatic degradation. This unique mode of transport confers exceptional stability to circulating miRNAs, allowing them to persist in harsh extracellular environments and be reliably quantified.57
Following release into the circulation, miRNAs can be taken up by recipient cells at distal sites, where they may exert functional regulatory effects, supporting their role as mediators of intercellular communication. Clearance of circulating miRNAs occurs primarily through hepatic and renal pathways, yet their half-life remains sufficient for clinical detection. These biological characteristics—stability, accessibility, and disease-specific expression patterns—have established circulating miRNAs as promising minimally invasive biomarkers for cancer diagnosis, prognosis, and therapeutic monitoring. In hepatocellular carcinoma, where tissue accessibility is often limited, serum miRNAs such as miR-let-7c provide a clinically practical window into tumor-associated molecular alterations, reinforcing their potential diagnostic and translational value.58
Our results revealed that the TT genotype and the T allele distribution were dominant in the HCC group rather than the CT and CC genotypes in the control group, with a highly significant difference between the two groups, P = 0.0001. Additionally, the T allele frequency was found in 87% of HCC patients compared with 13% in the controls, with a highly significant P-value of 0.0001. These results are in agreement with Abdel Salam et al, 2020, who showed that the T allele and the TT genotype were found in hepatocellular carcinoma in patients who were infected with HCV.59 In contrast, Silverman et al, 2004, documented that the C allele was dominant in the TGF-β polymorphism −509 in patients with asthma.60
Our results showed that serum TGF-β was highly elevated in the serum of HCC patients compared to the control group, in parallel with He and Liu, 2016, who proved that serum TGF-β can be used as a biomarker in HCC patients.61 Besides Farid et al, 2014, who studied and proved the expression of serum TGF-β in patients of HCC.26 Moreover, our results demonstrated the high diagnostic efficacy of TGF-β in HCC patients, as confirmed by ROC analysis, which showed a high AUC value of 0.9907, Sensitivity of 97.33%, Specificity of 98%, and a p-value of 0.0001.
On the other hand, our data documented the significant downregulation of miR-let-7c relative expression in HCC patients compared to the control group, with a mean fold change of 0.7 in the HCC patients and 1.22 in the control group, P = 0.001. According to ROC analyses, our results showed that miR-let-7c can discriminate between HCC patients and controls, with an AUC of 0.7172, sensitivity of 66.67%, specificity of 68%, and P = 0.0001, as shown in Table 3.
These results are confirmed by Lin et al, 2024, who proved that microRNA let-7c-5p alleviates hepatocellular carcinoma by targeting the enhancer of the Zeste homolog.39 In addition, Jilek et al, 2019, stated that bioengineered Let-7c inhibits orthotopic hepatocellular carcinoma and improves the survival rate.62 In contrast with Shi et al, 2017, who revealed that miR-let-7c overexpression was related to bad outcome and disease progression in HCC patients.63 Our results showed that TGF-β genotypes had a significantly strong association with the serum TGF-β protein levels, OR = 1.021, and P < 0.0001. On the other hand, TGF-β genotypes had a significantly strong negative association with relative expression of miR-let-7c serum levels, OR = 0.06790, CI (0.01096 to 0.2772), and P < 0.0001. Combining both markers yielded an AUC of 0.994 (95% CI: 0.987–1.000) in a multivariate logistic regression model, indicating the potential clinical value of a dual TGF-β1/miR-let-7c biomarker panel.
Our results were confirmed by computational bioinformatics and biostatistical analysis, which showed that miR-Let-7c targets the TGF-β pathway by targeting genes such as TGFR1, TGFR3, SMAD2/3/4/6, and MYC, as shown in Fig. 5. Our results demonstrated that TGF-β and miR-let-7c are inversely proportional to each other; downregulation of miR-let-7c stimulates the TGF-β pathway. These results were in accordance with Wang et al. 2020 who had documented that miR-let-7c targets the TGF-β pathway, causing chronic kidney disease and kidney fibrosis. His results also showed that the downregulation of miR-let-7c coincides by TGF-β pathway activation 64. Besides, Deji et al., 2020, revealed that NF-Kb was induced by TGF-β pathway stimulation that was triggered by miR-let-7c expression suppression in retinal pigment epithelial cells.65, 66 In contrast with Xie et al., who had stated that let-7c suppresses cholangiocarcinoma proliferation, but it promotes cell proliferation and invasion to extrahepatic regions.67
From a translational perspective, the inverse relationship observed between circulating TGF-β levels and miR-let-7c expression underscores the potential of miR-let-7c as both a diagnostic input and a therapeutic target. Recent developments in RNA-based synthetic biology provide a compelling framework for leveraging such miRNA dysregulation in precision medicine. miRNA-responsive mRNA platforms have been successfully employed to drive cell-specific drug delivery, induce tumor-selective immunogenic cell death, and enable programmable activation of therapeutic pathways exclusively within malignant cells.68
In parallel, RNA interference–based technologies incorporating single-cell barcoding and encoded gene silencing have enabled high-throughput combinatorial screening of siRNA and miRNA interactions, accelerating the identification of synergistic RNA therapeutics. Within this context, miR-let-7c downregulation in HCC may be exploited to design smart RNA circuits or modular mRNA therapeutics that selectively respond to TGF-β–driven oncogenic signaling. Such approaches position miR-let-7c not only as a circulating biomarker but also as a functional component of next-generation RNA therapeutics tailored for hepatocellular carcinoma.69 This study has some limitations. Its observational case–control design precludes causal inference regarding the relationship between TGF-β rs9282871, miR-let-7c dysregulation, and hepatocellular carcinoma. Functional validation experiments were not performed and should be addressed in future mechanistic studies. In addition, detailed stratification of HCC etiologies was not fully explored. Although the combined TGF-β/miR-let-7c biomarker model showed excellent diagnostic performance, external validation in independent cohorts is required to confirm generalizability and reduce potential overfitting.

5
Conclusion
TGF-β rs 9282871 is associated with TGF-β protein overexpression in the serum of HCC patients, and it is reciprocally expressed with serum miR-Let-7c. Additionally, miR-let-7c was downregulated in HCC patients compared to healthy individuals. Therefore, TGF-β and miR-let7c can be used as biomarkers for the diagnosis of HCC patients.

Funding

ASRT-BA grant no. 2378.

Declaration of generative AI in scientific writing

All authors declare that there is no use of generative AI and AI-assisted technologies in writing the manuscript; the entire manuscript was written without any AI usage.

CRediT authorship contribution statement
Sally Farouk: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Investigation, Funding acquisition. Maha M. Elbrashy: Software, Resources, Methodology, Formal analysis, Data curation. Ahmed Khairy: Supervision, Data curation, Conceptualization. Noha G. Bader El Din: Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Project administration, Conceptualization.