1. Introduction

Hepatocellular carcinoma (HCC) is a common and serious liver cancer condition that typically manifests noticeable symptoms only in the later stages of tumor formation and progresses rapidly [1, 2]. Major risk factors for HCC include chronic viral hepatitis (especially hepatitis B virus infection), alcohol abuse, fatty liver disease, genetic factors, dietary habits, and environmental exposure [3]. Particularly in some developing countries, hepatitis B virus infection is the primary causative factor. With the advancement of scientific research, there is an increasing focus on exploring tumor occurrence and development from a genetic perspective [4, 5]. Differential gene expression plays a significant role in the development of HCC. With the development of transcriptomics and bioinformatics technologies, we have been able to identify differential gene expression patterns between HCC tissues and normal liver tissues [6]. These differential genes not only serve as diagnostic markers for HCC but may also be involved in regulating biological processes such as tumor cell proliferation, metastasis, and apoptosis [7]. However, it remains unclear how these differential genes influence tumor immune responses and immune evasion in HCC.

The liver, as one of the largest visceral organs in the human body, plays a crucial role in physiological and immune functions [8]. However, due to the liver’s unique position in metabolism, detoxification, and immune regulation, it is susceptible to various external and internal factors, thereby increasing the risk of developing HCC [9]. Moreover, sepsis, a severe disease caused by systemic infection, is closely associated with liver function. Therefore, the liver, acting as a bridge, further strengthens the relationship between HCC and sepsis [10, 11]. In the fields of oncology and immunology, the interactions among HCC, sepsis, tumor immunity, and differential gene expression are increasingly being scrutinized [12]. The occurrence of sepsis may also accelerate the progression of HCC by inducing inflammatory responses, immune suppression, and metabolic abnormalities, thereby promoting tumor growth and metastasis, ultimately increasing the mortality rate of patients [13, 14].

Acute sepsis is a systemic inflammatory response syndrome, usually caused by bacterial infection. Severe sepsis can lead to multiorgan dysfunction and even endanger life. As the largest immune organ of the human body, the liver plays an important role in the occurrence and development of sepsis [15, 16]. HCC is a malignant tumor. The incidence rate is closely related to cirrhosis, which brings long-term health challenges to patients. The long-term prognosis of patients with acute sepsis and HCC is often influenced by multiple factors. The basic health status and immune system function of patients play a crucial role in prognosis [17, 18]. Patients with underlying diseases or immune suppression are more prone to infections, multiple organ dysfunction, and recurrence. Secondly, the timeliness and effectiveness of treatment are also crucial for prognosis, and postoperative rehabilitation and lifestyle management of patients also have a significant impact on long-term prognosis. The genetic factors and lifestyle habits of patients can also have an impact on the long-term prognosis of acute sepsis and HCC, and some genetic variations may make patients more susceptible to acute sepsis or HCC [19, 20]. In patients with a history of acute sepsis, the likelihood of developing HCC in the later stages may increase, which may be the result of the combined effects of factors such as the immune status and functional recovery of the liver during the infection process [21].

This study selected differential genes related to sepsis from TCGA and GEO databases, combined with clinical information of patients in these databases, to construct a prognostic model based on these common differential genes. Furthermore, the study analyzed the association between the results of the model and mutation status, immune function, and drug sensitivity response in HCC patients. Pathway enrichment analysis was also conducted to explore potential mechanisms. This study aims to systematically investigate the molecular mechanisms related to differential gene expression, immunity, and sepsis in HCC patients by comprehensively utilizing transcriptomics, bioinformatics, and clinical data, with the goal of providing new scientific evidence for individualized treatment and prognosis assessment of HCC. The whole study is shown in Figure 1.

2. Methods

3. TMB

Using tumor mutation burden data from samples in the TCGA database, along with risk values calculated for each sample using our constructed model, we analyzed the differences in mutation burden between high- and low-risk groups. Through survival curves, we further demonstrated the survival differences between samples with high and low mutation burden.

4. Results

4.1. Acquisition of Public Differential Genes

After analyzing the HCC samples in the TCGA database, we obtained differential genes between tumor and adjacent tissues (1876 genes) (Figure 2(a)). After analyzing the sepsis samples in the GEO database, we obtained differential genes between control group samples and sepsis (602 genes) (Figure 2(b)). By intersecting these two sets, we identified 24 common differential genes (Figures 2(c) and 2(d)).

4.3. Survival Analysis

Combining the previously obtained risk values for each sample, we employed risk curves for each sample, revealing that patient mortality in the high-risk group increased over time compared to the low-risk group. Moreover, the expression levels of KRT20, PAEP, CCR3, and ANLN were elevated in the high-risk group, as illustrated in Figure 3 (a–c: all samples, d–f: train groups, g–i: test groups). Through survival analysis, it can be observed that, in all samples, training set, and validation set, the survival rate of the low-risk group is higher than that of the high-risk group as time progresses, as shown in Figures 4(a), 4(b), 4(c). Additionally, we further extracted the expression levels of the four genes included in the model, with KRT20, PAEP, CCR3, and ANLN showing higher expression in the high-risk group, as illustrated in Figures 4(d), 4(e), 4(f), 4(g). To further validate the accuracy of the model predictions, we conducted independent prognostic analysis on different age groups, genders, stages, and grades, showing that our prognostic model’s risk values have excellent predictive results in both univariate and multivariate prognostic analyses (Figures 4(h) and 4(i)), with p values all less than 0.01. Additionally, we further demonstrated the predictive accuracy of the model at 1, 3, and 5 years using ROC curves, The area under the curve (AUC) values were 0.750 at 1 year, 0.666 at 3 years, and 0.669 at 5 years, respectively, underscoring the model’s predictive accuracy at these time intervals, as displayed in Figure 4(j). Furthermore, to further validate the accuracy of the model predictions, we used ROC curves to evaluate the accuracy of the model among different age groups, genders, stages, and grades. The results are displayed in Figure 4(k). Combining clinical data from pan-cancer TCGA database and risk values obtained from our model, we divided the samples into high- and low-risk groups based on the median risk value, further comparing the survival differences between the high- and low-risk groups in the progression-free survival status. From the results, we observed significant differences in survival time between the high- and low-risk groups in the progression-free survival period as determined by our model grouping. The results are displayed in Figure 4(l).

4.4. Functional Enrichment Analysis

Through functional enrichment analysis, GO enrichment analysis results indicate that the differential genes in sepsis samples are mainly enriched in the following pathways (BP: leukocyte cell-cell adhesion; regulation of T-cell activation; regulation of leukocyte differentiation; viral process; regulation of hemopoiesis; peptidyl-serine modification; positive regulation of cytokine production; regulation of lymphocyte differentiation; peptidyl-serine phosphorylation; intrinsic apoptotic signaling pathway; CC: proteasome core complex; secretory granule lumen; cytoplasmic vesicle lumen; vesicle lumen; proteasome complex; ficolin-1-rich granule lumen; ficolin-1-rich granule; endopeptidase complex; respirasome; peptidase complex; MF: NADH dehydrogenase (ubiquinone) activity; NADH dehydrogenase (quinone) activity; NADH dehydrogenase activity; NAD(P)H dehydrogenase (quinone) activity; peptidase activator activity involved in apoptotic process; oxidoreductase activity, acting on NAD(P)H, quinone, or similar compound as acceptor; protein serine/threonine kinase activator activity; FK506 binding; cysteine-type endopeptidase activator activity involved in apoptotic process; oxidoreduction-driven active transmembrane transporter activity), as shown in Figures 5(a) and 5(b). To further explore the role of these differential genes in tumorigenesis, we conducted KEGG enrichment analysis on the common differential genes, revealing that these differential genes are mainly enriched in the following pathways (cell cycle; neuroactive ligand-receptor interaction; oocyte meiosis; ECM-receptor interaction; serotonergic synapse; retinol metabolism; protein digestion and absorption; alcoholism; progesterone-mediated oocyte maturation; bladder cancer; p53 signaling pathway; GnRH secretion; morphine addiction; systemic lupus erythematosus; motor proteins; mineral absorption; GABAergic synapse; cytokine-cytokine receptor interaction; dopaminergic synapse; nicotine addiction; neutrophil extracellular trap formation; linoleic acid metabolism; transcriptional misregulation in cancer; retrograde endocannabinoid signaling; gastric cancer; Fanconi anemia pathway; IL-17 signaling pathway; chemical carcinogenesis-receptor activation; cytoskeleton in muscle cells; chemical carcinogenesis-DNA adducts), as shown in Figures 5(c) and 5(d). These enrichment results demonstrate that these differentially expressed genes related to sepsis are indeed significantly enriched in key pathways of tumor development, further emphasizing the significance of studying the intersection between sepsis and HCC.

4.5. Immune Cell Infiltration Analysis and Immune-Related Function Analysis

Through immune-related function analysis, it can be seen that the immune-related functions that differ between the high- and low-risk groups are (Type_II_IFN_Reponse, Cytolytic_activity, MHC_class_I, and Type_I_IFN_Reponse), as shown in Figure 6(a). Through immune cell infiltration analysis, it can be observed that the type of immune cells positively correlated with ANLN was macrophage M0, while the type negatively correlated is NK cells activated, T cells regulatory (Tregs), T cells CD4 memory resting, and monocytes, as shown in Figure 6(b). The results of the differences in 22 immune cells in HCC samples are shown in Sfigure 2A and 2B. Combining the risk values of each sample in the prediction model, the differences in immune cell infiltration between high-risk and low-risk groups were compared and analyzed, as shown in Sfigure 2C and 2D.

4.7. Differential Analysis of TMB

Through the TMB data from samples in the TCGA database, combined with the samples′ risk values, we visualized the tumor mutation burden using waterfall plots. The top three mutated genes in the high-risk group were TP53 (39%), CTNNB1 (24%), and TTN (24%), while in the low-risk group, they were CTNNB1 (28%), TTN (24%), and TP53 (13). The results are shown in Figures 7(a) and 7(b). We analyzed the differences in mutation burden between the high- and low-risk groups, and from the results, it can be observed that the mutation frequency in the high-risk group is higher than that in the low-risk group, as depicted in Figure 7(c). Next, we demonstrated the survival differences between high and low mutation groups using survival curves. It can be seen from the results that with increasing time, the survival rate in the high mutation group is lower than that in the low mutation group, as shown in Figure 7(d). To analyze the relationship between mutation burden, risk values, and survival time, we divided the samples into four groups: H-TMB + high risk, H-TMB + low risk, L-TMB + high risk, and L-TMB + low risk. It can be observed from the results that the samples in the L-TMB + low risk group have the highest survival rate as time extends, further indicating the accuracy of our model. The results are shown in Figure 7(e).

4.8. Drug Sensitivity Analysis

Based on the drug sensitivity data file in the database, we scored the drug sensitivity for each sample and analyzed the sensitivity to different drugs in the high-risk and low-risk groups by combining each sample’s risk value. Through filtering, we excluded results without differences and obtained 12 drugs with differential drug sensitivity between the high-risk and low-risk groups, as shown in Figures 8(a), 8(b), 8(c), 8(d), 8(e), 8(f), 8(g), 8(h), 8(i), 8(j), 8(k), 8(l). In addition, we obtained 12 drugs with lower sensitivity, as shown in Sfigure 1A–1L.

5. Discussion

Hepatocellular carcinoma (HCC) is a highly heterogeneous and immunosuppressive cancer, characterized by complex interactions between tumor cells and the immune microenvironment [23, 24]. Dysregulation of immune responses in HCC leads to tumor progression and limits the efficacy of existing treatment modalities [25, 26]. Understanding the differential gene expression patterns that lead to immune evasion and immune suppression in HCC is crucial for developing targeted immunotherapies and improving patient prognosis [27]. Studies have shown that sepsis can be a potential trigger for hepatocellular carcinoma [28]. The systemic inflammatory response triggered by sepsis may induce abnormal cell proliferation and malignant transformation in the liver, thereby promoting the occurrence and development of hepatocellular carcinoma [20, 29]. Individuals with liver disease are at an increased risk of infection, which is a primary contributor to sepsis. The liver plays a vital role in the immune system, including functions such as pathogen clearance and the regulation of inflammatory responses. However, when hepatic function is compromised, these immune processes may be diminished, rendering patients more susceptible to infections and consequently heightening the risk of sepsis [30]. Previous studies have found that sepsis may directly impair liver function and exacerbate the progression of liver disease.

Sepsis, the systemic inflammatory response caused by sepsis, may lead to liver cell damage and acute deterioration of liver function, thereby accelerating the progression of liver disease, including the occurrence of cirrhosis and hepatocellular carcinoma [31, 32].

The molecular mechanisms of hepatocellular carcinoma (HCC) are complex and multifactorial. They involve gene mutations, abnormalities in cellular signaling pathways, and genetic factors. Typically, these mechanisms include abnormal expression of oncogenes and tumor suppressor genes, abnormal cell proliferation and apoptosis, and changes in the tumor microenvironment. Additionally, chronic viral hepatitis infection and alcohol abuse are significant contributors to HCC. Multiple factors can disrupt the balance between inactivation and activation of tumor suppressor genes and oncogenes, leading to abnormal activation of molecular signaling pathways. Consequently, this is one of the primary reasons why sepsis may lead to the development of HCC [3, 33]. Hepatitis B virus (HBV) and hepatitis C virus (HCV) are the primary causative agents of hepatocellular carcinoma (HCC). Cirrhosis is the consequence of prolonged chronic liver disease, leading to an elevated risk of HCC development. Prolonged alcohol abuse can induce liver damage and elevate the risk of developing HCC. Fatty liver disease, including nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH), are also recognized as risk factors for HCC [34–36]. Research has demonstrated that aberrant activation of molecules in various signaling pathways regulating cell survival, apoptosis, cell cycle, proliferation, and differentiation contributes to the advancement of HCC. During sepsis, immune cell function, including macrophages and T cells, is inhibited by inflammatory mediators, diminishing the body’s capacity to combat infections [37]. Long-term inflammatory response can result in immune system exhaustion and depletion of the body’s immune resources, rendering it unable to effectively respond to infections. The inflammatory response triggered by sepsis can cause damage to lymphatic organs and disrupt the normal function of the immune system [38]. Sepsis may disrupt the balance of immune regulation in the body, disrupting the balance between inflammation and anti-inflammatory responses, leading to the emergence of immune suppression [39]. Sepsis can further induce the occurrence of HCC by inducing immune suppression in the body.

As research on the molecular mechanisms of HCC and sepsis progresses, there is increasing attention on exploring the association between them at the genetic level. Exploring their potential biological mechanisms through differential gene analysis and functional analysis methods, analysis of differential genes reveals key genes and signaling pathways driving the onset of HCC and immune dysfunction. These genes play a crucial role in regulating the tumor microenvironment of hepatocellular carcinoma and immune responses [40, 41]. The upregulation of common immune checkpoint molecules (such as PD-L1 and CTLA-4) in HCC leads to T-cell exhaustion and immune evasion [42, 43]. The use of immune checkpoint inhibitors targeting these immune checkpoints has yielded promising results in clinical trials, highlighting the importance of differential gene expression analysis in identifying potential therapeutic targets for HCC.

Prognostic models can guide doctors to promptly initiate treatment in emergency situations by assessing the severity of the patients and predicting the progression of the disease. By analyzing the clinical indicators and biomarkers of patients, prognostic models can provide more accurate assessments of their condition, thereby facilitating doctors to make faster diagnosis and treatment decisions [44]. In cases of acute sepsis, appropriate treatment measures can be taken promptly based on the evaluation results of the prognostic model, thereby reducing the patient’s mortality rate and mitigating the impact of acute sepsis on the patient’s systemic immune status [45, 46]. The prognostic model also has the potential to facilitate precise medication in acute situations. By integrating patients’ physiological indicators, disease characteristics, and genotype information, prognostic models can provide doctors with more accurate medication recommendations, help prevent the overuse and improper use of drugs, and improve treatment effectiveness, thereby further alleviating long-term prognosis and reducing the likelihood of HCC occurrence.

This study applied techniques such as RNA sequencing to tumor tissues and biological specimens from sepsis patients, identifying genes with significantly altered expression levels in HCC and sepsis. Using these differentially expressed genes as a starting point, the study further explored their potential roles in the onset of HCC and patient prognosis. Additionally, through analysis of immune cell infiltration and differences in immune function, the study further confirmed the roles of these common differentially expressed genes, providing a reference for further exploration of the potential association between the two conditions.

However, this study has some limitations. Firstly, due to limitations in sample size and study design, we cannot exclude the influence of other potential factors on differential gene expression, thus requiring larger-scale studies for validation. Secondly, this study is limited to observational research and has not delved into the functions and mechanisms of differential genes and immune factors, necessitating further cellular and animal experiments.

6. Conclusions

This study obtained transcriptomic data of HCC tumor and adjacent tissues from TCGA, as well as data from sepsis patients and control groups from the GEO database. Differentially expressed genes were identified in each dataset, and the common differentially expressed genes were further investigated. Using these common differential genes and patient clinical information, a prognostic model was constructed, which showed good predictive performance for HCC. Additionally, significant differences were observed between high- and low-risk groups based on this prognostic model. Furthermore, the analysis of the results from the common differential gene prediction model was related to HCC patient mutation status, immune function, and drug sensitivity reactions, providing a reference for further research on the potential association between sepsis and HCC. These predictive models can enhance patient prognosis and inform the creation of early treatment protocols for sepsis, consequently aiding in the prevention of sepsis-induced HCC development through the modulation of the overall immune status.

Conflicts of Interest

The author claims that this research was conducted without any commercial or financial relationships that could be interpreted as potential conflicts of interest.

Authors’ Contributions

Xin Chu and Qi Wu analyzed and interpreted the data and drafted the manuscript. Linglin Kong and Qiang Peng interpreted the data and contributed to the substantial revisions of the manuscript and helped to perform the statistical analysis and interpret the data. Junhua Shen made a contribution to the conception and design, analyzed and interpreted the data, supervised the study, provided the project funding, revised the manuscript, and finally approved the version of the manuscript for publication. All authors read and approved the final manuscript. Xin Chu is a co-first authors.