Ting Li , Lu-Jian Zhu , An-Min Huang , Yi-Feng Wei , Jun Xu , Ye-Jin Xu

a Department of Infectious Diseases, Jinhua Municipal Central Hospital, Jinhua 3210 0 0, China

b School of the First Clinical Medical Sciences (School of Information and Engineering), Wenzhou Medical University, Wenzhou 325035, China

TotheEditor:

Hepatocellular carcinoma (HCC) is a prevalent form of gastrointestinal malignancies.The current combination of immunotherapy and other treatments improves overall survival compared to conventional therapies, but the immunosuppressive microenvironment of HCC is a significant barrier to the efficacy of immunotherapeutic drugs [1].Tumor immune escape is promoted by interactions among immunosuppressive cells, including tumor-associated macrophages, marrow-derived suppressor cells, tumor-associated neutrophils, cancer-associated fibroblasts, and tumor-infiltrating Tregs [2].

Researchers have mapped out lactate metabolism as a potential therapeutic target for HCC.Following the deletion of lactate dehydrogenase-A, Serra et al.found that CD4 + lymphocytes was significantly increased in HCC, and that the metabolic reprogramming was toward Warburg metabolism [3].Hypoxia and lactate metabolism in HCC cells may be the key to improving the immunosuppressive microenvironment.

The Cancer Genome Atlas (TCGA, http://portal.gdc.cancer.gov/ )was utilized to collect information on liver cancer patients and healthy individuals.MsigDB ( http://www.gsea-msigdb.org/gsea/index.jsp ) was used to retrieve 1557 hypoxia-related genes and 327 lactic acid metabolism-related genomes.Totally 421 cases (consisting of 363 HCC patients and 58 normal individuals) from TCGA were randomly divided into the training set and the test set in a 3:2 ratio.Using least absolute shrinkage and selection operator (LASSO) regression and Cox proportional hazard regression,we subsequently determined hypoxia- and lactic acid metabolismrelated prognostic risk signatures (HLSig).

Subsequently, survival analyses were conducted on patients classified as “high-risk” or “l(fā)ow-risk” with the median of HLSigs,and the receiver operating characteristic (ROC) curve was used to evaluate model accuracy.To further investigate the significance of prognostic genes in patient risk assessments and liver cancer cell tumorigenesis, we constructed a nomogram based on the results of multivariate regression ( Table 1 ) and conducted someinvitrotests.Targeting protein for Xenopus kinesin-like protein 2 (TPX2)gene was manipulated to find its role in HCC.Utilizing ROC curves and decision curve analysis (DCA), we evaluated the effectiveness of the nomogram.For the investigation of biological influences, we used two HCC cell lines, HCCLM3 and Huh7.Cell counting kit-8(CCK8) assays, transwell assays, scratch wound-healing assays, and lactic acid assays were investigated [4].

Table 1Multivariate Cox regression analysis for overall survival.

Fig.1 A and B depict the coefficient profiles of LASSO and the optimal penalty parameter lambda.Using the test set data and multivariate Cox regression, secreted phosphoprotein 1 (SPP1)andTPX2were subsequently screened out in order to construct HLsig.The formula for Hlsig is as follows: HLSig = 0.033 × Exp(SPP1) + 1.535 × Exp (TPX2).In the training set, the prognosis was poor for the high-risk group [hazard ratio (HR) = 2.57, 95% confidence interval (CI): 1.60-4.12;P＜0.001; Fig.1 C].The high-risk group in the testing set also exhibited a poor prognosis (HR = 1.83,95% CI: 1.05-3.19;P= 0.034; Fig.1 E).In Fig.1 D, F, ROC analysis indicated that the area under the curve (AUC) values of the training set for 1-, 3-, and 5-year were 0.788 (95% CI: 0.712-0.864),0.652 (95% CI: 0.535-0.785), and 0.732 (95% CI: 0.623-0.840), while those of the test set were 0.771 (95% CI: 0.678-0.864), 0.660 (95%CI: 0.535-0.785), and 0.702 (95% CI: 0.549-0.855).As depicted in Fig.2 A, a nomogram based on the entire TCGA set was constructed according to the age, sex, grade, stage and HLsigs.The AUC of thenomogram for three-year overall survival was 0.713 (95% CI: 0.655-0.770) ( Fig.2 B).Results demonstrated this nomogram’s superior discriminatory ability and clinical utility, and its ability to guide clinical decision-making ( Fig.2 C).

Fig.1.Establishment and validation of HLSig.A: Optimal penalty parameter lambda of the LASSO; B: LASSO model; C: Kaplan-Meier curve for HLSig relative to the overall survival of the training set; D: ROC curve of the training set; E: Kaplan-Meier curve for HLSig relative to overall survival of the testing set; F: ROC curve of the testing set.HLSig: hypoxia- and lactic acid metabolism-related prognostic risk signature; LASSO: least absolute shrinkage and selection operator; ROC: receiver operating characteristic;HR: hazard ratio; AUC: area under the curve; CI: confidence interval; TPR: true positive rate; FPR: false positive rate.

After a successfulTPX2knockdown, the biological characteristics of HCC cells were observed ( Fig.3 A).The CCK8 assays demonstrated that cells with lowTPX2expression had diminished proliferation ( Fig.3 B).In transwell migration assays,TPX2downregulation inhibited HCC cell migration and invasion ( Fig.3 C).Lactic acid levels decreased both intracellularly and in the supernatant afterTPX2knockdown ( Fig.3 D), indicating thatTPX2may be essential for regulating lactic acid levels.

Fig.3.In vitro experiments.A: Expression of TPX2 at mRNA level after siRNA transfection; B: cell counting kit-8 assays at 0, 24, 48, 72, and 96 h after siRNA transfection;C: transwell assays with or without Matrigel for the migration and invasiveness of cancer cells; D: determination of lactate content in (left) and supernatant (right) of hepatocellular carcinoma cells.*: P ＜ 0.1, **: P ＜ 0.01, ***: P ＜ 0.001 compared with si-TPX2-NC.TPX2: targeting protein for Xenopus kinesin-like protein 2; siRNA: small interfering RNA; NC: negative control.

In HCC, cellular hypoxia and metabolic alterations support the energy requirements and tumor microenvironment [ 5 , 6 ].Experiment has shown thatSPP1inhibits glycolysis, enhances the Warburg effectively and activates macrophages [7], and promotes the expression of programmed cell death protein 1 ligand [8].Wang et al’s research demonstrated thatTPX2can regulate CX-C chemokine receptor type 5 via the nuclear factor kappa-B signal pathway to maintain CD8 + T cells [9].However, it remains unknown howTPX2affects the lactic acid metabolism of HCC.

According to ourinvitrostudy,TPX2influenced the tumorigenic properties of HCC cells, including their proliferation, invasion, and migration.And the reduction of lactate inside and outside HCC cells followingTPX2knockdown suggested thatTPX2influenced glycolysis and lactic acid metabolism.More experiments are required to investigate the mechanism underlying this phenomenon.Our research suggests a direction for future research on immunomodulatory therapies for HCC.

Acknowledgments

None.

CRediT authorship contribution statement

Ting Li:Data curation, Formal analysis, Writing - original draft.Lu-Jian Zhu:Methodology, Writing - original draft.An-Min Huang:Software, Visualization.Yi-Feng Wei:Investigation, Data curation.Jun Xu:Resources, Visualization.Ye-Jin Xu:Conceptualization, Funding acquisition, Supervision, Writing - review & editing.

Funding

This study was supported by a grant from Zhejiang Province Public Welfare Technology Application Research Funding Project( GD22H036691 ).

Ethical approval

Not needed.Competing interest

No benefits in any form have been received or will be received from a commercial party related directly or indirectly to the subject of this article.