| Primary liver cancer(PLC)is one of the most common malignant tumors in the world.In all cancers,the incidence rate is sixth,and the mortality rate is fourth.Among them,the incidence rate of male is fifth,and the mortality rate is second.According to the latest statistical analysis of the incidence and mortality of malignant tumors in China,the incidence rate of new cases and deaths of liver cancer in China is 370 thousand and 326 thousand respectively,ranking fourth and second respectively,among them,the incidence rate of male is third,and mortality rate is second.Liver fibrosis and cirrhosis caused by hepatitis B virus(HBV)infection are the main risk factors of hepatocellular carcinoma(HCC).70-80% of patients with HCC in China have a history of hepatitis B.HBV infection is the main cause of primary liver cancer.The number of hepatitis B patients in China is huge.The incidence of hepatitis B-related liver cancer is still high for a long time,and the early diagnosis rate is less than30%.Although surgical resection can greatly improve the prognosis of patients,the overall5-year survival rate of liver cancer is still very low,and the improvement speed is quite slow.In recent years,it has increased from 10.1% to 12.1%.Early diagnosis and early intervention are considered to be the most important way for patients to obtain long-term survival.However,early diagnosis can only rely on effective means of HCC screening and monitoring,especially for high-risk groups,such as patients with chronic hepatitis B and liver cirrhosis,which can effectively improve the detection rate of early HCC and reduce the mortality of liver cancer with HBV infection as the main cause.Pathological examination after liver puncture is the gold standard for the diagnosis of liver cancer at present,but invasive puncture has great trauma to patients and cannot be generally accepted by patients.Imaging examination(such as MRI,CT,etc.)is of high value in the diagnosis of liver cancer,which can provide help for the diagnosis of liver cancer patients,and can clearly show the venous lesions and reflect the liver blood flow of patients.However,there are problems such as high examination cost and long waiting time before examination.At present,most guidelines recommend the use of serum alpha fetoprotein(AFP)and liver ultrasound as the main tools for screening HCC.Liver ultrasound is the most common imaging method in clinical.Its advantages are low cost,easy to operate and portable,but its detection rate of small liver cancer is limited.Serum AFP has long been regarded as a high diagnostic value of HCC serological markers,which has important clinical application value for early diagnosis,efficacy monitoring,recurrence monitoring and prognosis judgment of HCC.However,the appearance of false positive and false negative conditions will affect the accuracy of clinical diagnosis.About 30% to 40% of patients with liver cancer have normal AFP level and lack of sensitivity and specificity of AFP,the defect can cause missed diagnosis and misdiagnosis of HCC.In clinical diagnosis of HCC,it is still urgent to add other serological markers and to improve the efficiency of HCC monitoring by relying on the multi parameter diagnostic model of big data.Clinically,the ideal auxiliary diagnostic markers of liver cancer need to have high sensitivity and can assist in early diagnosis.The diagnostic specificity should be improved as much as possible,and it is easy to repeat dynamic detection.Meanwhile,it should also be able to show the biological status,stage and monitoring treatment effect of tumor.In addition to AFP,some guidelines for the diagnosis of liver cancer also mentioned some other serological markers of liver cancer,mainly including alpha fetoprotein heterogeneity(AFP-L3),abnormal prothrombin(DCP,also known as PIVKA-II),α-fucosidase(AFU),seven kinds of micro RNAs,etc.With the development of proteomics,genomics and transcriptomics,more and more serum markers are used in clinic.In the era of precision medicine,with the development of big data and artificial intelligence in medicine,more and more models are used in the early auxiliary diagnosis and course management of HCC.For example,the auxiliary diagnosis and MVI prediction model of HCC established by using common clinical indicators in this study is an effective method to improve the efficiency of auxiliary diagnosis.Part Ⅰ: Clinical validation of GALAD model in diagnosis of hepatocellular carcinoma and prediction of microvascular invasionGALAD model was established by Johnson in 2014 based on clinical parameters,in 2016,Berhane was validated based on multiple international cohorts.The parameters include gender(G),age(A)and three serological markers alpha fetoprotein(A),lectinbound alpha-fetoprotein(L)and des-gamma-carboxy prothrombin(D).GALAD model can improve the sensitivity of HCC diagnosis,especially the detection rate of early HCC,and can supplement the deficiency of ultrasound screening.At present,the prediction of MVI is mainly achieved through the pathomorphological detection of tissue samples after surgery,but it can not achieve preoperative prediction.Some scholars have proposed to use DCP to predict the occurrence of MVI,while the statistical model GALAD,which uses DCP as an important weight parameter,has not been proposed to predict MVI.This part of the study mainly discusses the value of GALAD model in the diagnosis of hepatocellular carcinoma and the prediction of microvascular invasion(MVI).A retrospective study was conducted,5919 patients with hepatocellular carcinoma(HCC)and 1745 patients with benign liver diseases(BLDs)were enrolled as the experimental group and the control group respectively.The results showed that the diagnostic value of GALAD model was higher than that of single serological marker.The cutoff value of-0.33 was the best.The sensitivity,specificity and accuracy of GALAD model were 91.9%,86.8% and 90.7%respectively.The area under the curve was as high as 0.960 [95% CI(0.955～0.964)].For the tumor with diameter less than 5cm,the model also has a high auxiliary diagnostic value,especially in the auxiliary diagnosis of HCC with diameter less than 2cm,the area under the curve of GALAD model can also reach 0.850 [95% CI(0.832-0.869)].Compared with no MVI(M0),the GALAD model values of low-risk group(M1),high-risk group(M2)and MVI group(M1+2)were significantly higher.The area under the curve of M0 vs M1+2 was 0.663 [95% CI(0.649-0.677)],which was significantly higher than that of DCP 0.635 [95% CI(0.621-0.649)].The product under the curve of MVI(M2)predicted by GALAD model was 0.717 [95% CI(0.701-0.733)].The results show that GALAD model is better than single marker in the diagnosis of hepatocellular carcinoma,and has a certain clinical predictive value for MVI.Part Ⅱ: Establishment and application of differential diagnosis model for HCCThe establishment of the GALAD diagnosis model validated in the first part did not focus on HCC related to hepatitis B virus(HBV),and more than 80% of HCC in China are related to HBV infection.Some special indicators used for the differential diagnosis of HCC,such as AFP-L3 and DCP,as well as other indicators,can not be routinely carried out in many medical institutions and physical examination people.Therefore,in this study,we tried to establish diagnosis models of HCC based on multi-dimensional clinical laboratory data to distinguish HCC from benign liver diseases,as well as HCC from non-HCC,and use multi-center data to validate the diagnostic efficacy of the established models.From 2015 to 2019,16516 subjects were recruited from five hospitals,including HCC and non-HCC control groups.Binary logistic regression was used to analyze the data of 50 clinical dimensions.The independent variables were screened by single factor analysis,and then the significant independent variables were stepwise analyzed by multi factor logistic regression.The independent risk factors were determined by stepwise regression,and the corresponding logistic regression model was constructed.Finally,three special auxiliary diagnostic models LAD,C-GALAD and GAPTALAD were established.The sensitivity,specificity and accuracy of these models were evaluated respectively,and the effectiveness of the models was verified in the validation cohort.In the training cohort and validation cohort,the area under the curve of the three differential diagnosis models were greater than 0.9,and the GAPTALAD model was as high as 0.973.To further analyze the risk factors related to routine physical examination indexes and HCC.Finally,Gender,Age,NEU,PLT,TBIL,ALB and lg AFP were associated with HCC.GATANAP model was established and the nomogram was drawn.In the training and validation cohort,the area under the ROC curve of GATANAP model were 0.917 [95% CI(0.912-0.922)] and 0.931[95% CI(0.923-0.939)] respectively.The AUC of AFP in differentiating HCC and nonHCC patients in the training cohort and validation cohort were 0.864 [95% CI(0.858-0.871)] and 0.835 [95% CI(0.822-0.849)] respectively.The results showed that the auxiliary diagnostic efficiency of the model was significantly higher than that of AFP.In the training cohort,the sensitivity,specificity and area under the curve(AUC)of GATANAP model in distinguishing AFP-negative HCC from benign liver diseases(BLDs)were 53.8%,87.9% and 0.830 [95% CI(0.821-0.840)] respectively,auxiliary diagnostic performance is confirmed in the validation cohort.In the longitudinal follow-up cohort,the OS time after hepatectomy in high-risk patients(29.58 ± 13.46 months)was shorter than that in low-risk patients(31.15 ± 12.69 months).At the same time,RFS in high-risk group was also poor.The results show that GATANAP model has potential prognostic value for hepatocellular carcinoma.The consistency of GATANAP model in the external validation set was more than 70%,and the highest was 91.7%.The parameters of GATANAP are all routine physical examination indexes,and this model has strong applicability for routine physical examination population and some medical institutions that cannot carry out triple HCC examination.The three special models include AFP-L3,AFP and DCP.The GAPTALAD model with the most parameters has the highest diagnostic efficiency,and the LAD model with the least parameters is the most convenient to use.Each user can choose the appropriate model according to their own conditions and information integration ability.Part Ⅲ: Establishment and application of MVI prediction model for HCCMicrovascular invasion(MVI)is a marker of invasive biological behavior of hepatocellular carcinoma,which has important predictive value for the prognosis of hepatocellular carcinoma,and is an important pathological indicator of postoperative recurrence of hepatocellular carcinoma.The previous attempt is to predict whether MVI occurs in HCC patients with GALAD model,but the prediction efficiency is not ideal,This part attempts to establish a multi parameter model based on laboratory data to predict the occurrence of MVI in HCC patients.From 2015 to 2017,5602 HCC patients were included as the training cohort,and 1905 HCC patients in 2018 were included as the validation cohort.Gender,age and preoperative 48 dimensional data were analyzed.In the training cohort,logistic regression analysis was used to evaluate the risk factors of MVI in patients with HCC before operation,and a combined diagnostic model was established.PA-SALAD=0.220-0.002*PLT-0.019*AGE+0.016*SA-0.029*ALB+0.012*AFP-L3+0.204*lg AFP+0.308*lg DCP,and independently validated in the validation cohort.The receiver operating characteristic(ROC)curve was used to evaluate the effectiveness of PA-SALAD model in diagnosing preoperative MVI in patients with HCC.When the optimal cut-off value was-0.2353,in the training cohort,the area under the curve(AUC)with MVI diagnosed by PA-SALAD model was 0.695.In the validation cohort,with the same optimal cut-off value,the AUC of MVI diagnosed by the model was 0.704.When discriminating M0 and M2,the AUC was 0.752.It can be seen that the microvascular invasion prediction model based on clinical test data can better predict the occurrence of microvascular invasion,especially high-risk MVI.The risk of microvascular invasion was stratified to provide the basis for individualized treatment of HCC patients with microvascular invasion.Part Ⅳ: Expression of DCP in hepatocellular carcinoma and its effect on hepatocellular carcinoma cells in vitroThe three parts above of clinical big data studies suggest that DCP is an important weight indicator for HCC auxiliary diagnosis and MVI prediction.Therefore,this part further studies the DCP protein level and cell function in vitro,mainly studying the expression of DCP in liver cancer tissues,through immunoblotting(Western Blot,WB)experimentally analyzes the expression of DCP in tumor thrombus tissue,liver cancer tissue and paired adjacent tissues to provide a basis for the value of DCP detection in the clinical application of HCC patients.A total of 10 pairs of HCC patients with tumor thrombus tissue,liver cancer tissue and adjacent tissues of the relative internal control of the expression of DCP protein were analyzed,and the results showed that there was no significant difference among the three(P >0.05).We made tissue microarray from 90 pairs of surgically resected and pathologically diagnosed hepatocellular carcinoma and paired adjacent tissues,stained the tissue microarray,scanned the microarray with ZEISS microscope and generated scanning images,analyzed the immunohistochemical staining results of tissue microarray,judged the staining intensity and positive rate of each tissue sample,and defined the staining index as the product of staining intensity and positive rate.The staining index was 0.05(0.03,0.08)in HCC tissues and 0.08(0.05,0.11)in paracancerous tissues.The DCP staining index in paracancerous tissues was significantly higher than that in cancerous tissues(P<0.001).The value of lg DCP in DCP IHC(-)patients was 2.22(1.80,2.64),and that in DCP IHC(+)patients was 3.27(2.90,3.65).The value of lg DCP in DCP IHC(+)patients was significantly higher than that in DCP IHC(-)patients(P < 0.001).The value of lg DCP in DCP IHC(-)patients and DCP IHC(+)patients was 2.67(2.12,3.22)and 3.10(2.71,3.50),respectively.The value of lg DCP in DCP IHC(+)patients was also significantly higher than that in DCP IHC(-)patients(P < 0.001).In order to further explore the role of DCP in the occurrence and development of HCC,we analyzed the relationship between DCP staining index and pathological characteristics of cancer tissues and adjacent tissues.It was found that DCP staining index had no difference in the presence or absence of capsule,vascular invasion,portal vein tumor thrombus,tumor size,satellite focus,adjacent organ invasion,TNM stage and Edmondson grade.68 patients with HCC were followed up for 46.7 ± 35.9months.The study showed that there was no difference in survival time between DCP IHC(+)and DCP IHC(-)in cancer tissues,DCP IHC(+)and DCP IHC(-)in paracancerous tissues,and between negative and positive DCP in blood(P>0.05).Further study of cell function suggested that DCP could promote the migration of Hep G2 and PLC/PRF/5 cells,and promote the proliferation of Hep G2 and PLC/PRF/5 cells.After 72 hours of incubation,the wound healing rate of Hep G2 cells increased from 50.5% to 78%,and that of PLC/PRF/5 cells increased from 45% to 78%.DCP promoted the proliferation of Hep G2 cells,and the proliferation rate reached the plateau at 48 h.There was a dose effect relationship at different DCP concentrations(5-320 m AU/m L),the proliferation rates were40.0%,70.5%,92.4%,106.0%,116.2%,134.3% and 148.1%,respectively.DCP could also promote the proliferation of PLC/PRF/5 cells,and the proliferation rate reached the plateau at 96 h.At different DCP concentrations(5-320 m AU/m L),the proliferation rates were32.9%,46.9%,63.6%,74.2%,87.4%,96.2% and 117.9%,respectively.There was also a dose effect relationship.In summary,this study first validated the auxiliary diagnosis of HCC by the GALAD model,and tried to analyze the predictive value of the GALAD model for MVI.Furthermore,on the basis of the above work,we built the auxiliary diagnosis and MVI prediction model of HCC according to the etiological characteristics of HCC in Chinese.It is found that our self built model can greatly improve the auxiliary diagnosis of HCC and the prediction efficiency of MVI,which is suitable for the real scene of selecting different detection indicators.Clinical big data research suggests that DCP is an important weight indicator for HCC diagnosis and MVI prediction.Further studies on DCP protein level and cell function in vitro have found that DCP can promote the proliferation and migration of HCC cells in vitro. |
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