LncRNA FUNDC2P4 down-regulation promotes epithelial-mesenchymal transition by reducing E-cadherin expression in residual hepatocellular carcinoma after insufficient radiofrequency ablation

The implications for urological malignancies of non-coding RNAs in the the tumor microenvironment

Urological malignancies are a major global health issue because of their complexity and the wide range of ways they affect patients. There's a growing need for in-depth research into these cancers, especially at the molecular level. Recent studies have highlighted the importance of non-coding RNAs (ncRNAs) – these don't code for proteins but are crucial in controlling genes – and the tumor microenvironment (TME), which is no longer seen as just a background factor but as an active player in cancer progression. Understanding how ncRNAs and the TME interact is key for finding new ways to diagnose and predict outcomes in urological cancers, and for developing new treatments. This article reviews the basic features of ncRNAs and goes into detail about their various roles in the TME, focusing specifically on how different ncRNAs function and act in urological malignancies.

Progression of hepatocellular carcinoma after radiofrequency ablation: Current status of research

Hepatocellular carcinoma (HCC) remains an important disease for health care systems in view of its high morbidity, mortality, and increasing incidence worldwide. Radiofrequency ablation (RFA) is preferred to surgery as a local treatment for HCC because it is safer, less traumatic, less painful, better tolerated, causes fewer adverse reactions, and allows more rapid postoperative recovery. The biggest shortcoming of RFA when used to treat HCC is the high incidence of residual tumor, which is often attributed to the vascular thermal deposition effect, the wide infiltration zone of peripheral venules, and the distance between satellite foci and the main focus of the cancer. Recurrence and progression of the residual tumor is the most important determinant of the prognosis. Therefore, it is important to be aware of the risk of recurrence and to improve the efficacy of RFA. This review summarizes the relevant literature and the possible mechanisms involved in progression of HCC after RFA. Current studies have demonstrated that multimodal treatments which RFA combined with other anti-cancer approaches can prevent progression of HCC after RFA.

Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system

Objectives

Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.

Methods

The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model.

Results

Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis.

Conclusions

The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04657-3.

An Overview of Hepatocellular Carcinoma After Insufficient Radiofrequency Ablation

Radiofrequency ablation (RFA) is a commonly used treatment for hepatocellular carcinoma (HCC), however, various complex conditions in clinical practice may lead to insufficient radiofrequency ablation (IRFA), allowing residual HCC to survive. In clinical practice and laboratory models, IRFA plays an important role in rapid tumor progression. Therefore, targeting the residual HCC and avoiding IRFA were worthwhile methods. A deeper understanding of IRFA is required; IRFA contributes to the improvement of proliferative activity, migration rates, and invasive capacity, and this may be due to the involvement of multiple complex processes or proteins, including epithelial mesenchymal transitions (EMTs), cancer stem cells (CSCs), autophagy, heat shock proteins (HSPs), changes of non-tumor cells and extracellular matrix, altered immune microenvironment, hypoxia-inducible factors (HIFs), growth factors, epigenetic alterations, and metabolic reprogramming. We focus on the processes of the above mechanisms and possible therapeutic approach, with a review of the literature. Additionally, we recapitulated the construction methods of various experimental models of IRFA (in vivo and in vitro).

lncENST Suppress the Warburg Effect Regulating the Tumor Progress by the Nkx2-5/ErbB2 Axis in Hepatocellular Carcinoma

The therapeutic efficacy of radiofrequency ablation (RFA) against liver cancer is often limited by proliferation and metastasis of residual tumor cells. These phenomena are closely associated with the Warburg effect, wherein ErbB2 is activated. While RFA inhibits the Warburg effect of residual tumor cells at the early stage, the specific mechanisms remain unclear. We explored the regulatory relationship between the long noncoding RNA ENST00000570843.1 (lncENST) and ErbB2 using lentiviral transfection of lncENST and ErbB2 overexpression/interference vectors in in vitro and in vivo models of hepatocellular carcinoma in the presence of sublethal heat at 50°C. ErbB2-mediated Warburg effect was suppressed by lncENST, as manifested by reduced glucose uptake and lactic acid production in SMMC-7721 cells. lncENST also increased tumor apoptosis and inhibited tumor progression in nude Balb/c mice for up to 28 days after RFA. Additionally, we predicted through bioinformatic analysis that the promoter of ErbB2 binds to the transcription factor Nkx2-5, resulting in a negative regulatory effect. This speculation was confirmed by chromatin immunoprecipitation of the Nkx2-5 protein and ErbB2, indicating that ErbB2 transcription was curbed by Nkx2-5. We propose that lncENST downplays the Warburg effect in residual tumor cells by downregulating ErbB2 via Nkx2-5 activation. This study is aimed at providing molecular targets that can prevent residual tumor cell proliferation after RFA, with clinical significance in hepatocellular carcinoma treatment.

Potential Prognostic Immune Biomarkers of Overall Survival in Ovarian Cancer Through Comprehensive Bioinformatics Analysis: A Novel Artificial Intelligence Survival Prediction System

Background: The tumour immune microenvironment plays an important role in the biological mechanisms of tumorigenesis and progression. Artificial intelligence medicine studies based on big data and advanced algorithms are helpful for improving the accuracy of prediction models of tumour prognosis. The current research aims to explore potential prognostic immune biomarkers and develop a predictive model for the overall survival of ovarian cancer (OC) based on artificial intelligence algorithms.

Methods: Differential expression analyses were performed between normal tissues and tumour tissues. Potential prognostic biomarkers were identified using univariate Cox regression. An immune regulatory network was constructed of prognostic immune genes and their highly related transcription factors. Multivariate Cox regression was used to identify potential independent prognostic immune factors and develop a prognostic model for ovarian cancer patients. Three artificial intelligence algorithms, random survival forest, multitask logistic regression, and Cox survival regression, were used to develop a novel artificial intelligence survival prediction system.

Results: The current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes between tumour samples and normal samples. Further univariate Cox regression identified 84 prognostic immune gene biomarkers for ovarian cancer patients in the model dataset (GSE32062 dataset and GSE53963 dataset). An immune regulatory network was constructed involving 63 immune genes and 5 transcription factors. Fourteen immune genes (PSMB9, FOXJ1, IFT57, MAL, ANXA4, CTSH, SCRN1, MIF, LTBR, CTSD, KIFAP3, PSMB8, HSPA5, and LTN1) were recognised as independent risk factors by multivariate Cox analyses. Kaplan-Meier survival curves showed that these 14 prognostic immune genes were closely related to the prognosis of ovarian cancer patients. A prognostic nomogram was developed by using these 14 prognostic immune genes. The concordance indexes were 0.760, 0.733, and 0.765 for 1-, 3-, and 5-year overall survival, respectively. This prognostic model could differentiate high-risk patients with poor overall survival from low-risk patients. According to three artificial intelligence algorithms, the current study developed an artificial intelligence survival predictive system that could provide three individual mortality risk curves for ovarian cancer.

Conclusion: In conclusion, the current study identified 1,307 differentially expressed genes and 337 differentially expressed immune genes in ovarian cancer patients. Multivariate Cox analyses identified fourteen prognostic immune biomarkers for ovarian cancer. The current study constructed an immune regulatory network involving 63 immune genes and 5 transcription factors, revealing potential regulatory associations among immune genes and transcription factors. The current study developed a prognostic model to predict the prognosis of ovarian cancer patients. The current study further developed two artificial intelligence predictive tools for ovarian cancer, which are available at https://zhangzhiqiao8.shinyapps.io/Smart_Cancer_Survival_Predictive_System_17_OC_F1001/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_17_OC_F1001/. An artificial intelligence survival predictive system could help improve individualised treatment decision-making.

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Background

Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients.

Methods

Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model.

Results

There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients.

Conclusion

The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system

The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer.

Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system.

Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score.

The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.

Percutaneous ethanol injection enhanced the efficacy of radiofrequency ablation in the treatment of HCC: an insight into the mechanism of ethanol action

PURPOSE

To discuss the possible reasons why percutaneous intratumoral ethanol injection (PEI) combined with radiofrequency ablation (RFA) to treat hepatocellular carcinoma (HCC) reduced the recurrence and metastasis compared with RFA alone.

MATERIALS AND METHODS

Forty VX2 tumor-bearing rabbits were randomly divided into four groups (n = 10): the PEI, RFA, PEI-RFA, and control groups. Five rabbits from each group were sacrificed on the 3rd and 7th days after ablation. The number of metastatic tumors in the lung was counted. The ablation volume was measured, and residual tumor specimens were prepared for hematoxylin and eosin staining and caspase-3, Ki-67, and VEGF immunohistochemical staining.

RESULTS

The volume of ablation in the PEI-RFA group was significantly larger than that in the RFA and PEI groups (p < 0.05). However, no significant differences in the number of lung metastases after ablation were observed among the groups (p > 0.05). The number of microthrombi in the PEI-RFA group was greater than that in the control and RFA groups (p < 0.001 and p < 0.05). The Ki-67 labeling index (LI) and H-score of VEGF in the PEI-RFA group were lower than those in the RFA group, while the H-score of caspase-3 was higher than that in the RFA group on the 7th day after ablation (p < 0.05).

CONCLUSION

PEI occluded blood vessels by inducing microthrombi formation, and thereby reducing heat dissipation and increasing the effect of RFA. More importantly, in comparison with an incomplete RFA, PEI-RFA inhibited the increase in the Ki-67 and VEGF expression levels and the decrease in the caspase-3 expression level to happen at some extent and therefore improved the prognosis.

SP1-induced upregulation of lncRNA CTBP1-AS2 accelerates the hepatocellular carcinoma tumorigenesis through targeting CEP55 via sponging miR-195-5p

As reported in many research, LncRNA CTBP1 divergent transcript (CTBP1-AS2) remarkably affects the progression of several tumors. However, the precise role and function of CTBP1-AS2 in hepatocellular carcinoma (HCC) remained unknown. We found that CTBP1-AS2 expressions were increased in HCC samples and cells. After treatment with microwave ablation (MWA), CTBP1-AS2 was distinctly up-regulated in residual HCC tissues compared with HCC samples. CTBP1-AS2 was upregulated under the induction of the nuclear transcription factor SP1. As revealed by the clinical assays, high CTBP1-AS2 expression usually related to lymph node metastasis, clinical stage and weaker prognosis specific to HCC patients. Functionally, CTBP1-AS2 knockdown suppressed HCC cells in terms of the proliferation, migration, invasion, chemotherapy resistance as well as EMT progress, but promoted apoptosis. Mechanistically, CTBP1-AS2 was a sponge of miR-195-5p for elevating CEP55 expression, a target of miR-195-5p, and thereby exhibited its oncogenic roles in HCC progression. Overall, an emerging regulatory mechanism of SP1/CTBP1-AS2/miR-195-5p/CEP55 axis was reported in the paper, which possibly served as a new therapeutic HCC treatment target.

c-Met/MAPK pathway promotes the malignant progression of residual hepatocellular carcinoma cells after insufficient radiofrequency ablation

Radiofrequency ablation (RFA) is popularly used in the treatment of hepatocellular carcinoma (HCC). However, the accelerated malignant progression of residual HCC cells after RFA is the main obstacle for the application of this technology in HCC treatment. In the present study, HepG2 cells, an established human HCC cell line, experienced repeatedly with heat treatment, survived cells, HepG2-H cells, were used to simulate residual HCC cells after RFA. The abilities of proliferation, colony formation, and migration were compared between HepG2 and HepG2-H cells. Then, RNA sequencing was used to explore the difference in genes expression between two groups of cells. Subsequently, the level of c-Met, one of membranous receptors of MAPK signal pathway, was measured by RT-qPCR and western blot; the effect of c-Met inhibition on the malignant progression of HepG2-H cells was evaluated. The results showed that HepG2-H cells exhibited higher abilities in the proliferation, colony formation, and migration than that of HepG2 cells. Moreover, differentially expressed genes between two groups of cells were prominently enriched in MAPK signal pathway. The level of c-Met in HepG2-H cells was significantly higher than that in HepG2 cells, and the inhibition in the activity of c-Met could repress the malignant behaviors of HepG2-H cells. These results indicated that the accelerated malignant progression of residual HCC cells after RFA can be partly attributed to the overexpression of c-Met and the activation of MAPK signal pathway. Therefore, we proposed that RFA followed by c-Met inhibitor intake maybe is a better treatment protocol for HCC.

GAS6-AS2 Promotes Hepatocellular Carcinoma via miR-3619-5p/ARL2 Axis Under Insufficient Radiofrequency Ablation Condition

Background: Hepatocellular carcinoma (HCC) is a common malignancy worldwide. Radiofrequency ablation (RFA) is applied for treating HCC; however, insufficient RFA promotes HCC development and accelerates HCC recurrence. Therefore, the molecular functions underlying this process have gradually attracted attention. Aim of the study: We sought to examine whether GAS6-AS2 (also known as GAS6-DT: growth arrest specific 6 divergent transcript) played a role in the development of HCC after insufficient RFA. Methods: The in vitro model was established by heating Huh7 and MHCC97 cells in water bath at 47°C, named as Huh7-H and MHCC97-H. Colony formation, transwell and western blot assays were conducted for functional analysis. Results: GAS6-AS2 was upregulated in Huh7-H and MHCC97-H cells relative to Huh7 and MHCC97 cells. GAS6-AS2 deficiency hampered cell proliferation, migration, invasion, epithelial-mesenchymal transition, and stemness in Huh7-H and MHCC97-H cells. Moreover, microRNA-3619-5p (miR-3619-5p) combined with GAS6-AS2 and ARL2 (ADP ribosylation factor-like GTPase 2) was the target gene of miR-3619-5p. GAS6-AS2 served as the competing endogenous RNA (ceRNA) of ARL2 via absorbing miR-3619-5p. Conclusion: On the whole, present study uncovered a novel ceRNA mechanism of GAS6-AS2/miR-3619-5p/ARL2 in HCC after insufficient RFA, which might shed a new insight into treatment of HCC after insufficient RFA.

Exosome-transferred long non-coding RNA ASMTL-AS1 contributes to malignant phenotypes in residual hepatocellular carcinoma after insufficient radiofrequency ablation

Objectives

Long non‐coding RNAs (lncRNAs) are emerging RNA regulators in cancer progression, including in hepatocellular carcinoma (HCC). Recently, insufficient radiofrequency ablation (RFA) has been reported to lead to recurrence and metastasis of residual HCC tumours. Herein, we aimed to the role of ASMTL‐AS1 in residual HCC after insufficient RFA.

Materials and methods

In vitro insufficient RFA model was simulated in Huh7 cells and subsequently named Huh7‐H cells. In vitro and in vivo assays were conducted to investigate ASMTL‐AS1 function in HCC.

Results

LncRNA ASMTL‐AS1 low expressed in normal human liver was found to be highly expressed in HCC tissues and further increased in tumours after insufficient RFA. ASMTL‐AS1 expression was related to stage, metastasis and prognosis in HCC. Huh7‐H possessed higher ASMTL‐AS1 level and more aggressive than Huh7 cells. ASMTL‐AS1 contributed to the malignancy of HCC cells both in vitro and in vivo. Mechanistically, ASMTL‐AS1 was trans‐activated by MYC and promoted NLK expression to activate YAP signalling via sequestering miR‐342‐3p in HCC. Interestingly, ASMTL‐AS1 could be wrapped by exosomes and then convey malignancy through NLK/YAP axis between cells even in residual HCC after insufficient RFA.

Conclusions

Exosomal ASMTL‐AS1 aggravates the malignancy in residual HCC after insufficient RFA via miR‐342‐3p/NLK/YAP signalling, opening a new road for the treatment of HCC and the prevention of recurrence or metastasis of residual HCC after insufficient RFA.

ATPase Inhibitory Factor 1 Promotes Hepatocellular Carcinoma Progression After Insufficient Radiofrequency Ablation, and Attenuates Cell Sensitivity to Sorafenib Therapy

Epithelial-mesenchymal transition (EMT) and angiogenesis is involved in tumor progression after radiofrequency ablation (RFA). ATPase inhibitory factor 1 (IF1) is a bad predictor of prognosis. Sorafenib inhibited EMT of hepatocellular carcinoma (HCC) after RFA. Whether IF1 promotes the EMT and angiogenesis of HCC and attenuates the effect of sorafenib after insufficient RFA is investigated. In this study, higher expression of IF1 was found in residual tumor after insufficient RFA. Hep3B or Huh7 cells after insufficient RFA were designated as Hep3B-H or Huh7-H cells in vitro. Hep3B-H or Huh7-H cells exhibited enhanced capacities of colony formation, migration, and increased expression of EMT associated markers and IF1 compared with Hep3B or Huh7 cells. IF1 knockdown in Hep3B-H or Huh7-H cells decreased the colony formation and migratory capacity, and IF1 overexpression in Hep3B or Huh7 cells increased these capacities. IF1 in HCC cells directly and indirectly affected angiogenesis of TAECs after insufficient RFA. IF1 promoted HCC cells growth and metastasis after insufficient RFA. IF1 increased HCC cells resistance after insufficient RFA to sorafenib. Higher IF1 expression indicated poor disease survival in HCC patients after sorafenib therapy. NF-κB activation induced by IF1 attenuated the effect of sorafenib on HCC cells after insufficient RFA. Our results demonstrated that IF1 promotes the EMT and angiogenesis, and attenuates HCC cell sensitivity to sorafenib after insufficient RFA through NF-κB signal pathway.

Bioinformatics Identified 17 Immune Genes as Prognostic Biomarkers for Breast Cancer: Application Study Based on Artificial Intelligence Algorithms

An increasing body of evidence supports the association of immune genes with tumorigenesis and prognosis of breast cancer (BC). This research aims at exploring potential regulatory mechanisms and identifying immunogenic prognostic markers for BC, which were used to construct a prognostic signature for disease-free survival (DFS) of BC based on artificial intelligence algorithms. Differentially expressed immune genes were identified between normal tissues and tumor tissues. Univariate Cox regression identified potential prognostic immune genes. Thirty-four transcription factors and 34 immune genes were used to develop an immune regulatory network. The artificial intelligence survival prediction system was developed based on three artificial intelligence algorithms. Multivariate Cox analyses determined 17 immune genes (ADAMTS8, IFNG, XG, APOA5, SIAH2, C2CD2, STAR, CAMP, CDH19, NTSR1, PCDHA1, AMELX, FREM1, CLEC10A, CD1B, CD6, and LTA) as prognostic biomarkers for BC. A prognostic nomogram was constructed on these prognostic genes. Concordance indexes were 0.782, 0.734, and 0.735 for 1-, 3-, and 5- year DFS. The DFS in high-risk group was significantly worse than that in low-risk group. Artificial intelligence survival prediction system provided three individual mortality risk predictive curves based on three artificial intelligence algorithms. In conclusion, comprehensive bioinformatics identified 17 immune genes as potential prognostic biomarkers, which might be potential candidates of immunotherapy targets in BC patients. The current study depicted regulatory network between transcription factors and immune genes, which was helpful to deepen the understanding of immune regulatory mechanisms for BC cancer. Two artificial intelligence survival predictive systems are available at https://zhangzhiqiao7.shinyapps.io/Smart_Cancer_Survival_Predictive_System_16_BC_C1005/ and https://zhangzhiqiao8.shinyapps.io/Gene_Survival_Subgroup_Analysis_16_BC_C1005/. These novel artificial intelligence survival predictive systems will be helpful to improve individualized treatment decision-making.

Two precision medicine predictive tools for six malignant solid tumors: from gene-based research to clinical application

Background

The current study aimed to construct competing endogenous RNA (ceRNA) regulation network and develop two precision medicine predictive tools for colorectal cancer (CRC).

Methods

Differentially expressed (DE) analyses were performed between CRC tissues and normal tissues. A ceRNA regulation network was constructed based on DElncRNAs, DEmiRNAs, and DEmRNAs.

Results

Fifteen mRNAs (ENDOU, MFN2, FASLG, SHOC2, VEGFA, ZFPM2, HOXC6, KLK10, DDIT4, LPGAT1, BEX4, DENND5B, PHF20L1, HSP90B1, and PSPC1) were identified as prognostic biomarkers for CRC by multivariate Cox regression. Then a Fifteen-mRNA signature was developed to predict overall survival for CRC patients. Concordance indexes were 0.817, 0.838, and 0.825 for 1-, 2- and 3-year overall survival. Patients with high risk scores have worse OS compared with patients with low risk scores.

Conclusion

The current study provided deeper understanding of prognosis-related ceRNA regulatory network for CRC. Two precision medicine predictive tools named Smart Cancer Survival Predictive System and Gene Survival Analysis Screen System were constructed for CRC. These two precision medicine predictive tools can provide valuable precious individual mortality risk prediction before surgery and improve the individualized treatment decision-making.

Two predictive precision medicine tools for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is a serious threat to public health due to its poor prognosis. The current study aimed to develop and validate a prognostic nomogram to predict the overall survival of HCC patients.

Methods

The model cohort consisted of 24,991 mRNA expression data points from 348 HCC patients. The least absolute shrinkage and selection operator method (LASSO) Cox regression model was used to evaluate the prognostic mRNA biomarkers for the overall survival of HCC patients.

Results

Using multivariate Cox proportional regression analyses, a prognostic nomogram (named Eight-mRNA prognostic nomogram) was constructed based on the expression data of N4BP3, -ADRA2B, E2F8, MAPT, PZP, HOXD9, COL15A1, and -NDST3. The C-index of the Eight-mRNA prognostic nomogram was 0.765 (95% CI 0.724-0.806) for the overall survival in the model cohort. The Harrell's concordance-index of the Eight-mRNA prognostic nomogram was 0.715 (95% CI 0.658-0.772) in the validation cohort. The survival curves demonstrated that the HCC patients in the high risk group had a significantly poorer overall survival than the patients in the low risk group.

Conclusion

In the current study, we have developed two convenient and efficient predictive precision medicine tools for hepatocellular carcinoma. These two predictive precision medicine tools are helpful for predicting the individual mortality risk probability and improving the personalized comprehensive treatments for HCC patients. The Smart Cancer Predictive System can be used by clicking the following URL: https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_HCC_2/. The Gene Survival Analysis Screen System is available at the following URL: https://zhangzhiqiao5.shinyapps.io/Gene_Survival_Analysis_A1001/.

The competitive endogenous RNA regulatory network reveals potential prognostic biomarkers for overall survival in hepatocellular carcinoma

The aim of the present study is to construct a competitive endogenous RNA (ceRNA) regulatory network by using differentially expressed long noncoding RNAs (lncRNAs), microRNAs (miRNAs), and mRNAs in patients with hepatocellular carcinoma (HCC), and to construct a prognostic model for predicting overall survival (OS) of HCC patients. Differentially expressed lncRNAs, miRNAs, and mRNAs were explored between HCC tissues and normal liver tissues. A prognostic model was built for predicting OS of HCC patients and receiver operating characteristic curves were used to evaluate the performance of the prognostic model. There were 455 differentially expressed lncRNAs, 181 differentially expressed miRNAs, and 5035 differentially expressed mRNAs. A ceRNA regulatory network was constructed based on 43 lncRNAs, 37 miRNAs, and 105 mRNAs. Eight mRNA biomarkers (H2AFX, SQSTM1, ITM2A, PFKP, TPD52L1, ACSL4, STRN3, and CPEB3) were identified as independent risk factors by multivariate Cox regression and were used to develop a prognostic model for OS. The C-indexes in the model group were 0.776 (95% confidence interval [CI], 0.730-0.822), 0.745 (95% CI, 0.699-0.791), and 0.789 (95% CI, 0.743-0.835) for 1-, 3-, and 5-year OS, respectively. The current study revealed potential molecular biological regulation pathways and prognostic biomarkers by the ceRNA regulatory network. A prognostic model based on prognostic mRNAs in the ceRNA network might be helpful to predict the individual mortality risk for HCC patients. The individual mortality risk calculator can be used by visiting the following URL: https://zhangzhiqiao.shinyapps.io/Smart_cancer_predictive_system_HCC/.

Insufficient radiofrequency ablation promotes the metastasis of residual hepatocellular carcinoma cells via upregulating flotillin proteins

Purpose

Radiofrequency ablation (RFA) therapy has proven to be effective and feasible for early-stage hepatocellular carcinoma (HCC); however, rapid progression of residual tumor cells after RFA has been confirmed, but the molecular mechanisms of this phenomenon are poorly understood. This study evaluated the effect of the lipid raft proteins known as flotillins on the invasive and metastatic potential of residual HCC.

Methods

The human HCC cell line HCCLM3 was used to establish insufficient RFA models in vivo and in vitro. Changes in cellular morphology, soft agar colony formation, motility, metastasis, and epithelial–mesenchymal transition (EMT) markers after insufficient RFA intervention in vitro and in vivo were detected by real-time PCR, western blotting, immunohistochemistry and transwell assays.

Results

The results showed that flotillin-1 and flotillin-2 expression were upregulated in HCCLM3 cells following 45 °C heat treatment and in residual HCCLM3 xenografts cells after insufficient RFA. Knocking down flotillin-1 or flotillin-2 in HCCLM3 cells by shRNA significantly lowered insufficient RFA-induced tumor growth, EMT changes, and metastasis in vitro and in vivo. Furthermore, mechanism studies indicated that flotillins altered the EMT status and metastatic potential of heat-treated HCCLM3 cells by activating the Akt/Wnt/β-catenin signaling pathway.

Conclusions

Our findings present new evidence that flotillins play a key role in the aggressive behaviors of residual cancer cells after insufficient RFA and provide new insights into the regulatory mechanism of Wnt/β-catenin signaling.

Electronic supplementary material

The online version of this article (10.1007/s00432-019-02852-z) contains supplementary material, which is available to authorized users.

The emergence of long non-coding RNAs in hepatocellular carcinoma: an update

Hepatocellular carcinoma (HCC) accounting for roughly 90% of all primary liver neoplasms is the sixth most frequent neoplasm and the second prominent reason of tumor fatality worldwide. As regulators of diverse biological processes, long non-coding RNAs (lncRNAs) are involved in onset and development of neoplasms. With the continuous booming of well-featured lncRNAs in HCC from 2016 to now, we reviewed the newly-presented comprehension about the relationship between lncRNAs and HCC in this study. To be specific, we summarized the overview function and study tools of lncRNAs, elaborated the roles of lncRNAs in HCC, and sketched the molecule mechanisms of lncRNAs in HCC. In addition, the application of lncRNAs serving as biomarkers in early diagnosis and outcome prediction of HCC patients was highlighted.

A new apatinib microcrystal formulation enhances the effect of radiofrequency ablation treatment on hepatocellular carcinoma

Introduction

Radiofrequency ablation (RFA) is the foremost treatment option for advanced hepatocellular carcinoma (HCC), however, rapid and aggressive recurrence of HCC often occurs after RFA due to epithelial–mesenchymal transition process. Although combination of RFA with sorafenib, a molecular targeted agent, could attenuate the recurrence of HCC, application of this molecular targeted agent poses a heavy medical burden and oral administration of sorafenib also brings severe side effects.

Materials and methods

In this study, we prepared an apatinib microcrystal formulation (Apa-MS) that sustainably releases apatinib, a novel molecular targeted agent, for advanced HCC treatment. We injected apatinib solution or Apa-MS into subcutaneous HCC tumors.

Results

It was found that Apa-MS exhibited slow apatinib release in vivo and in turn inhibited the epithelial–mesenchymal transition of HCC cells for extended time. Moreover, in rodent HCC model, Apa-MS enhanced the antitumor effect of RFA treatment.

Conclusion

Based on these results, we conclude that Apa-MS, a slow releasing system of apatinib, allows apatinib to remain effective in tumor tissues for a long time and could enhance the antitumor effect of RFA on HCC.