Normal tissue adjacent to tumor expression profile analysis developed and validated a prognostic model based on Hippo-related genes in hepatocellular carcinoma

Integrated clinical and prognostic analyses of mTOR/Hippo pathway core genes in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most aggressive and dismal cancers globally. Emerging evidence has established that mTOR and Hippo pathways are oncogenic drivers of HCC. However, the prognostic value of these pathways in HCC remains unclear. In this study, we aimed to develop a gene signature utilizing the mTOR/Hippo genes for HCC prognostication. A multiple stage strategy was employed to screen, and a 12-gene signature based on mTOR/Hippo pathways was constructed to predict the prognosis of HCC patients. The risk scores calculated by the signature were inversely correlated with patient prognosis. Validation of the signature in independent cohort confirmed its predictive power. Further analysis revealed molecular differences between high and low-risk groups at genomic, transcriptomic, and protein-interactive levels. Moreover, immune infiltration analysis revealed an immunosuppressive state in the high-risk group. Finally, the gene signature could predict the sensitivity to current chemotherapeutic drugs. This study demonstrated that combinatorial mTOR/Hippo gene signature was a robust and independent prognostic tool for survival prediction of HCC. Our findings not only provide novel insights for the molecular understandings of mTOR/Hippo pathways in HCC, but also have important clinical implications for guiding therapeutic strategies.

PGK1 is a potential biomarker for early diagnosis and prognosis of hepatocellular carcinoma

Hepatocellular carcinoma (HCC), a common type of liver cancer, is increasing in incidence worldwide. An early diagnosis of hepatocellular carcinoma (HCC) is still challenging: Currently, few biomarkers are available to diagnose the early stage of HCC, therefore, additional prognostic biomarkers are required to identify potential risk factors. The present study analyzed gene expression levels of HCC tissue samples and the protein expression levels obtained from the GSE46408 HCC dataset using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The metabolically associated differentially expressed genes (DEGs), including DEGs involved in the glucose metabolism pathway, were selected for further analysis. Phosphoglycerate kinase 1 (PGK1), a glycolytic enzyme, was determined as a potential prognostic biomarker through Kaplan-Meier curve and clinical association variable analyses. This was also verified based on the expression levels of PGK1 in tumor tissue and protein expression levels in several liver cancer cell lines. PGK1 mRNA demonstrated a high level of expression in HCC tissue and was significantly associated with a poor prognosis, showing a negative association with survival time. In addition, as an independent risk factor, PGK1 may potentially be a valuable prognostic biomarker for patients with HCC. Furthermore, expression of PGK1 was associated with the early stages (stage I and T1) of HCC. Moreover, PGK1 mRNA expression levels demonstrated a positive association with progression of liver cancer. The results suggested that PGK1 mRNA may be involved in the degree of HCC malignancy and may be a future potential prognostic biomarker for HCC progression.

Inflammation in the tumor-adjacent lung as a predictor of clinical outcome in lung adenocarcinoma

Approximately 30% of early-stage lung adenocarcinoma patients present with disease progression after successful surgical resection. Despite efforts of mapping the genetic landscape, there has been limited success in discovering predictive biomarkers of disease outcomes. Here we performed a systematic multi-omic assessment of 143 tumors and matched tumor-adjacent, histologically-normal lung tissue with long-term patient follow-up. Through histologic, mutational, and transcriptomic profiling of tumor and adjacent-normal tissue, we identified an inflammatory gene signature in tumor-adjacent tissue as the strongest clinical predictor of disease progression. Single-cell transcriptomic analysis demonstrated the progression-associated inflammatory signature was expressed in both immune and non-immune cells, and cell type-specific profiling in monocytes further improved outcome predictions. Additional analyses of tumor-adjacent transcriptomic data from The Cancer Genome Atlas validated the association of the inflammatory signature with worse outcomes across cancers. Collectively, our study suggests that molecular profiling of tumor-adjacent tissue can identify patients at high risk for disease progression.

Transcriptomes of the tumor-adjacent normal tissues are more informative than tumors in predicting recurrence in colorectal cancer patients

BACKGROUND

Previous investigations of transcriptomic signatures of cancer patient survival and post-therapy relapse have focused on tumor tissue. In contrast, here we show that in colorectal cancer (CRC) transcriptomes derived from normal tissues adjacent to tumors (NATs) are better predictors of relapse.

RESULTS

Using the transcriptomes of paired tumor and NAT specimens from 80 Korean CRC patients retrospectively determined to be in recurrence or nonrecurrence states, we found that, when comparing recurrent with nonrecurrent samples, NATs exhibit a greater number of differentially expressed genes (DEGs) than tumors. Training two prognostic elastic net-based machine learning models-NAT-based and tumor-based in our Samsung Medical Center (SMC) cohort, we found that NAT-based model performed better in predicting the survival when the model was applied to the tumor-derived transcriptomes of an independent cohort of 450 COAD patients in TCGA. Furthermore, compositions of tumor-infiltrating immune cells in NATs were found to have better prognostic capability than in tumors. We also confirmed through Cox regression analysis that in both SMC-CRC as well as in TCGA-COAD cohorts, a greater proportion of genes exhibited significant hazard ratio when NAT-derived transcriptome was used compared to when tumor-derived transcriptome was used.

CONCLUSIONS

Taken together, our results strongly suggest that NAT-derived transcriptomes and immune cell composition of CRC are better predictors of patient survival and tumor recurrence than the primary tumor.

A Microfluidic Device for Long-Term Maintenance of Organotypic Liver Cultures

Liver cultures may be used for disease modeling, testing therapies and predicting drug-induced injury. The complexity of the liver cultures has evolved from hepatocyte monocultures to co-cultures with non-parenchymal cells and finally to precision-cut liver slices. The latter culture format retains liver's native biomolecular and cellular complexity and therefore holds considerable promise for in vitro testing. However, liver slices remain functional for ~72 h in vitro and display limited utility for some disease modeling and therapy testing applications that require longer culture times. This paper describes a microfluidic device for longer-term maintenance of functional organotypic liver cultures. Our microfluidic culture system was designed to enable direct injection of liver tissue into a culture chamber through a valve-enabled side port. Liver tissue was embedded in collagen and remained functional for up to 31 days, highlighted by continued production of albumin and urea. These organotypic cultures also expressed several enzymes involved in xenobiotic metabolism. Conversely, matched liver tissue embedded in collagen in a 96-well plate lost its phenotype and function within 3-5 days. The microfluidic organotypic liver cultures described here represent a significant advance in liver cultivation and may be used for future modeling of liver diseases or for individualized liver-directed therapies.

Multi-region sequencing with spatial information enables accurate heterogeneity estimation and risk stratification in liver cancer

BACKGROUND

Numerous studies have used multi-region sampling approaches to characterize intra-tumor heterogeneity (ITH) in hepatocellular carcinoma (HCC). However, conventional multi-region sampling strategies do not preserve the spatial details of samples, and thus, the potential influences of spatial distribution on patient-wise ITH (represents the overall heterogeneity level of the tumor in a given patient) have long been overlooked. Furthermore, gene-wise transcriptional ITH (represents the expression pattern of genes across different intra-tumor regions) in HCC is also under-explored, highlighting the need for a comprehensive investigation.

METHODS

To address the problem of spatial information loss, we propose a simple and easy-to-implement strategy called spatial localization sampling (SLS). We performed multi-region sampling and sequencing on 14 patients with HCC, collecting a total of 75 tumor samples with spatial information and molecular data. Normalized diversity score and integrated heterogeneity score (IHS) were then developed to measure patient-wise and gene-wise ITH, respectively.

RESULTS

A significant correlation between spatial and molecular heterogeneity was uncovered, implying that spatial distribution of sampling sites did influence ITH estimation in HCC. We demonstrated that the normalized diversity score had the ability to overcome sampling location bias and provide a more accurate estimation of patient-wise ITH. According to this metric, HCC tumors could be divided into two classes (low-ITH and high-ITH tumors) with significant differences in multiple biological properties. Through IHS analysis, we revealed a highly heterogenous immune microenvironment in HCC and identified some low-ITH checkpoint genes with immunotherapeutic potential. We also constructed a low-heterogeneity risk stratification (LHRS) signature based on the IHS results which could accurately predict the survival outcome of patients with HCC on a single tumor biopsy sample.

CONCLUSIONS

This study provides new insights into the complex phenotypes of HCC and may serve as a guide for future studies in this field.

Classification of IHC Images of NATs With ResNet-FRP-LSTM for Predicting Survival Rates of Rectal Cancer Patients

BACKGROUND

Over a decade, tissues dissected adjacent to primary tumors have been considered "normal" or healthy samples (NATs). However, NATs have recently been discovered to be distinct from both tumorous and normal tissues. The ability to predict the survival rate of cancer patients using NATs can open a new door to selecting optimal treatments for cancer and discovering biomarkers.

METHODS

This paper introduces an artificial intelligence (AI) approach that uses NATs for predicting the 5-year survival of pre-operative radiotherapy patients with rectal cancer. The new approach combines pre-trained deep learning, nonlinear dynamics, and long short-term memory to classify immunohistochemical images of RhoB protein expression on NATs.

RESULTS

Ten-fold cross-validation results show 88% accuracy of prediction obtained from the new approach, which is also higher than those provided from baseline methods.

CONCLUSION

Preliminary results not only add objective evidence to recent findings of NATs' molecular characteristics using state-of-the-art AI methods, but also contribute to the discovery of RhoB expression on NATs in rectal-cancer patients.

CLINICAL IMPACT

The ability to predict the survival rate of cancer patients is extremely important for clinical decision-making. The proposed AI tool is promising for assisting oncologists in their treatments of rectal cancer patients.

Multi-Omic Investigations of a 17-19 Translocation Links MINK1 Disruption to Autism, Epilepsy and Osteoporosis

Balanced structural variants, such as reciprocal translocations, are sometimes hard to detect with sequencing, especially when the breakpoints are located in repetitive or insufficiently mapped regions of the genome. In such cases, long-range information is required to resolve the rearrangement, identify disrupted genes and, in symptomatic carriers, pinpoint the disease-causing mechanisms. Here, we report an individual with autism, epilepsy and osteoporosis and a de novo balanced reciprocal translocation: t(17;19) (p13;p11). The genomic DNA was analyzed by short-, linked- and long-read genome sequencing, as well as optical mapping. Transcriptional consequences were assessed by transcriptome sequencing of patient-specific neuroepithelial stem cells derived from induced pluripotent stem cells (iPSC). The translocation breakpoints were only detected by long-read sequencing, the first on 17p13, located between exon 1 and exon 2 of MINK1 (Misshapen-like kinase 1), and the second in the chromosome 19 centromere. Functional validation in induced neural cells showed that MINK1 expression was reduced by >50% in the patient’s cells compared to healthy control cells. Furthermore, pathway analysis revealed an enrichment of changed neural pathways in the patient’s cells. Altogether, our multi-omics experiments highlight MINK1 as a candidate monogenic disease gene and show the advantages of long-read genome sequencing in capturing centromeric translocations.

Advances in prognostic and therapeutic targets for hepatocellular carcinoma and intrahepatic cholangiocarcinoma: The hippo signaling pathway

Primary liver cancer is the sixth most frequently diagnosed cancer worldwide and the third leading cause of cancer-related death. The majority of the primary liver cancer cases are hepatocellular carcinoma and intrahepatic cholangiocarcinoma. Worldwide, there is an increasing incidence of primary liver cancer cases due to multiple risk factors ranging from parasites and viruses to metabolic diseases and lifestyles. Often, patients are diagnosed at advanced stages, depriving them of surgical curability benefits. Moreover, the efficacy of the available chemotherapeutics is limited in advanced stages. Furthermore, tumor metastases and recurrence make primary liver cancer management exceptionally challenging. Thus, exploring the molecular mechanisms for the development and progression of primary liver cancer is critical in improving diagnostic, treatment, prognostication, and surveillance modalities. These mechanisms facilitate the discovery of specific targets that are critical for novel and more efficient treatments. Consequently, the Hippo signaling pathway executing a pivotal role in organogenesis, hemostasis, and regeneration of tissues, regulates liver cells proliferation, and apoptosis. Cell polarity or adhesion molecules and cellular metabolic status are some of the biological activators of the pathway. Thus, understanding the mechanisms exhibited by the Hippo pathway is critical to the development of novel targeted therapies. This study reviews the advances in identifying therapeutic targets and prognostic markers of the Hippo pathway for primary liver cancer in the past six years.

High Expression of a Cancer Stemness-Related Gene, Chromobox 8 (CBX8), in Normal Tissue Adjacent to the Tumor (NAT) Is Associated with Poor Prognosis of Colorectal Cancer Patients

Background: Several studies have demonstrated that the molecular profile of normal tissue adjacent to the tumor (NAT) is prognostic for recurrence in patients with different cancers. This study investigated the clinical significance of CBX8 gene expression, a cancer stemness-related gene, in tumor and NAT tissue of colorectal cancer (CRC) patients. Methods: The gene level of CBX8 in paired CRC and NAT specimens from 95 patients was determined by quantitative PCR. CBX8 protein level in CRC and NAT specimens from 66 patients was determined by immunohistochemistry. CBX8 gene and protein levels were correlated with the patients’ clinicopathological parameters and circulatory immune cell profiles. The association between CBX8 and pluripotency-associated genes was analyzed using the TCGA database. Results: NAT CBX8 gene level positively correlated with TNM stage, tumor invasion, lymph node metastasis and distant metastasis, indicating its association with tumor progression and metastasis. There was no correlation between NAT CBX8 protein level and clinicopathological parameters. Moreover, a high level of CBX8 gene and protein in NAT both correlated with poor DFS and OS. There was an inverse correlation between CBX8 gene level and post-operative platelet counts and platelet to lymphocyte level, suggesting its association with systematic inflammation. Finally, TCGA analysis showed that CBX8 level was correlated with a couple of pluripotency-associated genes, supporting its association with cancer stemness. Conclusions: High NAT CBX8 is a poor prognostic factor for tumor progression and survival in CRC patients.

Detection of exosomal miR-18a and miR-222 levels in Egyptian patients with hepatic cirrhosis and hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is known to be the second leading cause of cancer-related mortality worldwide. For improving the prognosis as well as reducing the rate of mortality, early diagnosis of HCC is a must.

AIMS

This study was conducted to assess the ability of the serum expression of exosomal miR-18a and miR-222 to differentiate and diagnose patients with HCC, patients with liver cirrhosis, and healthy controls.

METHODS

This study included 51 patients with liver cirrhosis, 51 patients with HCC on top of hepatitis C virus (HCV) infection, and 50 healthy controls.

RESULTS

miR-18a and miR-222 were assessed using reverse transcription-polymerase chain reaction. MiR-18a and miR-222 levels were significantly higher in the liver cirrhosis and HCC groups than the control group (p ˂ 0.001). However, no statistically significant difference was found between patients with HCC and liver cirrhosis (p = 0.4 for miR-18a and p = 0.1 for miR-222). ROC curve analyses to evaluate the diagnostic performances of the two miRNAs as important noninvasive diagnostic markers revealed a best cutoff value of 2 for miR-18a to differentiate between liver cirrhosis, HCC, and healthy controls. And for mir-222, a cutoff value of 1.7 and 1.9 showed the highest specificity for discrimination between liver cirrhosis, HCC, and healthy controls, respectively. Moreover, logistic regression model revealed that miR-18a expression was independent predictive factor in HCC patients (p = 0.004), while miR-222 expression was independent predictive factor in liver cirrhosis patients (p < 0.001).

CONCLUSION

miR-18a and miR-222 were significantly discriminative markers between patients with liver cirrhosis and HCC and healthy individuals. Therefore, they have a prognostic rather than a diagnostic value. Moreover, miR-18a and miR-222 could be useful in identifying liver injuries, including fibrosis and cirrhosis.