Deciphering intratumoral heterogeneity of hepatocellular carcinoma with microvascular invasion with radiogenomic analysis.

BACKGROUND AND AIMS: The recurrence and metastasis of hepatocellular carcinoma (HCC) are mainly caused by microvascular invasion (MVI). Our study aimed to uncover the cellular atlas of MVI+ HCC and investigate the underlying immune infiltration patterns with radiomics features. METHODS: Three MVI positive HCC and three MVI negative HCC samples were collected for single-cell RNA-seq analysis. 26 MVI positive HCC and 30 MVI negative HCC tissues were underwent bulk RNA-seq analysis. For radiomics analysis, radiomics features score (Radscore) were built using preoperative contrast MRI for MVI prediction and overall survival prediction. We deciphered the metabolism profiles of MVI+ HCC using scMetabolism and scFEA. The correlation of Radscore with the level of APOE+ macrophages and iCAFs was identified. Whole Exome Sequencing (WES) was applied to distinguish intrahepatic metastasis (IM) and multicentric occurrence (MO). Transcriptome profiles were compared between IM and MO. RESULTS: Elevated levels of APOE+ macrophages and iCAFs were detected in MVI+ HCC. There was a strong correlation between the infiltration of APOE+ macrophages and iCAFs, as confirmed by immunofluorescent staining. MVI positive tumors exhibited increased lipid metabolism, which was attributed to the increased presence of APOE+ macrophages. APOE+ macrophages and iCAFs were also found in high levels in IM, as opposed to MO. The difference of infiltration level and Radscore between two nodules in IM was relatively small. Furthermore, we developed Radscore for predicting MVI and HCC prognostication that were also able to predict the level of infiltration of APOE+ macrophages and iCAFs. CONCLUSION: This study demonstrated the interactions of cell subpopulations and distinct metabolism profiles in MVI+ HCC. Besides, MVI prediction Radscore and MVI prognostic Radscore were highly correlated with the infiltration of APOE+ macrophages and iCAFs, which helped to understand the biological significance of radiomics and optimize treatment strategy for MVI+ HCC.

Crosstalk Between Metabolism and Immune Activity Reveals Four Subtypes With Therapeutic Implications in Clear Cell Renal Cell Carcinoma.

Clear cell renal cell carcinoma (ccRCC) is characterized by metabolic dysregulation and distinct immunological signatures. The interplay between metabolic and immune processes in the tumor microenvironment (TME) causes the complexity and heterogeneity of immunotherapy responses observed during ccRCC treatment. Herein, we initially identified two distinct metabolic subtypes (C1 and C2 subtypes) and immune subtypes (I1 and I2 subtypes) based on the occurrence of differentially expressed metabolism-related prognostic genes and immune-related components. Notably, we observed that immune regulators with upregulated expression actively participated in multiple metabolic pathways. Therefore, we further delineated four immunometabolism-based ccRCC subtypes (M1, M2, M3, and M4 subtypes) according to the results of the above classification. Generally, we found that high metabolic activity could suppress immune infiltration. Immunometabolism subtype classification was associated with immunotherapy response, with patients possessing the immune-inflamed, metabolic-desert subtype (M3 subtype) that benefits the most from immunotherapy. Moreover, differences in the shifts in the immunometabolism subtype after immunotherapy were observed in the responder and non-responder groups, with patients from the responder group transferring to subtypes with immune-inflamed characteristics and less active metabolic activity (M3 or M4 subtype). Immunometabolism subtypes could also serve as biomarkers for predicting immunotherapy response. To decipher the genomic and epigenomic features of the four subtypes, we analyzed multiomics data, including miRNA expression, DNA methylation status, copy number variations occurrence, and somatic mutation profiles. Patients with the M2 subtype possessed the highest VHL gene mutation rates and were more likely to be sensitive to sunitinib therapy. Moreover, we developed non-invasive radiomic models to reveal the status of immune activity and metabolism. In addition, we constructed a radiomic prognostic score (PRS) for predicting ccRCC survival based on the seven radiomic features. PRS was further demonstrated to be closely linked to immunometabolism subtype classification, immune score, and tumor mutation burden. The prognostic value of the PRS and the association of the PRS with immune activity and metabolism were validated in our cohort. Overall, our study established four immunometabolism subtypes, thereby revealing the crosstalk between immune and metabolic activities and providing new insights into personal therapy selection.

Comprehensive analysis of tumor immune microenvironment and prognosis of m6A-related lncRNAs in gastric cancer.

BACKGROUND: N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal roles in gastric cancer (GC) progression. The emergence of immunotherapy in GC has created a paradigm shift in the approaches of treatment, whereas there is significant heterogeneity with regard to degree of treatment responses, which results from the variability of tumor immune microenvironment (TIME). How the interplay between m6A and lncRNAs enrolling in the shaping of TIME remains unclear. METHODS: The RNA sequencing and clinical data of GC patients were collected from TCGA database. Pearson correlation test and univariate Cox analysis were used to screen out m6A-related lncRNAs. Consensus clustering method was implemented to classify GC patients into two clusters. Survival analysis, the infiltration level of immune cells, Gene set enrichment analysis (GSEA) and the mutation profiles were analyzed and compared between two clusters. A competing endogenous RNA (ceRNA) network and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were applied for the identification of pathways in which m6A-related lncRNAs enriched. Then least absolute shrinkage and selection operator (LASSO) COX regression was implemented to select pivotal lncRNAs, and risk model was constructed accordingly. The prognosis value of the risk model was explored. In addition, the response to immune checkpoint inhibitors (ICIs) therapy were compared between different risk groups. Finally, we performed qRT-PCR to detect expression patterns of the selected lncRNAs in the 35 tumor tissues and their paired adjacent normal tissues, and validated the prognostic value of risk model in our cohort (N = 35). RESULTS: The expression profiles of 15 lncRNAs were included to cluster patients into 2 subtypes. Cluster1 with worse prognosis harbored higher immune score, stromal score, ESTIMATE score and lower mutation rates of the genes. Different immune cell infiltration patterns were also displayed between the two clusters. GSEA showed that cluster1 preferentially enriched in tumor hallmarks and tumor-related biological pathways. KEGG pathway analysis found that the target mRNAs which m6A-related lncRNAs regulated by sponging miRNAs mainly enriched in vascular smooth muscle contraction, cAMP signaling pathway and cGMP-PKG signaling pathway. Next, eight lncRNAs were selected by LASSO regression algorithm to construct risk model. Patients in the high-risk group had poor prognoses, which were consistent in our cohort. As for predicting responses to ICIs therapy, patients from high-risk group were found to have lower tumor mutation burden (TMB) scores and account for large proportion in the Microsatellite Instability-Low (MSI-L) subtype. Moreover, patients had distinct immunophenoscores in different risk groups. CONCLUSION: Our study revealed that the interplay between m6A modification and lncRNAs might have critical role in predicting GC prognosis, sculpting TIME landscape and predicting the responses to ICIs therapy.

Risk stratification of LI-RADS M and LI-RADS 4/5 combined hepatocellular cholangiocarcinoma: prognostic values of MR imaging features and clinicopathological factors.

OBJECTIVES: To investigate the role of clinicopathological factors and MR imaging factors in risk stratification of combined hepatocellular cholangiocarcinoma (cHCC-CCA) patients who were classified as LR-M and LR-4/5. METHODS: We retrospectively identified consecutive patients who were confirmed as cHCC-CCA after surgical surgery in our institution from June 2015 to November 2020. Two radiologists evaluated the preoperative MR imaging features independently, and each lesion was assigned with a LI-RADS category. Preoperative clinical data were also collected. Multivariate Cox proportional hazards model was applied to separately identify the independent factors correlated with the recurrence of cHCC-CCAs in LR-M and LR-4/5. Risk stratifications were conducted separately in LR-M and LR-4/5. Recurrence-free survival (RFS) rates and overall survival (OS) rates were analyzed by using the Kaplan-Meier survival curves and log-rank test. RESULTS: A total of 131 patients with single primary lesion which met the 2019 WHO classification criteria were finally included. Corona enhancement, delayed central enhancement, and microvascular invasion (MVI) were identified as predictors of RFS in LR-M. Mosaic architecture, CA19-9, and MVI were independently associated with RFS in LR-4/5. Based on the number of these independent predictors, patients were stratified into favorable-outcome groups (LR-ML subgroup and LR-4/5L subgroup) and dismal-outcome groups (LR-MH subgroup and LR-4/5H subgroup). The corresponding median RFS for LR-ML, LR-MH, LR-5L, and LR-5H were 25.6 months, 8.2 months, 51.7 months, and 18.1 months. CONCLUSION: Our study explored the prognostic values of imaging and clinicopathological factors for LR-M and LR-4/5 cHCC-CCA patients, and different survival outcomes were observed among four subgroups after conducting risk stratifications. KEY POINTS: • Corona enhancement, delayed central enhancement, and MVI were identified as predictors of RFS in cHCC-CCAs which were classified into LR-M. Mosaic architecture, CA19-9, and MVI were independently associated with RFS in cHCC-CCAs which were classified into LR-4/5. • Based on the identified risk factors, LR-M and LR-4/5 cHCC-CCA patients could be stratified into two subgroups respectively, with significantly different RFS and OS. • cHCC-CCA patients from LR-M did not always have worse RFS and OS than those from LR-4/5 in some cases.

A multidimensional nomogram combining imaging features and clinical factors to predict the invasiveness and metastasis of combined hepatocellular cholangiocarcinoma.

BACKGROUND: Combined hepatocellular cholangiocarcinoma (CHCC-CCA) is a rare type of primary liver cancer having aggressive behavior. Few studies have investigated the prognostic factors of CHCC-CCA. Therefore, this study aimed to establish a nomogram to evaluate the risk of microvascular invasion (MVI) and the presence of satellite nodules and lymph node metastasis (LNM), which are associated with prognosis. METHODS: One hundred and seventy-one patients pathologically diagnosed with CHCC-CCA were divided into a training set (n=116) and validation set (n=55). Logistic regression analysis was used to assess the relative value of clinical factors associated with the presence of MVI and satellite nodules. The least absolute shrinkage and selection operator (LASSO) algorithm was used to establish the imaging model of all outcomes, and to build clinical model of LNM. Nomograms were constructed by incorporating clinical risk factors and imaging features. The model performance was evaluated on the training and validation sets to determine its discrimination ability, calibration, and clinical utility. Kaplan Meier analysis and time dependent receiver operating characteristic (ROC) were displayed to evaluate the prognosis value of the predicted nomograms of MVI and satellite nodule. RESULTS: A nomogram comprising the platelet to lymphocyte ratio (PLR), albumin-to-alkaline phosphatase ratio (AAPR) and imaging model was established for the prediction of MVI. Carcinoembryonic antigen (CEA) level and size were combined with the imaging model to establish a nomogram for the prediction of the presence of satellite nodules. Favorable calibration and discrimination were observed in the training and validation sets for the MVI nomogram (C-indexes of 0.857 and 0.795), the nomogram for predicting satellite nodules (C-indexes of 0.919 and 0.883) and the LNM nomogram (C-indexes of 0.872 and 0.666). Decision curve analysis (DCA) further confirmed the clinical utility of the nomograms. The preoperatively predicted MVI and satellite nodules by the combined nomograms achieved satisfactory performance in recurrence-free survival (RFS) and overall survival (OS) prediction. CONCLUSIONS: The proposed nomograms incorporating clinical risk factors and imaging features achieved satisfactory performance for individualized preoperative predictions of MVI, the presence of satellite nodules, and LNM. The prediction models were demonstrated to be good indicator for predicting the prognosis of CHCC-CCA, facilitating treatment strategy optimization for patients with CHCC-CCA.

Analysis of m6A-Related lncRNAs for Prognosis Value and Response to Immune Checkpoint Inhibitors Therapy in Hepatocellular Carcinoma.

INTRODUCTION: N6-methyladenosine (m6A) modification and long non-coding RNAs (lncRNAs) play pivotal roles in the progression of hepatocellular carcinoma (HCC). However, how their interaction is involved in the prognostic value of HCC and immune checkpoint inhibitors (ICIs) therapy remains unclear. METHODS: The RNA sequencing and clinical data of HCC patients were collected from TCGA database. The prognostic m6A-related lncRNAs were screened out with Pearson correlation test, univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) Cox regression. Patients with HCC were classified into 2 subtypes by consensus clustering. Survival analyses were performed to assess the prognostic value of different clusters and risk models. Potential tumor correlated biological pathways correlated with different clusters were explored through gene set enrichment analysis. We also identified the relationship of the risk model and clusters with response to immune checkpoint inhibitors (ICIs) therapy and tumor microenvironment (TME). Furthermore, the prognostic value of the 9 m6A-related lncRNAs was validated in the external cohort. Finally, the role of SNHG4 was explored by silencing and overexpression of SNHG4 through conducting proliferation, migration and invasion experiments. RESULTS: Patients from 2 clusters and different risk groups based on m6A-related lncRNAs had significantly different clinicopathological characteristics and overall survival outcomes. Tumor-correlated biological pathways were found to be correlated with Cluster 2 through GSEA. Moreover, we found that patients from different clusters and risk groups expressed higher levels of immune checkpoint genes and had distinct TME and different responses for ICIs therapy. Prognostic value of this risk model was further confirmed in the external cohort. Finally, consistent with the discovery, SNHG4 played an oncogenic role in vitro. CONCLUSION: Our study demonstrated that the 9 m6A-related lncRNA signature may serve as a novel predictor in the prognosis of HCC and optimize (ICIs) therapy. SNHG4 plays an oncogenic role in HCC.

Clinical value of whole-body magnetic resonance diffusion weighted imaging on detection of malignant metastases.

OBJECTIVE: To evaluate the value of whole-body diffusion weighted imaging (WB-DWI) on detection of malignant metastasis. METHODS: Forty-six patients with malignant tumors underwent WB-DWI examinations between April 2007 and August 2007 in our hospital. Before WB-DWI examination, the primary cancers of all the patients were confirmed by pathology, and the TNM-stage was assessed with conventional magnetic resonance imaging (MRI) or computed tomography (CT). WB-DWI was performed using short TI inversion recovery echo-planar imaging (STIR-EPI) sequence. Abnormal high signal intensities on WB-DWI were considered as metastases. The results of WB-DWI were compared with other imaging modalities. For the assessment of the diagnostic capability of WB-DWI, WB-DWI were compared with CT for demonstrating mediastinal lymph node metastases and lung metastases, and with conventional MRI for demonstrating metastases in other locations. RESULTS: WB-DWI demonstrated 143 focuses, 14 of which were diagnosed to be benign lesions in routine imaging. The number of bone metastases depicted on WB-DWI and routine imaging was 85 and 86; lymph node metastases was 17 and 18; liver metastases was 14 and 14; lung metastases was 4 and 8; and brain metastases was 6 and 8, respectively. WB-DWI failed to detect 12 metastatic lesions including 3 osteoplastic bone metastases, 4 lung metastases, 3 mediastinal lymph node metastases, and 2 brain metastases. Four metastatic lesions including 2 deltopectoral lymph nodes and 2 rib metastases were detected with WB-DWI alone, all of which evolved greatly during clinical follow-up for more than 6 months. WB-DWI had higher detection rates for metastatic lesions in liver, bone, and lymph nodes than those in lung and brain (chi2=30, P<0.001). CONCLUSIONS: WB-DWI could detect most of metastatic lesions that were diagnosed with conventional MRI and CT. The limitations of WB-DWI might be had high false-positive rate and low efficiency in detecting mediastinal lymph node, brain, and lung metastases.