Assessing the Influence of B-US, CDFI, SE, and Patient Age on Predicting Molecular Subtypes in Breast Lesions Using Deep Learning Algorithms

OBJECTIVES

Our study aims to investigate the impact of B-mode ultrasound (B-US) imaging, color Doppler flow imaging (CDFI), strain elastography (SE), and patient age on the prediction of molecular subtypes in breast lesions.

METHODS

Totally 2272 multimodal ultrasound imaging was collected from 198 patients. The ResNet-18 network was employed to predict four molecular subtypes from B-US imaging, CDFI, and SE of patients with different ages. All the images were split into training and testing datasets by the ratio of 80%:20%. The predictive performance on testing dataset was evaluated through 5 metrics including mean accuracy, precision, recall, F1-scores, and confusion matrix.

RESULTS

Based on B-US imaging, the test mean accuracy is 74.50%, the precision is 74.84%, the recall is 72.48%, and the F1-scores is 0.73. By combining B-US imaging with CDFI, the results were increased to 85.41%, 85.03%, 85.05%, and 0.84, respectively. With the integration of B-US imaging and SE, the results were changed to 75.64%, 74.69%, 73.86%, and 0.74, respectively. Using images from patients under 40 years old, the results were 90.48%, 90.88%, 88.47%, and 0.89. When images from patients who are above 40 years old, they were changed to 81.96%, 83.12%, 80.5%, and 0.81, respectively.

CONCLUSION

Multimodal ultrasound imaging can be used to accurately predict the molecular subtypes of breast lesions. In addition to B-US imaging, CDFI rather than SE contribute further to improve predictive performance. The predictive performance is notably better for patients under 40 years old compared with those who are 40 years old and above.

A prognostic signature established based on genes related to tumor microenvironment for patients with hepatocellular carcinoma

BACKGROUND

Complex cellular signaling network in the tumor microenvironment (TME) could serve as an indicator for the prognostic classification of hepatocellular carcinoma (HCC) patients.

METHODS

Univariate Cox regression analysis was performed to screen prognosis-related TME-related genes (TRGs), based on which HCC samples were clustered by running non-negative matrix factorization (NMF) algorithm. Furthermore, the correlation between different molecular HCC subtypes and immune cell infiltration level was analyzed. Finally, a risk score (RS) model was established by LASSO and Cox regression analyses (CRA) using these TRGs. Functional enrichment analysis was performed using gene set enrichment analysis (GSEA).

RESULTS

HCC patients were divided into three molecular subtypes (C1, C2, and C3) based on 704 prognosis-related TRGs. HCC subtype C1 had significantly better OS than C2 and C3. We selected 13 TRGs to construct the RS model. Univariate and multivariate CRA showed that the RS could independently predict patients' prognosis. A nomogram integrating the RS and clinicopathologic features of the patients was further created. We also validated the reliability of the model according to the area under the receiver operating characteristic (ROC) curve value, concordance index (C-index), and decision curve analysis. The current findings demonstrated that the RS was significantly correlated with CD8+ T cells, monocytic lineage, and myeloid dendritic cells.

CONCLUSION

This study provided TRGs to help classify patients with HCC and predict their prognoses, contributing to personalized treatments for patients with HCC.

The genomics of sporadic and hereditary colorectal cancer

Colorectal cancer (CRC) is a leading cause of cancer deaths worldwide. Over the past three decades, extensive efforts have sought to elucidate the genomic landscape of CRC. These studies reveal that CRC is highly heterogeneous at the molecular level, with different subtypes characterised by distinct somatic mutational profiles, epigenetic aberrations and transcriptomic signatures. This review summarises our current understanding of the genomic and epigenomic alterations implicated in CRC development and progression. Particular focus is given to how characterisation of CRC genomes is leading to more personalised approaches to diagnosis and treatment.

Construction and multicohort validation of a colon cancer prognostic risk score system based on big data of neutrophil-associated differentially expressed genes

Objective: To investigate the role of neutrophils in colon cancer progression. Methods: Genetic data from 1,273 patients with colon cancer were procured from public databases and categorized based on genes linked to neutrophils through an unsupervised clustering approach. Through univariate Cox regression analysis, differentially expressed genes (DEGs) influencing overall survival (OS) were identified, forming the basis for establishing a prognostic risk score (PRS) system specific to colon cancer. Additionally, the correlation between PRS and patient prognosis, immune cell infiltration, and intratumoral gene mutations were analyzed. Validation of PRS as an indicator for "pan-tumor" immunotherapy was conducted using four distinct immunotherapy cohorts. Results: The research identified two distinct subtypes of colon cancer, namely Cluster A and B, with patients in Cluster B demonstrating remarkably superior prognoses over those in Cluster A. A total of 17 genes affecting OS were screened based on 109 DEGs between the two cluster for constructing the PRS system. Notably, individuals classified under the high-PRS group (PRShigh) exhibited poorer prognoses, significantly linked with immune cell infiltration, an immunosuppressive tumor microenvironment, and increased genomic mutations. Remarkably, analysis of immunotherapy cohorts indicated that patients with PRShigh exhibited enhanced clinical responses, a higher rate of progression-free events, and improved overall survival post-immunotherapy. The PRS system, developed based on tumor typing utilizing neutrophil-associated genes, exhibited a strong correlation with prognostic elements in colon cancer and emerged as a vital predictor of "pan-tumor" immunotherapy efficacy. Conclusions: PRS serves as a prognostic model for patients with colon cancer and holds the potential to act as a "pan-tumor" universal marker for assessing immunotherapy efficacy across different tumor types. The study findings lay a foundation for novel antitumor strategies centered on neutrophil-focused approaches.

Functional characteristics of DNA N6-methyladenine modification based on long-read sequencing in pancreatic cancer

Abnormalities of DNA modifications are closely related to the pathogenesis and prognosis of pancreatic cancer. The development of third-generation sequencing technology has brought opportunities for the study of new epigenetic modification in cancer. Here, we screened the N6-methyladenine (6mA) and 5-methylcytosine (5mC) modification in pancreatic cancer based on Oxford Nanopore Technologies sequencing. The 6mA levels were lower compared with 5mC and upregulated in pancreatic cancer. We developed a novel method to define differentially methylated deficient region (DMDR), which overlapped 1319 protein-coding genes in pancreatic cancer. Genes screened by DMDRs were more significantly enriched in the cancer genes compared with the traditional differential methylation method (P < 0.001 versus P = 0.21, hypergeometric test). We then identified a survival-related signature based on DMDRs (DMDRSig) that stratified patients into high- and low-risk groups. Functional enrichment analysis indicated that 891 genes were closely related to alternative splicing. Multi-omics data from the cancer genome atlas showed that these genes were frequently altered in cancer samples. Survival analysis indicated that seven genes with high expression (ADAM9, ADAM10, EPS8, FAM83A, FAM111B, LAMA3 and TES) were significantly associated with poor prognosis. In addition, the distinction for pancreatic cancer subtypes was determined using 46 subtype-specific genes and unsupervised clustering. Overall, our study is the first to explore the molecular characteristics of 6mA modifications in pancreatic cancer, indicating that 6mA has the potential to be a target for future clinical treatment.

RSPO2-associated mitochondrial metabolism defines molecular subtypes with distinct clinical and immune features in esophageal cancer

BACKGROUND

Esophageal cancer is a highly aggressive malignancy with limited treatment options and poor prognosis. The identification of novel molecular subtypes and therapeutic targets is crucial for improving clinical outcomes.

METHOD

In this study, we investigated the role of R-spondin 2 (RSPO2) in esophageal cancer and its association with mitochondrial metabolism. Using bioinformatics analysis of publicly available datasets, we identified a panel of RSPO2-related mitochondrial metabolism genes and their expression patterns in esophageal cancer. Based on these genes, we stratified esophageal cancer patients into distinct molecular subtypes with different survival rates, immune cell infiltration profiles, and drug sensitivities.

RESULTS

Our findings suggest that RSPO2-related mitochondrial metabolism genes may serve as potential therapeutic targets and prognostic markers for esophageal cancer. These genes play an important role in the prognosis, immune cell infiltration and drug sensitivity of esophageal cancer.

CONCLUSION

The identified molecular subtypes provide valuable insights into the underlying molecular mechanisms of esophageal cancer and could guide personalized treatment strategies in the future.

Multiomics-based classifier to decipher immune landscape of uveal melanoma and predict patient outcomes

Uveal melanoma (UVM) prognosis and the possibilities for targeted therapy depend on a thorough understanding of immune infiltration features and the analysis of genomic and immune signatures. Leveraging multi-omics data from The Cancer Genome Atlas and GEO datasets, we employed an unsupervised clustering algorithm to categorize UVM into immune-related subgroups. Subsequent multi-omics analysis revealed two distinct UVM subtypes, each characterized by unique genomic mutations and immune microenvironment disparities. The aggressive UMCS2 subtype exhibited higher TNM stage and poorer survival, marked by elevated metabolism and increased immune infiltration. However, UMCS2 displayed heightened tumor mutational burden and immune dysfunction, leading to reduced responsiveness to immunotherapy. Importantly, these subtypes demonstrated differential sensitivity to targeted drugs due to significant variances in metabolic and immune environments, with UMCS2 displaying lower sensitivity. We developed a robust, subtype-specific marker-based risk scoring system. This system's diagnostic accuracy was validated through ROC curves, decision curve analysis, and calibration curves, all yielding satisfactory results. Additionally, cell experiments identified the pivotal function of HTR2B, the most crucial factor in this risk model. Knocking down HTR2B significantly reduced the activity, proliferation, and invasion ability of the UVM cell line. These findings underscored the impact of gene and immune microenvironment alterations in driving distinct molecular subtypes, emphasizing the need for precise treatment strategies. The molecular subtyping-based risk assessment system not only aids in predicting patient prognosis but also guides the identification of populations suitable for combined treatment. Molecules represented by HTR2B in the model may serve as effective therapeutic targets for UVM.Communicated by Ramaswamy H. Sarma.

Single-cell transcriptomes reveal the heterogeneity and microenvironment of vestibular schwannoma

BACKGROUND

Vestibular schwannoma (VS) is the most common benign tumor in the cerebellopontine angle and internal auditory canal. Illustrating the heterogeneous cellular components of VS could provide insights into its various growth patterns.

METHODS

Single-cell RNA sequencing was used to profile transcriptomes from 7 VS samples and 2 normal nerves. Multiplex immunofluorescence was employed to verify the data set results. Bulk RNA sequencing was conducted on 5 normal nerves and 44 VS samples to generate a prediction model for VS growth.

RESULTS

A total of 83 611 cells were annotated as 14 distinct cell types. We uncovered the heterogeneity in distinct VS tumors. A subset of Schwann cells with the vascular endothelial growth factor biomarker was significantly associated with fast VS growth through mRNA catabolism and peptide biosynthesis. The macrophages in the normal nerves were largely of the M2 phenotype, while no significant differences in the proportions of M1 and M2 macrophages were found between slow-growing and fast-growing VS. The normal spatial distribution of fibroblasts and vascular cells was destroyed in VS. The communications between Schwann cells and vascular cells were strengthened in VS compared with those in the normal nerve. Three cell clusters were significantly associated with fast VS growth and could refine the growth classification in bulk RNA.

CONCLUSIONS

Our findings offer novel insights into the VS microenvironment at the single-cell level. It may enhance our understanding of the different clinical phenotypes of VS and help predict growth characteristics. Molecular subtypes should be included in the treatment considerations.

Contrast-enhanced ultrasound for the preoperative prediction of pathological characteristics in breast cancer

Objective

We aimed to investigate the value of contrast-enhanced ultrasound (CEUS) in the preoperative prediction of the histological grades and molecular subtypes of breast cancer.

Methods

A total of 183 patients with pathologically confirmed breast cancer were included. Contrast enhancement patterns and quantitative parameters were compared in different groups. The receiver operating characteristic (ROC) curve was used to analyze the efficacy of CEUS in the preoperative prediction of pathological characteristics, including histologic grade and molecular subtypes.

Results

Heterogeneous enhancement, perfusion defects, and peripheral radial vessels were mostly observed in higher histologic grade (grade III) breast cancer. Heterogeneous enhancement and perfusion defect were the most effective indicators for grade III breast cancer, with the areas under the ROC curve of 0.768 and 0.756, respectively. There were significant differences in the enhancement intensity, post-enhanced margin, perfusion defects, and peripheral radial vessel among the different molecular subtypes of breast cancer (all P < 0.01). Perfusion defects and clear edge after enhancement were the best qualitative criteria for the diagnosis of HER-2 overexpressed and triple-negative breast cancers, and the corresponding areas under the ROC curves were 0.804 and 0.905, respectively. There were significant differences in PE, WiR, WiPI, and WiWoAUC between grade III vs grade I and II breast cancer (P < 0.05). PE, WiR, WiPI, and WiWoAUC had good efficiency in the diagnosis of high-histologic-grade breast cancer. PE had the highest diagnostic efficiency in Luminal A, while WiPI had the highest diagnostic efficiency in Luminal B subtype breast cancer, and the areas under the ROC curve were 0.825 and 0.838, respectively. WiWoAUC and WiR were the most accurate parameters for assessing triple-negative subtype breast cancers, and the areas under the curve were 0.932 and 0.922, respectively.

Conclusion

Qualitative and quantitative perfusion analysis of contrast-enhanced ultrasound may be useful in the non-invasive prediction of the histological grade and molecular subtypes of breast cancers.

Identification of molecular subtypes and diagnostic model in clear cell renal cell carcinoma based on collagen-related genes may predict the response of immunotherapy

Background: Collagen represents a prominent constituent of the tumor's extracellular matrix (ECM). Nonetheless, its correlation with the molecular subtype attributes of clear cell renal cell carcinoma (ccRCC) remains elusive. Our objective is to delineate collagen-associated molecular subtypes and further construct diagnostic model, offering insights conducive to the precise selection of ccRCC patients for immunotherapeutic interventions. Methods: We performed unsupervised non-negative matrix factorization (NMF) analysis on TCGA-KIRC samples, utilizing a set of 33 collagen-related differentially expressed genes (33CRDs) for clustering. Our analysis encompassed evaluations of subtype-associated differences in pathways, immune profiles, and somatic mutations. Through weighted gene co-expression network analysis (WGCNA) and four machine learning algorithms, two core genes were found and a diagnostic model was constructed. This was subsequently validated in a clinical immunotherapy cohort. Single cell sequencing analysis and experiments demonstrated the role of core genes in ccRCC. Finally, we also analyzed the roles of MMP9 and SCGN in pan-cancer. Results: We described two novel collagen related molecular subtypes in ccRCC, designated subtype 1 and subtype 2. Compared with subtype 1, subtype 2 showed more infiltration of immune components, but had a higher TIDE (tumor immunedysfunctionandexclusion) score and increased levels of immune checkpoint molecules. Furthermore, reduced prognosis for subtype 2 was a consistent finding in both high and low mutation load subgroups. MMP9 and SCGN were identified as key genes for distinguishing subtype 1 and subtype 2. The diagnostic model based on them could better distinguish the subtype of patients, and the differentiated patients had different progression free survival (PFS) in the clinical immunotherapy cohort. MMP9 was predominantly expressed in macrophages and has been extensively documented in the literature. Meanwhile, SCGN, which was overexpressed in tumor cells, underwent experimental validation, emphasizing its role in ccRCC. In various cancers, MMP9 and SCGN were associated with immune-related molecules and immune cells. Conclusion: Our study identifies two collagen-related molecular subtypes of ccRCC and constructs a diagnostic model to help select appropriate patients for immunotherapy.

Comprehensive Analysis of Fatty Acid Metabolism in Diabetic Nephropathy from the Perspective of Immune Landscapes, Diagnosis and Precise Therapy

Objective

Diabetic nephropathy (DN) represents the principal cause of end-stage renal diseases worldwide, lacking effective therapies. Fatty acid (FA) serves as the primary energy source in the kidney and its dysregulation is frequently observed in DN. Nevertheless, the roles of FA metabolism in the occurrence and progression of DN have not been fully elucidated.

Methods

Three DN datasets (GSE96804/GSE30528/GSE104948) were obtained and combined. Differentially expressed FA metabolism-related genes were identified and subjected to DN classification using "ConsensusClusterPlus". DN subtypes-associated modules were discovered by "WGCNA", and module genes underwent functional enrichment analysis. The immune landscapes and potential drugs were analyzed using "CIBERSORT" and "CMAP", respectively. Candidate diagnostic biomarkers of DN were screened using machine learning algorithms. A prediction model was constructed, and the performance was assessed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA). The online tool "Nephroseq v5" was conducted to reveal the clinical significance of the candidate diagnostic biomarkers in patients with DN. A DN mouse model was established to verify the biomarkers' expression.

Results

According to 39 dysregulated FA metabolism-related genes, DN samples were divided into two molecular subtypes. Patients in Cluster B exhibited worse outcomes with a different immune landscape compared with those in Cluster A. Ten potential small-molecular drugs were predicted to treat DN in Cluster B. The diagnostic model based on PRKAR2B/ANXA1 was created with ideal predictive values in early and advanced stages of DN. The correlation analysis revealed significant association between PRKAR2B/ANXA1 and clinical characteristics. The DN mouse model validated the expression patterns of PRKAR2B/ANXA1.

Conclusion

Our study provides new insights into the role of FA metabolism in the classification, immunological pathogenesis, early diagnosis, and precise therapy of DN.

Cellular Ligand-Receptor Perturbations Unravel MEIS2 as a Key Factor for the Aggressive Progression and Prognosis in Stage II/III Colorectal Cancer

Approximately 10-15% of stage II and 25-30% of stage III colorectal cancer (CRC) patients experience recurrence within 5 years after surgery, and existing taxonomies are insufficient to meet the needs of clinical precision treatment. Thus, robust biomarkers and precise management were urgently required to stratify stage II and III CRC and identify potential patients who will benefit from postoperative adjuvant therapy. Alongside, interactions of ligand-receptor pairs point to an emerging direction in tumor signaling with far-reaching implications for CRC, while their impact on tumor subtyping has not been elucidated. Herein, based on multiple large-sample multicenter cohorts and perturbations of the ligand-receptor interaction network, four well-characterized ligand-receptor-driven subtypes (LRDS) were established and further validated. These molecular taxonomies perform with unique heterogeneity in terms of molecular characteristics, immune and mutational landscapes, and clinical features. Specifically, MEIS2, a key LRDS4 factor, performs significant associations with proliferation, invasion, migration, and dismal prognosis of stage II/III CRC, revealing promising directions for prognostic assessment and individualized treatment of CRC patients. Overall, our study sheds novel insights into the implications of intercellular communication on stage II/III CRC from a ligand-receptor interactome perspective and revealed MEIS2 as a key factor in the aggressive progression and prognosis for stage II/III CRC.

The Impact of Chemotherapy on Cardiovascular Mortality across Breast Cancer Subtypes

Breast cancer is associated with cardiovascular mortality as an adverse effect of chemotherapy. Considering the variances across breast cancer subtypes, this study aims to investigate the cardiovascular mortality patterns in each subtype.

METHODS

This retrospective study used the SEER database of chemotherapy-receiving breast cancer patients (diagnosed in 2013-2020). The study population was categorized by cancer subtype, stage, patient age, and cause of death (COD). The percentage of cardiovascular CODs, odds ratio (ORs), 5-year cumulative crude probability of death, and standardized mortality ratios (SMRs) of each group were analyzed.

RESULTS

Among 23,263 nonsurviving breast cancer patients, 5.8% died from cardiovascular disease, whereas the HER2+/HR+ and HER2+/HR- subtypes exhibited the highest ORs of cardiovascular death and percentages of cardiovascular CODs, at 8.21% and 6.55%, respectively. The cardiovascular SMR increased with advancing stages and decreasing patient age. The HER2+/HR- subtype had the highest cardiovascular SMR, at 0.83 (p < 0.05), followed by TNBC, at 0.78 (p < 0.05). The 5-year cumulative probability of cardiovascular CODs also showed the highest risk in the HER2+/HR- subtype (1.02 ± 0.11%) and the TNBC subtype (0.95 ± 0.07%).

CONCLUSION

Breast cancer patients on chemotherapy face an elevated cardiovascular mortality risk, especially with aggressive subtypes (HER2-enriched, TNBC), advanced age, or HER2+/HR+ cancer receiving long-term treatment.

Common Misconceptions about Diet and Breast Cancer: An Unclear Issue to Dispel

Breast cancer (BC) constitutes a prevalent health condition among women. Recent years have witnessed the identification of dietary proto-oncogenic factors that deserve attention. Besides the well-known role of alcohol and red and processed meat in BC development, the impact of other dietary components remains unclear. Our narrative review aims to explore the diet-BC relationship, focusing on sugar, dairy, and soy consumption. We conducted a PubMed literature search covering the last decade (2013-2023) and included 35 papers. We found limited evidence on the association between high sugar intake and BC incidence. On the other hand, dairy and soy consumption displayed a protective effect in the majority of the analyzed papers. However, a significant degree of heterogeneity was reported among the results. Menopausal status and the specific BC molecular subtypes were the main factors influencing the interpretation of the results. Exploring dietary factors and BC revealed inconsistencies: high glycemic index post-menopause may be a risk factor, while sugar-sweetened drinks and artificial sweeteners yielded conflicting results; fermented dairy showed potential benefits, non-fermented dairy presented inconsistent findings; soy impact on BC varied according to molecular subtype, with some studies suggesting a positive association in luminal-like BC. Hence, further investigation is crucial to obtain a uniform consensus on the diet-BC relationship.

Exploring the Genomic Landscape of Hepatobiliary Cancers to Establish a Novel Molecular Classification System

Taxonomy of hepatobiliary cancer (HBC) categorizes tumors by location or histopathology (tissue of origin, TO). Tumors originating from different TOs can also be grouped by overlapping genomic alterations (GA) into molecular subtypes (MS). The aim of this study was to create novel HBC MSs. Next-generation sequencing (NGS) data from the AACR-GENIE database were used to examine the genomic landscape of HBCs. Machine learning and gene enrichment analysis identified MSs and their oncogenomic pathways. Descriptive statistics were used to compare subtypes and their associations with clinical and molecular variables. Integrative analyses generated three MSs with different oncogenomic pathways independent of TO (n = 324; p < 0.05). HC-1 "hyper-mutated-proliferative state" MS had rapidly dividing cells susceptible to chemotherapy; HC-2 "adaptive stem cell-cellular senescence" MS had epigenomic alterations to evade immune system and treatment-resistant mechanisms; HC-3 "metabolic-stress pathway" MS had metabolic alterations. The discovery of HBC MSs is the initial step in cancer taxonomy evolution and the incorporation of genomic profiling into the TNM system. The goal is the development of a precision oncology machine learning algorithm to guide treatment planning and improve HBC outcomes. Future studies should validate findings of this study, incorporate clinical outcomes, and compare the MS classification to the AJCC 8th staging system.

The outcomes of different regimens depend on the molecular subtypes of pulmonary large-cell neuroendocrine carcinoma: A retrospective study in China

BACKGROUND

The optimal systemic treatment for pulmonary large-cell neuroendocrine carcinoma (LCNEC) remains controversial, and recent advances in LCNEC molecular subtype classification have provided potential strategies for assisting in treatment decisions. Our study aimed to investigate the impact of treatment regimens, molecular subtypes and their concordance on clinical outcomes of patients diagnosed with LCNEC.

PATIENTS AND METHODS

All patients diagnosed with advanced pulmonary LCNEC in Peking Union Medical College Hospital (PUMCH) between January 2000 and October 2021 were enrolled in this retrospective study. The tumor samples were collected and sequenced using a tumor-specific gene panel, while clinical information was retrieved from the medical records system. The survival and therapeutic response were analyzed and compared between different subgroups classified by treatment regimen (SCLC or NSCLC-based), molecular subtype (type I or II) or the combination.

RESULTS

In univariate subgroup analysis categorized only by treatment regimen or molecular subtype, there were no differences identified in DCR, ORR, PFS, or OS. Nevertheless, the group with consistent treatment regimen and molecular subtype exhibited significantly longer OS than that of the inconsistent group (median OS 37.7 vs. 8.3 months; p = 0.046). Particularly, the OS of patients with type II LCNEC treated with SCLC-based regimen was significantly prolonged than that of others (median 37.7 vs. 10.5 months; p = 0.039).

CONCLUSIONS

Collectively, our study revealed the clinical outcomes of different treatment regimens for LCNEC patients highly depend on their molecular subtypes, highlighting the need for sequencing-guided therapy.

Metastasis and recurrence patterns in the molecular subtypes of urothelial bladder cancer

Urothelial cancer of the urinary bladder frequently metastasizes to lymph-nodes, lungs, liver and bone. A taxonomy for molecular classification exists, but it is unknown if molecular subtypes show tropism for different organs. Here, we study 146 patients with de novo metastatic disease or recurrence after curative treatment. We classify primary tumors using two transcriptomic methods and immunostaining and identify enrichment and depletion of metastatic sites in molecular subtypes using permutation tests. We observed significant depletion of bone metastases in the Basal/squamous molecular subtype, whereas the Urothelial-like subtype entailed an enrichment for metastases to bone. The Genomically unstable subtype was depleted of lung metastases, but enriched for atypical sites, including six out of seven patients with brain metastases. Stroma-rich primary tumor samples were associated with local recurrence, but not with distant sites. Additionally, the proportion with brain or testis metastases differed between systemic chemotherapy regimens (GC vs MVAC) suggesting a sanctuary effect. In conclusion, molecular subtypes of urothelial bladder cancer are significantly associated with specific metastatic sites, suggesting that subtype-specific molecular determinants could exist at various steps in the metastatic cascade.

Diabetes and incidence of breast cancer and its molecular subtypes: A systematic review and meta-analysis

Diabetes mellitus (DM) has been proposed to be positively associated with breast cancer (BCa) risk due to shared risk factors, metabolic dysfunction, and the use of antidiabetic medications. We conducted a systematic review and meta-analysis to evaluate the association between DM and BCa risk. We searched PubMed, Embase, and Web of Science for cohort and case-control studies assessing the association between DM and BCa published before 10 December 2021. Two reviewers independently screened the studies for inclusion, abstracted article data, and rated study quality. Random effects models were used to estimate summary risk ratios (RRs) and 95% confidence intervals (CIs). From 8396 articles identified in the initial search, 70 independent studies were included in the meta-analysis. DM was associated with an overall increased risk of BCa (RR = 1.20, 95% CI: 1.11-1.29). The 24 case-control studies demonstrated a stronger association (RR = 1.26, 95% CI: 1.13-1.40) than the 46 cohort studies (RR = 1.15, 95% CI: 1.05-1.27). Studies reporting risk by menopausal status found that postmenopausal women had an elevated risk of developing BCa (RR = 1.12, 95% CI: 1.07-1.17). No association between DM and BCa risk was observed among premenopausal women (RR = 0.95, 95% CI: 0.85-1.05). In addition, DM was associated with significantly increased risks of oestrogen receptor (ER)+ (RR = 1.09, 95% CI: 1.00-1.20), ER- (RR = 1.16, 95% CI: 1.04-1.30), and triple negative BCa (RR = 1.41, 95% CI: 1.01-1.96). The association estimate for human epidermal growth factor 2-positive BCa was also positive (RR = 1.21, 95% CI: 0.52-2.82), but the CI was wide and crossed the null. Our meta-analysis confirms a modest positive association between DM and BCa risk. In addition, our results suggest that the association between DM and BCa may be modified by menopausal status, and that DM may be differentially associated with BCa subtypes defined by receptor status. Additional studies are warranted to investigate the mechanisms underlying these associations and any influence of DM on BCa receptor expression.

Tumor microenvironment phenotypes and prognostic evaluation tools for osteosarcoma characterized by different prognostic outcomes and immunotherapy responses

BACKGROUND

The physiological and immunological characteristics of the tumor microenvironment (TME) have a profound impact on the effectiveness of immunotherapy. The present study aimed to define the TME subtype of osteosarcoma according to the signatures representing the global TME of the tumor, as well as create a new prognostic assessment tool to monitor the prognosis, TME activity and immunotherapy response of patients with osteosarcoma.

METHODS

The enrichment scores of 29 functional gene expression signatures in osteosarcoma samples were calculated by single sample gene set enrichment analysis (ssGSEA). TME classification of osteosarcoma was performed and a prognostic assessment tool was created based on 29 ssGSEA scores to comprehensively correlate them with TME components, immunotherapy efficacy and prognosis of osteosarcoma.

RESULTS

Three TME subtypes were generated that differed in survival, TME activity and immunotherapeutic response. Four differentially expressed genes between TME subtypes were involved in the development of prognostic assessment tools. The established prognosis assessment tool had strong performance in both training and verification cohorts, could be effectively applied to the survival prediction of samples of different ages, genders and transfer states, and could well distinguish the TME status of different samples.

CONCLUSIONS

The present study describes three different TME phenotypes in osteosarcoma, provides a risk stratification tool for osteosarcoma prognosis and TME status assessment, and provides additional information for clinical decision-making of immunotherapy.

Three molecular subtypes and a five-gene signature for hepatocellular carcinoma based on m7G-related classification

BACKGROUND

The current research investigated the heterogeneity of hepatocellular carcinoma (HCC) based on the expression of N7-methylguanosine (m7G)-related genes as a classification model and developed a risk model predictive of HCC prognosis, key pathological behaviors and molecular events of HCC.

METHODS

The RNA sequencing data of HCC were extracted from The Cancer Genome Atlas (TCGA)-live cancer (LIHC) database, hepatocellular carcinoman database (HCCDB) and Gene Expression Omnibus database, respectively. According to the expression level of 29 m7G-related genes, a consensus clustering analysis was conducted. The least absolute shrinkage and selection operator (LASSO) regression analysis and COX regression algorithm were applied to create a risk prediction model based on normalized expression of five characteristic genes weighted by coefficients. Tumor microenvironment (TME) analysis was performed using the MCP-Counter, TIMER, CIBERSORT and ESTIMATE algorithms. The Tumor Immune Dysfunction and Exclusion algorithm was applied to assess the responses to immunotherapy in different clusters and risk groups. In addition, patient sensitivity to common chemotherapeutic drugs was determined by the biochemical half-maximal inhibitory concentration using the R package pRRophetic.

RESULTS

Three molecular subtypes of HCC were defined based on the expression level of m7G-associated genes, each of which had its specific survival rate, genomic variation status, TME status and immunotherapy response. In addition, drug sensitivity analysis showed that the C1 subtype was more sensitive to a number of conventional oncolytic drugs (including paclitaxel, imatinib, CGP-082996, pyrimethamine, salubrinal and vinorelbine). The current five-gene risk prediction model accurately predicted HCC prognosis and revealed the degree of somatic mutations, immune microenvironment status and specific biological events.

CONCLUSION

In this study, three heterogeneous molecular subtypes of HCC were defined based on m7G-related genes as a classification model, and a five-gene risk prediction model was created for predicting HCC prognosis, providing a potential assessment tool for understanding the genomic variation, immune microenvironment status and key pathological mechanisms during HCC development.

The categorizations of vasculogenic mimicry in clear cell renal cell carcinoma unveil inherent connections with clinical and immune features

Background: Clear cell renal cell carcinoma (ccRCC) stands as the prevailing variant kidney cancer in humans. Unfortunately, patients with disseminated RCC at diagnosis often have a diminished prognosis. Rapid tumor growth necessitates efficient blood supply for oxygen and nutrients, involving the circulation of blood from vessels to tumor tissues, facilitating tumor cell entry into the extracellular matrix. Vasculogenic mimicry (VM) significantly contributes to tumor growth and metastasis. Within this investigation, we identified vasculogenic mimicry-related genes (VMRGs) by analyzing data from 607 cases of kidney renal clear cell carcinoma (KIRC) in The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo/). These findings offer insights into ccRCC progression and metastasis. Method: We identified VMRGs-related subtypes using consistent clustering methods. The signature of the VMRGs was created using univariate Cox regression and LASSO Cox regression analyses. To evaluate differences in immune cell infiltration, we employed ssGSEA. Afterwards, we created an innovative risk assessment model, known as the VM index, along with a nomogram to forecast the prognosis of ccRCC. Additionally, we verified the expression of an important gene related to VM, peroxiredoxin 2 (PRDX2), in tissue samples. Furthermore, we assessed the sensitivity to drugs in various groups by utilizing the pRRophetic R package. Results: Significant predictors of survival rates in both high- and low-risk groups of KIRC patients were identified as VMRGs. The independent prognostic factors for RCC were confirmed by both univariate and multivariate Cox regression analyses, validating VMRG risk signatures. Differences were observed in drug sensitivity, immune checkpoint expression, and responses to immune therapy between patients classified into high- and low-VMRG-risk groups. Our nomograms consistently demonstrated precise predictive capabilities. Finally, we experimentally verified PRDX2 expression levels and their impact on prognosis. Conclusion: The signature predicts patient prognosis and therapy response, laying the groundwork for future clinical strategies in treating ccRCC patients.

Integrated bioinformatics and experiment revealed that cuproptosis is the potential common pathogenesis of three kinds of primary cardiomyopathy

Cuproptosis is a recently reported new mode of programmed cell death which might be a potential co-pathogenesis of three kinds of primary cardiomyopathy. However, no investigation has reported a clear relevance between primary cardiomyopathy and cuproptosis. In this study, the differential cuproptosis-related genes (CRGs) shared by three kinds of primary cardiomyopathy were identified in training sets. As a result, four CRGs shared by three kinds of primary cardiomyopathy were acquired and they were mainly related to biological processes such as cell death and immuno-inflammatory response through differential analysis, correlation analysis, GSEA, GSVA and immune cell infiltration analysis. Then, three key CRGs (K-CRGs) with high diagnostic value were identified by LASSO regression. The results of nomogram, machine learning, ROC analysis, calibration curve and decision curve indicated that the K-CRGs exhibited outstanding performance in the diagnosis of three kinds of primary cardiomyopathy. After that, in each disease, two molecular subtypes clusters were distinguished. There were many differences between different clusters in the biological processes associated with cell death and immunoinflammation and K-CRGs had excellent molecular subtype identification efficacy. Eventually, results from validation datasets and in vitro experiments verified the role of K-CRGs in diagnosis of primary cardiomyopathy, identification of primary cardiomyopathic molecular subtypes and pathogenesis of cuproptosis. In conclusion, this study found that cuproptosis might be the potential common pathogenesis of three kinds of primary cardiomyopathy and K-CRGs might be promising biomarkers for the diagnosis and molecular subtypes identification of primary cardiomyopathy.

Multifactor assessment of ovarian cancer reveals immunologically interpretable molecular subtypes with distinct prognoses

Background

Ovarian cancer (OC) is a highly heterogeneous and malignant gynecological cancer, thereby leading to poor clinical outcomes. The study aims to identify and characterize clinically relevant subtypes in OC and develop a diagnostic model that can precisely stratify OC patients, providing more diagnostic clues for OC patients to access focused therapeutic and preventative strategies.

Methods

Gene expression datasets of OC were retrieved from TCGA and GEO databases. To evaluate immune cell infiltration, the ESTIMATE algorithm was applied. A univariate Cox analysis and the two-sided log-rank test were used to screen OC risk factors. We adopted the ConsensusClusterPlus algorithm to determine OC subtypes. Enrichment analysis based on KEGG and GO was performed to determine enriched pathways of signature genes for each subtype. The machine learning algorithm, support vector machine (SVM) was used to select the feature gene and develop a diagnostic model. A ROC curve was depicted to evaluate the model performance.

Results

A total of 1,273 survival-related genes (SRGs) were firstly determined and used to clarify OC samples into different subtypes based on their different molecular pattern. SRGs were successfully stratified in OC patients into three robust subtypes, designated S-I (Immunoreactive and DNA Damage repair), S-II (Mixed), and S-III (Proliferative and Invasive). S-I had more favorable OS and DFS, whereas S-III had the worst prognosis and was enriched with OC patients at advanced stages. Meanwhile, comprehensive functional analysis highlighted differences in biological pathways: genes associated with immune function and DNA damage repair including CXCL9, CXCL10, CXCL11, APEX, APEX2, and RBX1 were enriched in S-I; S-II combined multiple gene signatures including genes associated with metabolism and transcription; and the gene signature of S-III was extensively involved in pathways reflecting malignancies, including many core kinases and transcription factors involved in cancer such as CDK6, ERBB2, JAK1, DAPK1, FOXO1, and RXRA. The SVM model showed superior diagnostic performance with AUC values of 0.922 and 0.901, respectively. Furthermore, a new dataset of the independent cohort could be automatically analyzed by this innovative pipeline and yield similar results.

Conclusion

This study exploited an innovative approach to construct previously unexplored robust subtypes significantly related to different clinical and molecular features for OC and a diagnostic model using SVM to aid in clinical diagnosis and treatment. This investigation also illustrated the importance of targeting innate immune suppression together with DNA damage in OC, offering novel insights for further experimental exploration and clinical trial.

The Role of KRAS Mutation in Colorectal Cancer-Associated Thrombosis

Venous thromboembolic events (VTE) are common in patients with colorectal cancer (CRC) and represent a significant contributor to morbidity and mortality. Risk stratification is paramount in deciding the initiation of thromboprophylaxis and is calculated using scores that include tumor location, laboratory values, patient clinical characteristics, and tumor burden. Commonly used risk scores do not include the presence of molecular aberrations as a variable. This retrospective study aims to confirm the link between KRAS-activating mutations and the development of VTE in CRC. A total of 166 patients were included in this study. They were split into two cohorts based on KRAS mutational status. We evaluated the frequency and mean time to VTE development stratified by the presence of KRAS mutations. Patients with mutant KRAS had an odds ratio (OR) of 2.758 for VTE compared to KRAS wild-type patients, with an increased risk of thrombosis being maintained in KRAS mutant patients even after adjusting for other known VTE risk factors. Taking into account the results of this study, KRAS mutation represents an independent risk factor for VTE.

Integrating bulk and single-cell RNA sequencing data reveals epithelial-mesenchymal transition molecular subtype and signature to predict prognosis, immunotherapy efficacy, and drug candidates in low-grade gliomas

Objective: Epithelial-mesenchymal transition (EMT) is a tightly regulated and dynamic process occurring in both embryonic development and tumor progression. Our study aimed to comprehensively explore the molecular subtypes, immune landscape, and prognostic signature based on EMT-related genes in low-grade gliomas (LGG) in order to facilitate treatment decision-making and drug discovery. Methods: We curated EMT-related genes and performed molecular subtyping with consensus clustering algorithm to determine EMT expression patterns in LGG. The infiltration level of diverse immune cell subsets was evaluated by implementing the single-sample gene set enrichment analysis (ssGSEA) and ESTIMATE algorithms. The distinctions in clinical characteristics, mutation landscape, and immune tumor microenvironment (TME) among the subtypes were subjected to further investigation. Gene Set Variation Analysis (GSVA) was performed to explore the biological pathways that were involved in subtypes. The chemo drug sensitivity and immunotherapy of subtypes were estimated through GDSC database and NTP algorithm. To detect EMT subtype-related prognostic gene modules, the analysis of weighted gene co-expression network (WGCNA) was performed. The LASSO algorithm was utilized to construct a prognostic risk model, and its efficacy was verified through an independent CGGA dataset. Finally, the expression of the hub genes from the prognostic model was evaluated through the single-cell dataset and in-vitro experiment. Results: The TCGA-LGG dataset revealed the creation of two molecular subtypes that presented different prognoses, clinical implications, TME, mutation landscapes, chemotherapy, and immunotherapy. A three-gene signature (SLC39A1, CTSA and CLIC1) based on EMT expression pattern were established through WGCNA analysis. Low-risk patients showed a positive outlook, increased immune cell presence, and higher expression of immune checkpoint proteins. In addition, several promising drugs, including birinapant, fluvastatin, clofarabine, dasatinib, tanespimycin, TAK-733, GDC-0152, AZD8330, trametinib and ingenol-mebutate had great potential to the treatment of high risk patients. Finally, CTSA and CLIC1 were highly expressed in monocyte cell through single-cell RNA sequencing analysis. Conclusion: Our research revealed non-negligible role of EMT in the TME diversity and complexity of LGG. A prognostic signature may contribute to the personalized treatment and prognostic determination.

Advancing breast cancer subtyping: optimizing immunohistochemical staining classification with insights from real-world Taiwanese data

Gene expression signatures provide valuable information to guide postoperative treatment in breast cancer (BC) patients. However, genetic tests are prohibitively expensive for the majority of BC patients. Immunohistochemical staining (IHC) subtype classification system has been widely used for treatment guideline and is affordable to most BC patients. We aimed to revise immunohistochemical staining (IHC) subtyping to better match gene expression-based Prediction Analysis of Microarray 50 (PAM50) subtyping. Real world data of 372 BC patients were recruited in the Tri-Service General Hospital between Jan 2019 and Dec 2021. Clinical pathological information, blood, twelve pathological tissue slide samples, and fresh surgical tumor specimens were collected to examine IHC and PAM50. Current IHC subtyping (cIHC) tends to misclassify PAM50-based luminal A (lum A) to luminal B (lum B) by 35.81%, PAM50-lum B to PAM50-lum A by 9.09%, PAM50-Her2-enriched to lum B by 61.11%, PAM50-based Her2-enriched to lum B by 61.11%, and PAM50-based basal-like to lum B by 33.33%. We used random forest to identify estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (Her2), and Ki-67 status as the best indicators for revised IHC subtyping (rIHC4) and revised the classification rules by stratified analysis and prediction efficacy. rIHC4 increased the concordance rate for PAM50 subtypes from 68.3% to 74.7%. Both sensitivity and precision increased in most rIHC4 subtypes. Sensitivity increased from 33.3% to 87.4% in the Her2-enriched subtype; precision increased more evidently in the basal-like and lum B subtypes, from 71.4% to 83.3% and 57% to 65.1%, respectively. Our rIHC4 subtyping improved consistency with the PAM50 subtype, which could improve clinical management of BC patients without increasing medical expense.

High-Resolution Profiling of Head and Neck Squamous Cells Carcinoma Identifies Specific Biomarkers and Expression Subtypes of Clinically Relevant Vulnerabilities

BACKGROUND

Head and neck squamous cell carcinoma (HNSC) is the seventh most common cancer worldwide. Although there are several options for the treatment of HNSC, there is still a lack of better biomarkers to accurately predict the response to treatment and thus be more able to correctly treat the therapeutic modality.

METHODS

First, we typed cases from the TCGA-HNSC cohort into subtypes by a Bayesian non-negative matrix factorization (BayesNMF)-based consensus clustering approach. Subsequently, genomic and proteomic data from HNSC cell lines were integrated to identify biomarkers of response to targeted therapies and immunotherapies. Finally, associations between HNSC subtypes and CD8 T-cell-associated effector molecules, common immune checkpoint genes, were compared to assess the potential of HNSC subtypes as clinically predictive immune checkpoint blockade therapy.

RESULTS

The 500 HNSC cases from TCGA were put through a consensus clustering approach to identify six HNSC expression subtypes. In addition, subtypes with unique proteomics and dependency profiles were defined based on HNSC cell line histology and proteomics data. Subtype 4 (S4) exhibits hyperproliferative and hyperimmune properties, and S4-associated cell lines show specific vulnerability to ADAT2, EIF5AL1, and PAK2. PD-L1 and CASP1 inhibitors have therapeutic potential in S4, and we have also demonstrated that S4 is more responsive to immune checkpoint blockade therapy.

CONCLUSION

Overall, our HNSC typing approach identified robust tumor-expressing subtypes, and data from multiple screens also revealed subtype-specific biology and vulnerabilities. These HNSC expression subtypes and their biomarkers will help develop more effective therapeutic strategies.

Endometrial Carcinoma: A Comprehensive Analysis of Clinical Parameters, Treatment Modalities, and Prognostic Outcomes at a Tertiary Oncology Center in the UAE

Background Endometrial carcinoma (EC) remains a pressing global health issue, with a discernible upsurge in incidence, especially in developed countries. Notably, the United Arab Emirates (UAE) has witnessed a surge in EC cases, demanding an in-depth, region-specific exploration into the disease's clinical, treatment, and prognostic facets against the backdrop of its unique socio-genetic and environmental contours. Aim This study aimed to profess a comprehensive understanding of EC by examining clinical parameters, treatment modalities, and prognostic outcomes in the UAE context, thereby seeking to delineate potential correlations between varied therapeutic combinations, patient demographics, and tumor characteristics in affecting prognostic outcomes. Materials and methods A retrospective cohort study involving 93 patients diagnosed with EC from January 2011 to March 2023 at a leading oncology center in the UAE was conducted. Data, including demographic information, clinical presentation, treatment modalities, and prognostic outcomes, were meticulously extracted and analyzed. The R software (version 4.2.2) facilitated exhaustive statistical analyses, involving descriptive statistics, correlation analyses with the polycor package, and survival analyses utilizing the Kaplan-Meier method and Cox regression analysis via the survival and survminer packages, respectively. Results Although the correlation matrix revealed a noticeable relationship between "Family history" and "Age," most parameters displayed independence, offering a robust platform for ensuing multivariate analyses. Kaplan-Meier survival curves, stratified by therapeutic modalities, exhibited no statistically significant survival differences across therapeutic cohorts (p-values: 0.44, 0.86, and 0.83). Conversely, the composite Cox regression model underscored "non-national" demographic, Diabetes Mellitus II, and stromal invasion as pivotal prognostic factors, indicating the multifactorial nature of survival in EC patients and emphasizing demographic and tumor characteristics over therapeutic modalities as influential prognostic determinants. Conclusion In conclusion, while therapy types were not directly correlated with survival, demographic and tumor traits prominently impacted prognostic outcomes, advocating for an intricate, multidimensional approach to managing EC in the UAE. This study hopes to sow seeds for subsequent research, shaping clinically and culturally apt practices and policies in the region's healthcare landscape.

Emerging Prognostic and Predictive Factors in Pancreatic Cancer

Pancreatic cancer is a lethal disease with increasing incidence and high recurrence rates and is currently resistant to conventional therapies. Moreover, it displays extensive morphologic and molecular intratumoral and intertumoral heterogeneity and a mostly low mutational burden, failing to induce significant antitumor immunity. Thus, immunotherapy has shown limited effect in pancreatic cancer, except in rare tumors with microsatellite instability, constituting <1% of the cases. Currently, new methods, including single-cell and single-nucleus RNA sequencing, have refined and expanded the 2-group molecular classification based on bulk RNA sequencing (classical and basal-like subtypes), identifying hybrid forms and providing us with a comprehensive map of the tumor cell subsets that drive gene expression during tumor evolution, simultaneously giving us insight into therapy resistance and metastasis. Additionally, deeper profiling of the tumor microenvironment of pancreatic cancer by using spatial analyses and multiplex imaging techniques has improved our understanding of the heterogeneous distribution of both adaptive and innate immune components with their protumor and antitumor properties. By integrating host immune response patterns, as defined by spatial transcriptomic and proteomic analysis and multiplex immunofluorescence, with molecular and morphologic features of the tumors, we can increasingly understand the genetic, immunologic, and morphologic background of pancreatic cancer and recognize the potential predictors for different treatment modalities.

Clinical insights into small cell lung cancer: Tumor heterogeneity, diagnosis, therapy, and future directions

Small cell lung cancer (SCLC) is characterized by rapid growth and high metastatic capacity. It has strong epidemiologic and biologic links to tobacco carcinogens. Although the majority of SCLCs exhibit neuroendocrine features, an important subset of tumors lacks these properties. Genomic profiling of SCLC reveals genetic instability, almost universal inactivation of the tumor suppressor genes TP53 and RB1, and a high mutation burden. Because of early metastasis, only a small fraction of patients are amenable to curative-intent lung resection, and these individuals require adjuvant platinum-etoposide chemotherapy. Therefore, the vast majority of patients are currently being treated with chemoradiation with or without immunotherapy. In patients with disease confined to the chest, standard therapy includes thoracic radiotherapy and concurrent platinum-etoposide chemotherapy. Patients with metastatic (extensive-stage) disease are treated with a combination of platinum-etoposide chemotherapy plus immunotherapy with an anti-programmed death-ligand 1 monoclonal antibody. Although SCLC is initially very responsive to platinum-based chemotherapy, these responses are transient because of the development of drug resistance. In recent years, the authors have witnessed an accelerating pace of biologic insights into the disease, leading to the redefinition of the SCLC classification scheme. This emerging knowledge of SCLC molecular subtypes has the potential to define unique therapeutic vulnerabilities. Synthesizing these new discoveries with the current knowledge of SCLC biology and clinical management may lead to unprecedented advances in SCLC patient care. Here, the authors present an overview of multimodal clinical approaches in SCLC, with a special focus on illuminating how recent advancements in SCLC research could accelerate clinical development.

Molecular Subtypes of Ovarian Cancer Based on Lipid Metabolism and Glycolysis Reveals Potential Therapeutic Targets

BACKGROUND

Ovarian cancer (OC) is one of the most lethal gynecological malignant neoplasms. The aim of this study was to use high-throughput sequencing data to investigate the molecular and clinical characteristics of OC subtypes related to lipid metabolism and glycolysis, thus providing a theoretical basis for clinical decision-making.

METHODS

Molecular data and clinicopathological characteristics of OC patients were extracted from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression Project (GTEx), and the Gene Expression Omnibus (GEO). Following analysis of genes involved in lipid metabolism and glycolysis, OC was classified into subtypes by unsupervised clustering. The molecular features and clinical outcomes of these subtypes were then evaluated.

RESULTS

OC patients were divided into five subtypes based on the analysis of nine genes of interest. Amongst these, patients in subtype D had longer overall survival and more benign clinical features. Subtypes B and E had shorter overall- and progression-free survival, respectively. Both the B and E subtypes were closely related to lipid metabolism and to the glycolytic process. Subtype D was positively correlated with the infiltration of CD8+ T cells, CD4+ T cells, and macrophages, all of which play essential anti-tumor roles. Several risk models for selected subtypes were also constructed based on the expression of select genes.

CONCLUSIONS

The present work revealed that irregular metabolism in OC tissues was an indicator of poor clinical outcome and altered homeostasis in cancer-related pathways. Moreover, aberrant gene expression signatures associated with lipid metabolism and glycolysis were also correlated with an immunosuppressive tumor microenvironment. Based on lipid metabolism and glycolysis, we have therefore identified several OC molecular subtypes that may prove useful for the development of potential therapeutic targets.

Features of the immunosuppressive tumor microenvironment in endometrial cancer based on molecular subtype

Endometrial cancer (EC) is one of the three most prevalent gynecological tumors affecting women and is the most prevalent gynecological malignancy in the developed world. Its incidence is rapidly increasing worldwide, mostly affecting postmenopausal women, whereas recently its prevalence has increased in younger people. EC is an immune gene disease and many studies have shown that the tumor-immunosuppressive microenvironment plays an important role in cancer progression. In recent years, findings regarding the immunosuppressive tumor microenvironment (ITME) of EC have included immune evasion mechanisms and immunotherapy, which are mostly immune checkpoint inhibitors (ICI) for EC. Recently studies on the ITME of different molecular types of EC have found that different molecular types may have different ITME. With the research on the immune microenvironment of EC, a new immunophenotype classification based on the immune microenvironment has been carried out in recent years. However, the impact of the ITME on EC remains unclear, and the immunophenotype of EC remains limited to the research stage. Our review describes recent findings regarding the ITME features of different EC molecular types. The advent of immunotherapy has brought hope for improved efficacy and prognosis in patients with advanced or recurrent EC. The efficacy and safety of ICIs combination therapy remains the focus of future research.

Renal Function Parameters in Distinctive Molecular Subtypes of Prostate Cancer

Prostate cancer is a prevalent malignancy in male patients, having diverse clinical outcomes. The follow-up of patients diagnosed with prostate cancer involves the evaluation of renal function, because its impairment reduces patient survival rates and adds complexity to their treatment and clinical care. This study aimed to investigate the relationship between renal function parameters and distinctive molecular subtypes of prostate adenocarcinomas, defined by the immunoexpression of the SPINK1, ERG, HOXB13, and TFF3 markers. The study group comprised 72 patients with prostate cancer and associated chronic kidney disease (CKD) who underwent radical prostatectomy. Histopathological, molecular, and renal parameters were analyzed. Patients were categorized based on ERG/SPINK1 and HOXB13/TFF3 status, and correlations with renal function and prognostic grade groups were assessed. The ERG+/SPINK1+ subgroup exhibited significantly higher postoperative CKD stages and serum creatinine levels compared to the ERG+/SPINK1- subgroup. This suggests an intricate relationship between SPINK1 overexpression and renal function dynamics. The HOXB13-/TFF3+ subgroup displayed higher preoperative serum creatinine levels and CKD stages than the HOXB13-/TFF3- subgroup, aligning with TFF3's potential role in renal function. Furthermore, the study revealed associations between CKD stages and prognostic grade groups in different molecular subtypes, pointing out an intricate interplay between renal function and tumor behavior. Although the molecular classification of prostate acinar ADK is not yet implemented, this research underscores the variability of renal function parameters in different molecular subtypes, offering potential insights into patient prognosis.

Characterization of DNA Damage Repair Related Signature and Molecular Feature in Low-Grade Gliomas to Aid Chemotherapy and Drug Discovery

BACKGROUND

DNA damage repair (DDR) related genes are associated with the development, progression, aggressiveness, and heterogeneity of low-grade gliomas (LGG). However, the precise role of DDR in LGG prognosis and molecular subtypes remains to be elucidated.

METHODS

We analyzed 477 and 594 LGG samples from the Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) to develop a prognostic model using the random forest algorithm and Cox regression. Independent prognostic factors were incorporated into a nomogram, and its performance was assessed using receiver operating characteristic and calibration curves. We also used Connectivity Map analysis to identify potential small molecule drugs targeting DDR. Molecular subtypes based on DDR were identified by consensus cluster analysis, and the clinical characteristics, mutation landscape, immune tumor microenvironment, and drug sensitivity of patients with different subtypes in the TCGA and CGGA datasets were further compared. The Boruta algorithm was used to select features from the differentially expressed genes between clusters to generate DDR scores. Results were further validated in the Glioma Longitudinal AnalySiS consortium dataset. Statistical analysis and tests were implemented using R software version 4.0.2.

RESULTS

We developed a prognostic model containing six DDR-related genes, which served as a potential independent prognostic indicator in LGG across three datasets. The area under the curve (AUC) values for 1-, 3-, and 5-year survival in the TCGA dataset were 0.901, 0.832, and 0.771, respectively. The nomogram demonstrated high accuracy in predicting 1-, 3-, and 5-year survival, with AUC values greater than 0.8. Additionally, we identified and validated two molecular subtypes based on DDR genes. These subtypes exhibited significant differences in somatic mutations, clinical prognosis, and immune cell infiltration. One subtype showed higher immune and stromal scores, worse prognosis, and increased sensitivity to common chemotherapeutic agents. Finally, we established a DDR score which served as another promising prognostic predictor for LGG.

CONCLUSIONS

The prognostic model and molecular subtypes based on DDR genes can help in more detailed classification and provide insights for personalized management of LGG and clinical drug development.

Distinct molecular subtypes of systemic sclerosis and gene signature with diagnostic capability

Background

As Systemic Sclerosis (SSc) is a connective tissue ailment that impacts various bodily systems. The study aims to clarify the molecular subtypes of SSc, with the ultimate objective of establishing a diagnostic model that can inform clinical treatment decisions.

Methods

Five microarray datasets of SSc were retrieved from the GEO database. To eliminate batch effects, the combat algorithm was applied. Immune cell infiltration was evaluated using the xCell algorithm. The ConsensusClusterPlus algorithm was utilized to identify SSc subtypes. Limma was used to determine differential expression genes (DEGs). GSEA was used to determine pathway enrichment. A support vector machine (SVM), Random Forest(RF), Boruta and LASSO algorithm have been used to select the feature gene. Diagnostic models were developed using SVM, RF, and Logistic Regression (LR). A ROC curve was used to evaluate the performance of the model. The compound-gene relationship was obtained from the Comparative Toxicogenomics Database (CTD).

Results

The identification of three immune subtypes in SSc samples was based on the expression profiles of immune cells. The utilization of 19 key intersectional DEGs among subtypes facilitated the classification of SSc patients into three robust subtypes (gene_ClusterA-C). Gene_ClusterA exhibited significant enrichment of B cells, while gene_ClusterC showed significant enrichment of monocytes. Moderate activation of various immune cells was observed in gene_ClusterB. We identified 8 feature genes. The SVM model demonstrating superior diagnostic performance. Furthermore, correlation analysis revealed a robust association between the feature genes and immune cells. Eight pertinent compounds, namely methotrexate, resveratrol, paclitaxel, trichloroethylene, formaldehyde, silicon dioxide, benzene, and tetrachloroethylene, were identified from the CTD.

Conclusion

The present study has effectively devised an innovative molecular subtyping methodology for patients with SSc and a diagnostic model based on machine learning to aid in clinical treatment. The study has identified potential molecular targets for therapy, thereby offering novel perspectives for the treatment and investigation of SSc.

Improving Noninvasive Classification of Molecular Subtypes of Adult Gliomas With Diffusion-Weighted MR Imaging: An Externally Validated Machine Learning Algorithm

BACKGROUND

Genetic testing for molecular markers of gliomas sometimes is unavailable because of time-consuming and expensive, even limited tumor specimens or nonsurgery cases.

PURPOSE

To train a three-class radiomic model classifying three molecular subtypes including isocitrate dehydrogenase (IDH) mutations and 1p/19q-noncodeleted (IDHmut-noncodel), IDH wild-type (IDHwt), IDH-mutant and 1p/19q-codeleted (IDHmut-codel) of adult gliomas and investigate whether radiomic features from diffusion-weighted imaging (DWI) could bring additive value.

STUDY TYPE

Retrospective.

POPULATION

A total of 755 patients including 111 IDHmut-noncodel, 571 IDHwt, and 73 IDHmut-codel cases were divided into training (n = 480) and internal validation set (n = 275); 139 patients including 21 IDHmut-noncodel, 104 IDHwt, and 14 IDHmut-codel cases were utilized as external validation set.

FIELD STRENGTH/SEQUENCE

A 1.5 T or 3.0 T/multiparametric MRI, including T1-weighted (T1), T1-weighted gadolinium contrast-enhanced (T1c), T2-weighted (T2), fluid attenuated inversion recovery (FLAIR), and DWI.

ASSESSMENT

The performance of multiparametric radiomic model (random-forest model) using 22 selected features from T1, T2, FLAIR, T1c images and apparent diffusion coefficient (ADC) maps, and conventional radiomic model using 20 selected features from T1, T2, FLAIR, and T1c images was assessed in internal and external validation sets by comparing probability values and actual incidence.

STATISTICAL TESTS

Mann-Whitney U test, Chi-Squared test, Wilcoxon test, receiver operating curve (ROC), and area under the curve (AUC); DeLong analysis. P < 0.05 was statistically significant.

RESULTS

The multiparametric radiomic model achieved AUC values for IDHmut-noncodel, IDHwt, and IDHmut-codel of 0.8181, 0.8524, and 0.8502 in internal validation set and 0.7571, 0.7779, and 0.7491 in external validation set, respectively. Multiparametric radiomic model showed significantly better diagnostic performance after DeLong analysis, especially in classifying IDHwt and IDHmut-noncodel subtypes.

DATA CONCLUSION

Radiomic features from DWI could bring additive value and improve the performance of conventional MRI-based radiomic model for classifying the molecular subtypes especially IDHmut-noncodel and IDHwt of adult gliomas.

LEVEL OF EVIDENCE: 3

TECHNICAL EFFICACY

Stage 2.

Machine learning identification and immune infiltration of disulfidptosis-related Alzheimer's disease molecular subtypes

BACKGROUND

Alzheimer's disease (AD) is a common neurodegenerative disorder. Disulfidptosis is a newly discovered form of programmed cell death that holds promise as a therapeutic strategy for various disorders. However, the functional roles of disulfidptosis-related genes (DRGs) in AD remain unknown.

METHODS

Microarray data and clinical information from patients with AD and healthy controls were downloaded from the Gene Expression Omnibus database. A thorough examination of DRG expression and immune characteristics in both groups was performed. Based on the identified DRGs, we performed an unsupervised clustering analysis to categorize the AD samples into various disulfidptosis-related molecular clusters. Weighted gene co-expression network analysis was performed to select hub genes specific to disulfidptosis-related AD clusters. The performances of various machine learning models were compared to determine the optimal predictive model. The predictive ability of the optimal model was assessed using nomogram analysis and five external datasets.

RESULTS

Eight DRGs showed differential expression between the AD and control samples. Two different molecular clusters were identified. The immune cell infiltration analysis revealed distinct differences in the immune microenvironment of the two clusters. The support vector machine model showed the highest performance, and a panel of five signature genes was identified, which showed excellent performance on the external validation datasets. The nomogram analysis also showed high accuracy in predicting AD.

CONCLUSION

We identified disulfidptosis-related molecular clusters in AD and established a novel risk model to assess the likelihood of developing AD. These findings revealed a complex association between disulfidptosis and AD, which may aid in identifying potential therapeutic targets for this debilitating disorder.

Identification and validation of molecular subtypes and a 9-gene risk model for breast cancer

The long-term efficacy of treatment, heterogeneity, and complexity in the tumor microenvironment remained a clinical challenge in breast cancer (BRCA). There is a need to classify and refine appropriate therapeutic intervention decisions. A stable subtype classification based on gene expression associated with neoadjuvant chemotherapy (NAC) prognosis and assessment on the clinical features, immune infiltration, and mutational characteristics of the different subcategories was performed using ConsensusClusterPlus. We constructed a prognostic model by the least absolute shrinkage and selection operator regression (LASSO) and univariate Cox regression method and further investigated the association between the risk model and clinical features, mutation and immune characteristics of BRCA. We constructed 3 molecular clusters associated with NAC. We found that cluster 1 had the best prognosis, while cluster 3 showed a poor prognosis. Cluster 3 were associated with the advance stage, higher mutation score, activated oncogenic, and lower tumor immune dysfunction and exclusion (TIDE) score. Subsequently, we constructed a prognosis-related risk model comprising 9 genes (RLN2, MSLN, SAPCD2, LY6D, CACNG4, TUBA3E, LAMP3, GNMT, KLHDC7B). The higher-risk group exhibited lower immune infiltration and demonstrated improved overall survival (OS) in both the independent validation cohort. Finally, by combining clinicopathological features with the NAC-related prognostic risk model, we enhanced the accuracy of survival prediction and model performance. Here, we revealed 3 new molecular subtypes based on prognosis-related genes for BRCA NAC and developed a prognostic risk model. It has the potential to aid in the selection of appropriate individualized treatment and the prediction of patient prognosis.

Concordance of RT-qPCR with immunohistochemistry and its beneficial role in breast cancer subtyping

This study was to compare the concordance of transcription-quantitative polymerase chain reaction (RT-qPCR) with immunohistochemistry (IHC) in determining estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and tumor proliferation index (Ki67) status in breast cancer, and to assess the prognosis based on different subtypes. Totally 323 breast cancer patients were selected, including 216 in the training set and 107 in the validation set. Logistic regression models were constructed using 5-fold cross-validation with the mRNA expression of each biomarker as the predictor and the corresponding IHC expression level as the binary response variable. Receiver operating characteristic curve was used to determine the cutoff value. When the thresholds of ER, PR, HER2, and Ki67 were 0.764, 0.709, 0.161, and 0.554, there existed high concordance rates between IHC and RT-qPCR in ER (94.4%), PR (88.0%) and HER2 (89.4%) and a medium concordance rate in Ki67 (67.8%), which were further confirmed in the validation set (ER: 81.3%, PR: 78.3%, HER2: 80.4%, and Ki67: 69.1%). Based on the subtyping stratified by RT-qPCR, the 5-year recurrence-free interval rates of patients with luminal, HER2-enriched, and triple-negative subtypes were 88% (95% CI: 0.84-0.93), 82% (95% CI: 0.73-0.92) and 58% (95% CI: 0.42-0.80), respectively, which were similar to those assessed by IHC (88%, 78% and 47%). RT-qPCR may be a complementary method to IHC, which can not only provide additional useful information in clinic, but also show more advantages over IHC in determining certain subtypes of breast cancer.

[Heterogeneity analysis of pancreatic cancer and identification of molecular subtypes of tumor cells based on CEACAM5, LGALS1 and CENPF gene expression]

OBJECTIVE

To explore the heterogeneity of pancreatic cancer and new methods for tumor cell molecular subtyping and identify the signature genes in pancreatic cancer progression.

METHODS

Based on the single-cell sequencing data of 16 pancreatic cancer tissues from the GSE155698 dataset, the single pancreatic cancer cells were classified according to EPCAM gene expression after preliminary clustering, re-clustering, and subgrouping to identify the signature genes, followed by pathway enrichment analysis and pseudo-time analysis. The key genes identified were validated using the clinical and tissue gene and protein expression data from 179 pancreatic cancer patients and 171 healthy controls. The impact of CEACAM5, LGALS1, and CENPF on proliferation, migration and invasion of pancreatic cancer cells were analyzed.

RESULTS

Analysis of 48 570 cells from 16 pancreatic cancer samples revealed a total of 22 clusters, including 5 clusters of pancreatic cancer cells, which were classified into Subtype 1, Subtype 2, and Subtype 3, each exhibiting distinct gene expression patterns and functions. The signature genes were enriched in negatively regulated protein metabolic processes, ferroptosis, and antigen processing and presentation-related pathways in Subtype 1 pancreatic cancer cells; in peptide synthesis processes, translation, and ribosome-related pathways in Subtype 2; and in ATP metabolic processes, glycolysis/gluconeogenesis, and cell cyclerelated pathways in Subtype 3. Subtypes 2 and 3 were potentially derived from Subtype 1, and Subtype 3 possibly represented the final developmental stage of pancreatic cancer cells. The key signature genes (CEACAM5, LGALS1, and CENPF) also exhibited different expression patterns in the developmental trajectory and showed high expressions in pancreatic cancer in association with poor prognoses. In pancreatic cancer cells, downregulation of CEACAM5, LGALS1, and CENPF significantly inhibited the proliferation, migration, and invasion abilities of the cells (P<0.05).

CONCLUSION

Pancreatic cancer cells exhibit significant heterogeneity, and CEACAM5, LGALS1, and CENPF gene expressions, which affect pancreatic cancer cell proliferation, invasion, and metastasis, can be used to identify distinct molecular subtypes during tumor cell development.

Deciphering the immunological and prognostic features of bladder cancer through platinum-resistance-related genes analysis and identifying potential therapeutic target P4HB

Objectives

To identify the molecular subtypes and develop a scoring system for the tumor immune microenvironment (TIME) and prognostic features of bladder cancer (BLCA) based on the platinum-resistance-related (PRR) genes analysis while identifying P4HB as a potential therapeutic target.

Methods

In this study, we analyzed gene expression data and clinical information of 594 BLCA samples. We used unsupervised clustering to identify molecular subtypes based on the expression levels of PRR genes. Functional and pathway enrichment analyses were performed to understand the biological activities of these subtypes. We also assessed the TIME and developed a prognostic signature and scoring system. Moreover, we analyzed the efficacy of immune checkpoint inhibitors. Then we conducted real-time fluorescence quantitative polymerase chain reaction (RT-qPCR) experiments to detect the expression level of prolyl 4-hydroxylase subunit beta (P4HB) in BLCA cell lines. Transfection of small interference ribonucleic acid (siRNA) was performed in 5637 and EJ cells to knock down P4HB, and the impact of P4HB on cellular functions was evaluated through wound-healing and transwell assays. Finally, siRNA transfection of P4HB was performed in the cisplatin-resistant T24 cell to assess its impact on the sensitivity of BLCA to platinum-based chemotherapy drugs.

Results

In a cohort of 594 BLCA samples (TCGA-BLCA, n=406; GSE13507, n=188), 846 PRR-associated genes were identified by intersecting BLCA expression data from TCGA and GEO databases with the PRR genes from the HGSOC-Platinum database. Univariate Cox regression analysis revealed 264 PRR genes linked to BLCA prognosis. We identified three molecular subtypes (Cluster A-C) and the PRR scoring system based on PRR genes. Cluster C exhibited a better prognosis and lower immune cell infiltration compared to the other Clusters A and B. The high PRR score group was significantly associated with an immunosuppressive tumor microenvironment, poor clinical-pathological features, and a poor prognosis. Furthermore, the high PRR group showed higher expression of immune checkpoint molecules and a poorer response to immune checkpoint inhibitors than the low PRR group. The key PRR gene P4HB was highly expressed in BLCA cell lines, and cellular functional experiments in vitro indicate that P4HB may be an important factor influencing BLCA migration and invasion.

Conclusion

Our study demonstrates that the PRR signatures are significantly associated with clinical-pathological features, the TIME, and prognostic features. The key PRR gene, P4HB, s a biomarker for the individualized treatment of BLCA patients.

Determination of Cuproptosis-related Subtypes, Development of a Prognostic Model, and Characterization of Tumor Microenvironment Infiltration in Acute Myeloid Leukemia

BACKGROUND/AIM

Acute myeloid leukemia (AML) is a severe malignancy of the bone marrow marked by an abnormal accumulation of bone marrow precursors. Cuproptosis is a recently identified type of copper-dependent regulatory cell apoptosis that relies on mitochondrial respiration. However, its participation in the development of AML remains unclear. This study analyzed the association between cuproptosis-related genes and the prognosis of AML patients.

MATERIALS AND METHODS

Cases of AML were acquired from TCGA, GEO, and TARGET and the molecular subgroups characterized by genes associated with cuproptosis, besides the associated cell infiltration of the tumor microenvironment (TME) were investigated. The cuproptosis score was developed using the minor absolute shrinkage and selection operator (LASSO) tool to evaluate the cuproptosis features of a single tumor sample.

RESULTS

Two distinct molecular subgroups related to cuproptosis were discovered in AML with different prognoses. The cellular infiltration assay of TME showed immunological heterogeneity between the two subtypes. The cuproptosis score predicted tumor subgroups, immunity, and prognosis. A small cuproptosis value was marked by a good prognosis, whereas the anti-PD-1/PD-L1 immunotherapy group suggested the same cuproptosis group was related to an elevated immunotherapy potency.

CONCLUSION

The cuproptosis score is a biomarker important for determining the molecular subgroups, prognosis, TME cell infiltration features, and immunotherapeutic efficacy of individuals with leukemia.

Identification of endoplasmic reticulum stress-related biomarkers of diabetes nephropathy based on bioinformatics and machine learning

Backgrounds

Diabetes nephropathy (DN) is a growing public health concern worldwide. Renal dysfunction impairment in DN is intimately linked to ER stress and its related signaling pathways. Nonetheless, the underlying mechanism and biomarkers for this function of ER stress in the DN remain unknown.

Methods

Microarray datasets were retrieved from the Gene Expression Omnibus (GEO) database, and ER stress-related genes (ERSRGs) were downloaded from the MSigDB and GeneCards database. We identified hub ERSRGs for DN progression by intersecting ERSRGs with differentially expressed genes and significant genes in WGCNA, followed by a functional analysis. After analyzing hub ERSRGs with three machine learning techniques and taking the intersection, we did external validation as well as developed a DN diagnostic model based on the characteristic genes. Immune infiltration was performed using CIBERSORT. Moreover, patients with DN were then categorized using a consensus clustering approach. Eventually, the candidate ERSRGs-specific small-molecule compounds were defined by CMap.

Results

Several biological pathways driving pathological injury of DN and disordered levels of immune infiltration were revealed in the DN microarray datasets and strongly related to deregulated ERSRGs by bioinformatics multi-chip integration. Moreover, CDKN1B, EGR1, FKBP5, GDF15, and MARCKS were identified as ER stress signature genes associated with DN by machine learning algorithms, demonstrating their potential as DN biomarkers.

Conclusions

Our research sheds fresh light on the function of ER stress in DN pathophysiology and the development of early diagnostic and ER stress-related treatment targets in patients with DN.

Predicting Breast Cancer Subtypes Using Magnetic Resonance Imaging Based Radiomics With Automatic Segmentation

OBJECTIVE

The aim of the study is to demonstrate whether radiomics based on an automatic segmentation method is feasible for predicting molecular subtypes.

METHODS

This retrospective study included 516 patients with confirmed breast cancer. An automatic segmentation-3-dimensional UNet-based Convolutional Neural Networks, trained on our in-house data set-was applied to segment the regions of interest. A set of 1316 radiomics features per region of interest was extracted. Eighteen cross-combination radiomics methods-with 6 feature selection methods and 3 classifiers-were used for model selection. Model classification performance was assessed using the area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity.

RESULTS

The average dice similarity coefficient value of the automatic segmentation was 0.89. The radiomics models were predictive of 4 molecular subtypes with the best average: AUC = 0.8623, accuracy = 0.6596, sensitivity = 0.6383, and specificity = 0.8775. For luminal versus nonluminal subtypes, AUC = 0.8788 (95% confidence interval [CI], 0.8505-0.9071), accuracy = 0.7756, sensitivity = 0.7973, and specificity = 0.7466. For human epidermal growth factor receptor 2 (HER2)-enriched versus non-HER2-enriched subtypes, AUC = 0.8676 (95% CI, 0.8370-0.8982), accuracy = 0.7737, sensitivity = 0.8859, and specificity = 0.7283. For triple-negative breast cancer versus non-triple-negative breast cancer subtypes, AUC = 0.9335 (95% CI, 0.9027-0.9643), accuracy = 0.9110, sensitivity = 0.4444, and specificity = 0.9865.

CONCLUSIONS

Radiomics based on automatic segmentation of magnetic resonance imaging can predict breast cancer of 4 molecular subtypes noninvasively and is potentially applicable in large samples.

Prediction of Clinical Molecular Typing of Breast Invasive Ductal Carcinoma Using 18F-FDG PET/CT Dual-Phase Imaging

RATIONALE AND OBJECTIVES

To investigate the diagnostic value of Fluorine-18-labeled 2-fluoro-2-deoxy-D-glucose positron emission tomography and computed tomography (18F-FDG PET/CT) dual-phase imaging for the different molecular subtypes of invasive ductal carcinoma of the breast.

MATERIALS AND METHODS

Clinical imaging data of 164 women with invasive ductal carcinoma of the breast confirmed by pathology who underwent 18F-FDG PET/CT dual-phase imaging were retrospectively analyzed. The maximum standard uptake values (SUVmax) of the early and delayed phases of the lesion were measured and recorded as SUVmax1 and SUVmax2, respectively, and the retention index (RI) was calculated. We analyzed the change rule of SUVmax1, SUVmax2, and RI for the different molecular subtypes and molecular marker expression groups. The diagnostic threshold of different molecular marker expression status was determined using receiver operating characteristic curve analysis.

RESULTS

SUVmax1 and SUVmax2 were highest in the TNBC group and lowest in the luminal A group (p<0.001). TNBC and HER2 overexpression groups had higher RI than the luminal A and B groups (p<0.001), with no significant difference between the TNBC and HER2 overexpression groups or between the luminal A and B groups (p=0.640 and 0.345, respectively). The ER- and PR-negative groups had significantly higher SUVmax1, SUVmax2, and RI than the PR-positive group (p<0.001). The HER2-positive group had higher SUVmax1 and SUVmax2 than the negative group (p<0.001). The Ki67 overexpression group had higher SUVmax1 and SUVmax2 levels than the low expression group (p<0.001). There was no significant difference in RI between HER2-positive and negative groups or between Ki67 high and low expression groups (p=0.904 and 0.216, respectively). For ER-negative and positive expression status, the maximum area under the curve (AUC) of SUVmax2 was 0.852, diagnostic threshold was 10.87, sensitivity was 79.6%, and specificity was 74.5%. For PR-negative and positive expression status, the AUC of SUVmax2 was 0.858, diagnostic threshold was 10.45, sensitivity was 83.1%, and specificity was 75.3%. For HER2-negative and positive expression status, the AUC of SUVmax1 was 0.714, diagnostic threshold was 9.28, sensitivity was 79.6%, and specificity was 60.9%. For Ki67 high- and low expression status, the AUC of SUVmax2 was 0.915 at maximum, diagnostic threshold was 10.21, sensitivity was 83.4%, and specificity was 93.9%.

CONCLUSION

18F-FDG PET/CT dual-phase imaging facilitates the prediction of the expression of molecular markers and subtypes of invasive ductal carcinoma of the breast and the development of more tailored treatment plans for patients with this disease.

Molecular subtypes and scoring tools related to Foxo signaling pathway for assessing hepatocellular carcinoma prognosis and treatment responsiveness

Background: Transcription factors in Foxo signaling pathway influence hepatocellular carcinoma metastasis through epithelial mesenchymal transition-related pathways. Prognostic factors in the Foxo signaling pathway are feasible for HCC prognosis and therapeutic management. Methods: Based on the differentially expressed genes and Foxo signaling pathway genes in HCC, the ConsensusClusterPlus package was conducted to identify Foxo signaling pathway-related molecular subtypes in HCC. Based on the DEGs in the FMSs, the optimal prognostic factors in HCC were screened by cox and least absolute shrinkage and selection operator (LASSO) cox analysis to form the Foxo prognosis score (FPS). The prognostic predictive effectiveness of FPS was assessed by Kaplan Meier (K-M) analysis and Receiver Operating Characteristic (ROC) analysis. Additionally, tumor microenvironment (TME) score, tumor mutation burden (TMB) and treatment sensitivity differences in FMSs and FPS groups were also evaluated. Results: There were low, medium and high Foxo signaling pathway activity molecular subtypes in HCC named FMS 1, FMS 2 and FMS 3, respectively. FMS 1 with lowest Foxo signaling pathway activity presented an excellent survival advantage, while FMS 3 with highest Foxo signaling pathway activity exhibited an inhibitory TME status. According to FPS grouping, low FPS exhibited favorable survival, low TMB and anti-tumor activity. Patients in the low FPS group were mostly in the early stage of cancer. Moreover, we found that patients with high and low FPS exhibited different sensitivity to chemotherapy, and patients with low FPS were more sensitive to immunotherapy. Conclusion: We revealed a novel molecular subtype and prognostic tool based on Foxo signaling pathway signature, which could potentially provide a direction for accurate and effective assessment of potential personalized treatment options and prognostic management for HCC patients.

Identification of the HNSC88 Molecular Signature for Predicting Subtypes of Head and Neck Cancer

Head and neck squamous cell carcinoma (HNSC) exhibits genetic heterogeneity in etiologies, tumor sites, and biological processes, which significantly impact therapeutic strategies and prognosis. While the influence of human papillomavirus on clinical outcomes is established, the molecular subtypes determining additional treatment options for HNSC remain unclear and inconsistent. This study aims to identify distinct HNSC molecular subtypes to enhance diagnosis and prognosis accuracy. In this study, we collected three HNSC microarrays (n = 306) from the Gene Expression Omnibus (GEO), and HNSC RNA-Seq data (n = 566) from The Cancer Genome Atlas (TCGA) to identify differentially expressed genes (DEGs) and validate our results. Two scoring methods, representative score (RS) and perturbative score (PS), were developed for DEGs to summarize their possible activation functions and influence in tumorigenesis. Based on the RS and PS scoring, we selected candidate genes to cluster TCGA samples for the identification of molecular subtypes in HNSC. We have identified 289 up-regulated DEGs and selected 88 genes (called HNSC88) using the RS and PS scoring methods. Based on HNSC88 and TCGA samples, we determined three HNSC subtypes, including one HPV-associated subtype, and two HPV-negative subtypes. One of the HPV-negative subtypes showed a relationship to smoking behavior, while the other exhibited high expression in tumor immune response. The Kaplan-Meier method was used to compare overall survival among the three subtypes. The HPV-associated subtype showed a better prognosis compared to the other two HPV-negative subtypes (log rank, p = 0.0092 and 0.0001; hazard ratio, 1.36 and 1.39). Additionally, within the HPV-negative group, the smoking-related subgroup exhibited worse prognosis compared to the subgroup with high expression in immune response (log rank, p = 0.039; hazard ratio, 1.53). The HNSC88 not only enables the identification of HPV-associated subtypes, but also proposes two potential HPV-negative subtypes with distinct prognoses and molecular signatures. This study provides valuable strategies for summarizing the roles and influences of genes in tumorigenesis for identifying molecular signatures and subtypes of HNSC.

Comprehensive analysis of ligand-receptor interactions in colon adenocarcinoma to identify of tumor microenvironment oxidative stress and prognosis model

BACKGROUND

Single-cell technology enables a deep study on the mechanism of cancers. This work delineated the function of ligand-receptor [1] interaction in colon adenocarcinoma (COAD), and developed a LR pairs-based prognostic model.

METHODS

For identifying important LR pairs, Single-cell RNA sequencing data of COAD was included. Unsupervised consensus clustering constructed molecular subtypes. LASSO established a prognostic model. Infiltration of 22 immune cells was evaluated by Cibersort. Enrichment score of oxidative stress related pathways was determined by SsGSEA in each patient.

RESULTS

Forty-seven LR pairs were closely associated with the prognosis of COAD. Three molecular subtypes were differentiated according to 47 LR pairs, which displayed differential clinical features and molecular features. There were significant differences in immune T cell lytic activity among different subtypes. In clust1 with poor prognosis, significantly enriched oncogenic pathways were found, especially epithelial-mesenchymal transition (EMT). Additionally, it has been found that clust3 had significantly higher immune infiltration. A prognostic model containing eight LR pairs (PDGFB-PDGFRA, FLT4-VEGFC, CSF1R-CSF1, DLL1-NOTCH4, PDGFB-LRP1, DLL1-NOTCH3, FLT4-PDGFC, and NRP2-PGF) was established, which could effectively divide samples into low-risk and high-risk groups. Significantly higher oxidative stress was found among high-risk patients.

CONCLUSIONS

This study integrated expression data and single-cell data for demonstrating the effectiveness of LR pairs in establishing the prognostic model and constructing molecular subtypes. Prognostic LR pairs may contribute to tumorigenesis and progression in COAD. The prognostic model was the potential for predicting prognosis and guiding immunotherapy for COAD patients.

Identification of molecular subtypes based on PANoptosis-related genes and construction of a signature for predicting the prognosis and response to immunotherapy response in hepatocellular carcinoma

Background

Previous studies have demonstrated that PANoptosis is strongly correlated with cancer immunity and progression. This study aimed to develop a PANoptosis-related signature (PANRS) to explore its potential value in predicting the prognosis and immunotherapy response of hepatocellular carcinoma (HCC).

Methods

Based on the expression of PANoptosis-related genes, three molecular subtypes were identified. To construct a signature, the differentially expressed genes between different molecular subtypes were subjected to multivariate least absolute shrinkage and selection operator Cox regression analyses. The risk scores of patients in the training set were calculated using the signature. The patients were classified into high-risk and low-risk groups based on the median risk scores. The predictive performance of the signature was evaluated using Kaplan-Meier plotter, receiving operating characteristic curves, nomogram, and calibration curve. The results were validated using external datasets. Additionally, the correlation of the signature with the immune landscape and drug sensitivity was examined. Furthermore, the effect of LPCAT1 knockdown on HCC cell behavior was verified using in vitro experiments.

Results

This study developed a PANRS. The risk score obtained by using the PANRS was an independent risk factor for the prognosis of patients with HCC and exhibited good prognostic predictive performance. The nomogram constructed based on the risk score and clinical information can accurately predicted the survival probability of patients with HCC. Patients with HCC in the high-risk groups have high immune scores and tend to generate an immunosuppressive microenvironment. They also exhibited a favorable response to immunotherapy, as evidenced by high tumor mutational burden, high immune checkpoint gene expression, high human leukocyte antigen gene expression, low tumor immune dysfunction and low exclusion scores. Additionally, the PANRS enabled the identification of 15 chemotherapeutic agents, including sorafenib, for patients with HCC with different risk levels, guiding clinical treatment. The signature gene LPCAT1 was upregulated in HCC cell lines. LPCAT1 knockdown markedly decreased HCC cell proliferation and migration.

Conclusion

PANRS can accurately predict the prognosis and immunotherapy response of patients with HCC and consequently guide individualized treatment.

Machine learning for cryosection pathology predicts the 2021 WHO classification of glioma

BACKGROUND

Timely and accurate intraoperative cryosection evaluations remain the gold standard for guiding surgical treatments for gliomas. However, the tissue-freezing process often generates artifacts that make histologic interpretation difficult. In addition, the 2021 WHO Classification of Tumors of the Central Nervous System incorporates molecular profiles in the diagnostic categories, so standard visual evaluation of cryosections alone cannot completely inform diagnoses based on the new classification system.

METHODS

To address these challenges, we develop the context-aware Cryosection Histopathology Assessment and Review Machine (CHARM) using samples from 1,524 glioma patients from three different patient populations to systematically analyze cryosection slides.

FINDINGS

Our CHARM models successfully identified malignant cells (AUROC = 0.98 ± 0.01 in the independent validation cohort), distinguished isocitrate dehydrogenase (IDH)-mutant tumors from wild type (AUROC = 0.79-0.82), classified three major types of molecularly defined gliomas (AUROC = 0.88-0.93), and identified the most prevalent subtypes of IDH-mutant tumors (AUROC = 0.89-0.97). CHARM further predicts clinically important genetic alterations in low-grade glioma, including ATRX, TP53, and CIC mutations, CDKN2A/B homozygous deletion, and 1p/19q codeletion via cryosection images.

CONCLUSIONS

Our approaches accommodate the evolving diagnostic criteria informed by molecular studies, provide real-time clinical decision support, and will democratize accurate cryosection diagnoses.

FUNDING

Supported in part by the National Institute of General Medical Sciences grant R35GM142879, the Google Research Scholar Award, the Blavatnik Center for Computational Biomedicine Award, the Partners' Innovation Discovery Grant, and the Schlager Family Award for Early Stage Digital Health Innovations.