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Bladder cancer (BLCA), ranking as the tenth most prevalent malignancy globally, imposes a substantial public health and socio-economic challenge. Despite ongoing efforts by urologists to identify novel molecular subtypes and treatment paradigms, the intrinsic heterogeneity of BLCA continues to obstruct the efficacy of current diagnostic and therapeutic evaluations, leaving a gap in the comprehensive management of BLCA. This necessitates an in-depth investigation into the BLCA tumor microenvironment (TME) to identify pivotal molecules like MFAP3L. Our research concentrated on MFAP3L, commencing with a pan-cancer analysis of its immune profile. We discovered that MFAP3L exhibits a significant negative correlation with numerous immune components and markers in BLCA, a trend not observed in other cancer types. Within the TCGA-BLCA cohort, patients were classified into High-MFAP3L and Low-MFAP3L groups according to their MFAP3L transcript levels. Our exploration into the BLCA TME delved into immune infiltration, molecular subtype patterns, and treatment preferences within these MFAP3L groups. High MFAP3L expression was linked to favorable prognoses, luminal subtypes, and low immune infiltration, inversely associated with various immune molecules and characteristics. Additionally, high MFAP3L expressors exhibited diminished immune checkpoint levels, suggesting enhanced immunotherapy tolerance and sensitivity to oncogenic pathway targeting. Conversely, low MFAP3L expression correlated with poor outcomes, basal subtypes, increased immune infiltration, and heightened gene mutation rates, alongside sensitivity to radiotherapy, EGFR-targeted treatments, and immunotherapy. Hence, MFAP3L emerges as a critical yet underexplored gene in BLCA, offering insights into immune status within the TME and aiding in molecular subtyping and therapeutic decision-making. [ABSTRACT FROM AUTHOR]

Background: Although adequate physical activity has been shown to be beneficial in early breast cancer, evidence in metastatic breast cancer is sparse and contradictory, which could be related to distinct effects of physical activity on the different molecular cancer subtypes. Therefore, we here evaluated the effect of physical activity on progression-free and overall survival (PFS, OS) in metastatic breast cancer, specifically looking at molecular subtypes. Methods: International Physical Activity Questionnaire (IPAQ) questionnaires, filled out by patients enrolled in the prospective PRAEGNANT registry (NCT02338167; n = 1,270) were used to calculate metabolic equivalent task (MET) minutes, which were subsequently categorized into low (n = 138), moderate (n = 995) or high IPAQ categories (n = 137). Cox regression analyses were used to evaluate the impact of IPAQ categories and its interaction with molecular subtypes on PFS and OS. Results: Patient and tumor characteristics were equally distributed across IPAQ categories. HER2pos, HRpos and TNBC were present in 23.1%, 65.7% and 11.2% of patients, respectively. IPAQ scores did not have an impact on PFS and OS in addition to established prognostic factors, either overall or in particular molecular subtypes (PFS: p = 0.33 and OS: p = 0.08, likelihood ratio test). Exploratory analyses showed higher overall survival rates for high IPAQ categories compared to low/moderate IPAQ categories in luminal B-like breast cancer. Conclusions: Self-reported physical activity using the IPAQ questionnaire did not significantly affect PFS or OS in patients suffering from metastatic breast cancer. Nevertheless, some hypothesis-generating differences between molecular subtypes could be observed, which may be interesting to evaluate further. [ABSTRACT FROM AUTHOR]

The proliferation, metastasis, and drug resistance of cancer cells pose significant challenges to the treatment of lung squamous cell carcinoma (LUSC). However, there is a lack of optimal predictive models that can accurately forecast patient prognosis and guide the selection of targeted therapies. The extensive multi‐omic data obtained from multi‐level molecular biology provides a unique perspective for understanding the underlying biological characteristics of cancer, offering potential prognostic indicators and drug sensitivity biomarkers for LUSC patients. We integrated diverse datasets encompassing gene expression, DNA methylation, genomic mutations, and clinical data from LUSC patients to achieve consensus clustering using a suite of 10 multi‐omics integration algorithms. Subsequently, we employed 10 commonly used machine learning algorithms, combining them into 101 unique configurations to design an optimal performing model. We then explored the characteristics of high‐ and low‐risk LUSC patient groups in terms of the tumor microenvironment and response to immunotherapy, ultimately validating the functional roles of the model genes through in vitro experiments. Through the application of 10 clustering algorithms, we identified two prognostically relevant subtypes, with CS1 exhibiting a more favorable prognosis. We then constructed a subtype‐specific machine learning model, LUSC multi‐omics signature (LMS) based on seven key hub genes. Compared to previously published LUSC biomarkers, our LMS score demonstrated superior predictive performance. Patients with lower LMS scores had higher overall survival rates and better responses to immunotherapy. Notably, the high LMS group was more inclined toward “cold” tumors, characterized by immune suppression and exclusion, but drugs like dasatinib may represent promising therapeutic options for these patients. Notably, we also validated the model gene SERPINB13 through cell experiments, confirming its role as a potential oncogene influencing the progression of LUSC and as a promising therapeutic target. Our research provides new insights into refining the molecular classification of LUSC and further optimizing immunotherapy strategies. [ABSTRACT FROM AUTHOR]

BACKGROUND: It has been hypothesized that PANoptosis may be involved in the pathologic process of osteoporosis, but there have been no studies addressing the mechanisms of PANoptosis genes in osteoporosis. OBJECTIVE: To analyze the biological mechanism of PANoptosis regulators in the occurrence and development of osteoporosis. METHODS: The GSE56815 dataset was obtained from the Gene Expression Omnibus database and PANoptosis genes were extracted for differential analysis. The key genes of PANoptosis were screened by random forest tree model to construct a disease risk prediction model. Consensus clustering algorithm, single sample genome enrichment analysis and immune infiltration analysis were used to explore the differences between different PANoptosis molecular subtypes. Herbal drugs that regulate the key genes of PANoptosis were predicted through Coremine medical database, a medical ontology information retrieval platform. RESULTS AND CONCLUSION: Based on the four PANoptosis key genes (CASP1, CASP10, MEFV, and TNF), the diagnostic markers of osteoporosis were determined, and the risk prediction model was constructed and verified. Osteoporosis was divided into two different PANoptosis subtypes (clusters A, B and gene clusters A, B), and the PANoptosis scores of cluster B and gene cluster B were higher than those of cluster A and gene cluster A, respectively. Traditional Chinese drugs such as ginseng which can regulate the key genes of PANoptosis were predicted by the Coremine medical database. [ABSTRACT FROM AUTHOR]

This article presents a study on the development and validation of an ultrasound-based radiomics nomogram for predicting HER2-low expression breast cancer. The study included 222 patients with breast cancer, and machine learning methods were used to extract radiomics features from ultrasound images. The features were then used to construct a prediction model, which was validated using a training cohort and a test cohort. The nomogram, incorporating radiomics features and clinical risk factors, showed high prediction value for HER2-low expression breast cancer. This non-invasive approach could be useful for early detection and clinical therapy planning. [Extracted from the article]

Oncobiosis has emerged as a key contributor to the development, and modulator of the treatment efficacy of cancer. Hereby, we review the modalities through which the oncobiome can support the progression of tumors, and the emerging therapeutic opportunities they present. The review highlights the inherent challenges and limitations faced in sampling and accurately characterizing oncobiome. Additionally, the review underscores the critical need for the standardization of microbial analysis techniques and the consistent reporting of microbiome data. We provide a suggested metadata set that should accompany microbiome datasets from oncological settings so that studies remain comparable and decipherable. [ABSTRACT FROM AUTHOR]

PANoptosis is engaged in the program of immune response and carcinogenicity. Nonetheless, the actual impacts of PANoptosis on clinical management and oncology immunity in hepatocellular carcinoma (HCC) are not fully grasped. RNA-seq-derived computations were conducted to sort out the molecular subtypes and elucidate the disparities based on PANoptosis molecules. Single-cell sequencing (scRNA-seq) tools including Cytotrace and Addmodulescore were extracted to characterize diversification potency and quantify the PANoptosis motion. Transcriptional factors were inferred by the pySCENIC package and Cellchat program scrutinized the intercellular exchange across cell compartments. The PANoptosis score system originated by incorporating 10 machine learning algorithms and 101 compositions to project clinical results and deteriorate tendencies. Circulatory PANoptosis-associated protein HSP90AA1 was determined by enzyme-linked immunosorbent assay (ELISA). HCC individuals could be categorized into low- and high-PANoptosis groups with diverse biogenic and pharmacotherapy heterogeneity. Individuals in the elevated PANoptosis subtype were characterized as "hot tumor" conveying the increased presence of immunogenicity while reiterating an explicit negative connection with tumor stemness. Compared to immune and stromal cells, cancerous cells showcased decreased PANoptosis and heightened PANoptosis malignant cell subgroups might be tied to a substantial level of genomic expression of SREBF2, JUND, GATAD1, ZBTB20, SMAD5 and implied a more aggressive potential. The PANoptosis index, derived from machine learning, has been established to provide succinct frameworks for predicting outcomes and clarified the noteworthy utility of conventional regimens, as the differentiated power of HCC occurred together with vascular invasion and hepatocellular adenoma (HCA). The experiment confirmed that the circulating HSP90AA1 was aberrantly augmented in HCC patients, thus demonstrating its potential as a discriminatory biomarker. We systematically deciphered the molecular and immune ecosystem traits of PANoptosis in bulk and scRNA-seq degrees, which may deliver advantageous insights for customized treatment, awareness of the pathological process and prognosis scrutiny for HCC patients. [ABSTRACT FROM AUTHOR]

Background: High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment. Methods: Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments. Results: We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells. Conclusion: The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets. [ABSTRACT FROM AUTHOR]

Background: Acute myeloid leukemia (AML) is a genetically heterogeneous disease in which glutamine (Gln) contributes to AML progression. Therefore, this study aimed to identify potential prognostic biomarkers for AML based on Gln metabolism-related genes. Methods: Gln-related genes that were differentially expressed between Cancer Genome Atlas-based AML and normal samples were analyzed using the limma package. Univariate, least absolute shrinkage, selection operators, and stepwise Cox regression analyses were used to identify prognostic signatures. Risk score-based prognostic and nomogram models were constructed to predict the prognostic risk of AML. Subsequently, consistent cluster analysis was performed to stratify patients into different subtypes, and subtype-related module genes were screened using weighted gene co-expression network analysis. Results: Through a series of regression analyses, HGF, ANGPTL3, MB, F2, CALR, EIF4EBP1, EPHX1, and PDHA1 were identified as potential prognostic biomarkers of AML. Prognostic and nomogram models constructed based on these genes could significantly differentiate between high- and low-risk AML with high predictive accuracy. The eight-signature also stratified patients with AML into two subtypes, among which Cluster 2 was prone to a high risk of AML prognosis. These two clusters exhibited different immune profiles. Of the subtype-related module genes, the HOXA and HOXB family genes may be genetic features of AML subtypes. Conclusion: Eight Gln metabolism-related genes were identified as potential biomarkers of AML to predict prognostic risk. The molecular subtypes clustered by these genes enabled prognostic risk stratification. Highlights: Eight genes were identified as potential prognostic biomarkers of acute myeloid leukemia (AML). The prognostic and nomogram models can accurately predict the AML prognostic risks. Eight prognostic signatures stratified AML patients into two subtypes with different prognostic patterns and immune profiles. HOXA and HOXB family genes may be genetic features of AML subtypes. [ABSTRACT FROM AUTHOR]

Objective: Endometrial cancer is stepping into the era of precision therapy. Genomic test is recommended for newly diagnostic patients. However, outpatients displayed more complex characteristics. Here, we elucidated the clinical characteristics and genomic profiling of outpatients with endometrial cancer at our institution. Methods: Between 2018 and 2023, 68 endometrial cancer received genomic tests at outpatient department of Fudan University Shanghai Cancer Center. Data, including age, pathological histology, FIGO stage and treatment strategy were collected. Germline mutations, molecular subtypes and other somatic mutations were also summarized. Results: Overall, 72.1% (49/68) of patients receive genomic tests at primary diagnosis, while 27.9% (19/68) of patients received tests at recurrence. Nine patients had deleterious germline mutations, including BRCA1(2), MLH1(1), MSH2(2, including one with co-mutation of RAD50), MSH6(2), FANCA(1), MUTYH(1). Molecular subtypes were recognized among 62 patients, as POLE super-mutation(4, 6.5%), MSI-H(7, 11.3%), CN-Low(36, 58.1%) and CN-High(15, 24.2%). Ten patients received anti-PD1 monotherapy or in combination with chemotherapy or anti-angiogenic therapy, with the duration of disease control of 1 to 35 months. The ORR rate was 30%, and six patients had stable disease. The median (range) follow-up time was 18(2–160) months. 23(33.8%) relapses were recorded, and CN-High subtype displayed worst PFS compared with other subtypes (P < 0.01). 6 deaths were reported including 2(5.6%) of CN-Low and 4(26.7%) of CN-High. Conclusion: Outpatients department gathered a considerable proportion of recurrent patients with complex genomic features. Patients with worse prognosis could be well studied, and anti-PD1 therapy was a promising salvage therapy in the real world. [ABSTRACT FROM AUTHOR]

We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability to handle class imbalance should be considered priority. [ABSTRACT FROM AUTHOR]

Purpose: To characterize the histologic and molecular subtype distribution of, and survival from, breast cancer (BC) among Canadian women overall, and by stage and age at diagnosis. Methods: Invasive BC cases from the Canadian Cancer Registry for women aged 15–99 years between 2012 and 2017 in Canada, excluding Quebec, were examined using pre-existing mortality linkages. Stage at diagnosis, molecular, and histologic subtypes, and 5-year net survival (NS) by age, subtype, and stage were determined. Results: 107,271 women with BC were included. Luminal A was the most common subtype, present in increasing proportions as women aged, up to a maximum of 55% of cases in 70–74. Ductal and luminal A were most likely to be diagnosed at stage I, while HER2+ had the highest proportion of diagnosis at stage III; triple negative (TN) and unknown had the highest proportion of stage IV. For all stages combined, luminal A had a five-year NS of 98%, while TN was 74%. NS for stage I BC was 99–100% for all subtypes, excepting TN, which was 96%. Survival decreased with advancing stage, most markedly for TN, for which stage III was 47% and stage IV 7%. Survival by equivalent stage and subtype was comparable across age groups but declined in older age categories. Conclusions: The varying natural histories of BC subtypes and histologies can inform prognoses, health system economics, and screening practices. The NS of 96% or greater for stage I, regardless of subtype, highlights the importance of early detection for all subtypes of BC, especially in aggressive subtypes. [ABSTRACT FROM AUTHOR]

Objective: Triple-negative breast cancer (TNBC) is a heterogeneous and aggressive cancer. Although our previous study classified primary TNBC into four subtypes, comprehensive longitudinal investigations are lacking. Methods: We assembled a large-scale, real-world cohort comprised of 880 TNBC patients [465 early-stage TNBC (eTNBC) and 415 metastatic TNBC (mTNBC) patients] who were treated at Fudan University Shanghai Cancer Center. The longitudinal dynamics of TNBC subtypes during disease progression were elucidated in this patient cohort. Comprehensive analysis was performed to compare primary and metastatic lesions within specific TNBC subtypes. Results: The recurrence and metastasis rates within 3 years after initial diagnosis in the eTNBC cohort were 10.1% (47/465). The median overall survival (OS) in the mTNBC cohort was 27.2 months [95% confidence interval (CI), 24.4–30.2 months], which indicated a poor prognosis. The prognostic significance of the original molecular subtypes in both eTNBC and mTNBC patients was confirmed. Consistent molecular subtypes were maintained in 77.5% of the patients throughout disease progression with the mesenchymal-like (MES) subtype demonstrating a tendency for subtype transition and brain metastasis. Additionally, a precision treatment strategy based on the metastatic MES subtype of target lesions resulted in improved progression-free survival in the FUTURE trial. Conclusions: Our longitudinal study comprehensively revealed the clinical characteristics and survival of patients with the original TNBC subtypes and validated the consistency of most molecular subtypes throughout disease progression. However, we emphasize the major importance of repeat pathologic confirmation of the MES subtype. [ABSTRACT FROM AUTHOR]

Endometrial carcinoma is the most common gynaecological malignancy in the UK and its incidence is increasing worldwide. The classification of endometrial carcinoma (EC) has been based on cell type (histotype) for decades, and this, together with grade, lymphovascular space invasion (LVSI) and stage of the tumor was used for risk assessment, guiding decisions about the extent of surgery and the need for post-surgical adjuvant treatment. There has been, and remains, considerable variation in clinical practice worldwide, with respect to both the extent of surgery (lymph node dissection, omentectomy, pelvic washings) and use of adjuvant therapy (radiation, chemotherapy, or both) for patients with identical risk factors but treated at different centers. Furthermore, EC has tended to be treated as a single disease, irrespective of histotype. In the past five years there has been a significant move to more personalized risk assessment and treatment with the introduction of routine molecular assessment of EC, and diagnosis of molecular subtype. The four EC molecular subtypes differ with respect to molecular pathology, genetic and environmental risk factors, precursor lesions, prognosis and response to specific treatments. This review will discuss the assessment of EC molecular subtype in clinical practice and how this information impacts on risk assessment and treatment. [ABSTRACT FROM AUTHOR]

Bladder cancers are heterogeneous in nature, showing diverse molecular profiles and histopathological characteristics, which pose challenges for diagnosis and treatment. However, understanding the molecular basis of such heterogeneity has remained elusive. This study aimed to elucidate the molecular landscape of neuroendocrine‐like bladder tumors, focusing on the involvement of β‐catenin localization. Analyzing the transcriptome data and benefiting from the molecular classification tool, we undertook an in‐depth analysis of muscle‐invasive bladder cancers to uncover the molecular characteristics of the neuroendocrine‐like differentiation. The study explored the contribution of transcription factors and chromatin remodeling complexes to neuroendocrine differentiation in bladder cancer. The study revealed a significant correlation between β‐catenin localization and neuroendocrine differentiation in muscle‐invasive bladder tumors, highlighting the molecular complexity of neuroendocrine‐like tumors. Enrichment of YY1 transcription factor, E2F family members, and Polycomb repressive complex components in β‐catenin‐positive tumors suggest their potential contribution to neuroendocrine phenotypes. Our findings contribute valuable insights into the molecular complexity of neuroendocrine‐like bladder tumors. By identifying potential therapeutic targets and refining diagnostic strategies, this study advances our understanding of endocrinology in the context of bladder cancer. Further investigations into the functional implications of these molecular relationships are warranted to enhance our knowledge and guide future therapeutic interventions. [ABSTRACT FROM AUTHOR]

Abstract Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease characterized by a grim prognosis, in which various forms of cell death are significant contributors to its development. The objective of this study is to explore diagnostic biomarkers and molecular subtypes associated with PANoptosis in IPF, and to develop reliable diagnostic models based on PANoptosis-related mechanisms. The peripheral blood transcriptomic data of IPF from the Gene Expression Omnibus (GEO) database and PANoptosis-related genes from the GeneCards database were utilized to conduct differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), identifying PANoptosis-related differentially expressed genes (PDEGs). We yielded 9 PDEGs and employed machine learning algorithms to identify 3 key diagnostic biomarkers for PANoptosis in IPF: MMP9, FCMR, NIBAN3. Consensus clustering algorithm was applied to recognize two PANoptosis-related subtypes. Cluster 1 exhibited higher abundance of adaptive immune response cells and significant enrichment in DNA damage and repair-related pathways. Cluster 2 exhibited greater prevalence of innate immune response cells and predominant enhancement in pathways related to lipid cholesterol metabolism and vascular remodeling. Diagnostic models were developed with the aid of clinical decision-making and a novel approach to the diagnosis and treatment for IPF.

Abstract Background Although adequate physical activity has been shown to be beneficial in early breast cancer, evidence in metastatic breast cancer is sparse and contradictory, which could be related to distinct effects of physical activity on the different molecular cancer subtypes. Therefore, we here evaluated the effect of physical activity on progression-free and overall survival (PFS, OS) in metastatic breast cancer, specifically looking at molecular subtypes. Methods International Physical Activity Questionnaire (IPAQ) questionnaires, filled out by patients enrolled in the prospective PRAEGNANT registry (NCT02338167; n = 1,270) were used to calculate metabolic equivalent task (MET) minutes, which were subsequently categorized into low (n = 138), moderate (n = 995) or high IPAQ categories (n = 137). Cox regression analyses were used to evaluate the impact of IPAQ categories and its interaction with molecular subtypes on PFS and OS. Results Patient and tumor characteristics were equally distributed across IPAQ categories. HER2pos, HRpos and TNBC were present in 23.1%, 65.7% and 11.2% of patients, respectively. IPAQ scores did not have an impact on PFS and OS in addition to established prognostic factors, either overall or in particular molecular subtypes (PFS: p = 0.33 and OS: p = 0.08, likelihood ratio test). Exploratory analyses showed higher overall survival rates for high IPAQ categories compared to low/moderate IPAQ categories in luminal B-like breast cancer. Conclusions Self-reported physical activity using the IPAQ questionnaire did not significantly affect PFS or OS in patients suffering from metastatic breast cancer. Nevertheless, some hypothesis-generating differences between molecular subtypes could be observed, which may be interesting to evaluate further.

Abstract Background Acute myeloid leukemia (AML) is a genetically heterogeneous disease in which glutamine (Gln) contributes to AML progression. Therefore, this study aimed to identify potential prognostic biomarkers for AML based on Gln metabolism-related genes. Methods Gln-related genes that were differentially expressed between Cancer Genome Atlas-based AML and normal samples were analyzed using the limma package. Univariate, least absolute shrinkage, selection operators, and stepwise Cox regression analyses were used to identify prognostic signatures. Risk score-based prognostic and nomogram models were constructed to predict the prognostic risk of AML. Subsequently, consistent cluster analysis was performed to stratify patients into different subtypes, and subtype-related module genes were screened using weighted gene co-expression network analysis. Results Through a series of regression analyses, HGF, ANGPTL3, MB, F2, CALR, EIF4EBP1, EPHX1, and PDHA1 were identified as potential prognostic biomarkers of AML. Prognostic and nomogram models constructed based on these genes could significantly differentiate between high- and low-risk AML with high predictive accuracy. The eight-signature also stratified patients with AML into two subtypes, among which Cluster 2 was prone to a high risk of AML prognosis. These two clusters exhibited different immune profiles. Of the subtype-related module genes, the HOXA and HOXB family genes may be genetic features of AML subtypes. Conclusion Eight Gln metabolism-related genes were identified as potential biomarkers of AML to predict prognostic risk. The molecular subtypes clustered by these genes enabled prognostic risk stratification.

Abstract Objective Endometrial cancer is stepping into the era of precision therapy. Genomic test is recommended for newly diagnostic patients. However, outpatients displayed more complex characteristics. Here, we elucidated the clinical characteristics and genomic profiling of outpatients with endometrial cancer at our institution. Methods Between 2018 and 2023, 68 endometrial cancer received genomic tests at outpatient department of Fudan University Shanghai Cancer Center. Data, including age, pathological histology, FIGO stage and treatment strategy were collected. Germline mutations, molecular subtypes and other somatic mutations were also summarized. Results Overall, 72.1% (49/68) of patients receive genomic tests at primary diagnosis, while 27.9% (19/68) of patients received tests at recurrence. Nine patients had deleterious germline mutations, including BRCA1(2), MLH1(1), MSH2(2, including one with co-mutation of RAD50), MSH6(2), FANCA(1), MUTYH(1). Molecular subtypes were recognized among 62 patients, as POLE super-mutation(4, 6.5%), MSI-H(7, 11.3%), CN-Low(36, 58.1%) and CN-High(15, 24.2%). Ten patients received anti-PD1 monotherapy or in combination with chemotherapy or anti-angiogenic therapy, with the duration of disease control of 1 to 35 months. The ORR rate was 30%, and six patients had stable disease. The median (range) follow-up time was 18(2–160) months. 23(33.8%) relapses were recorded, and CN-High subtype displayed worst PFS compared with other subtypes (P

Abstract PANoptosis is engaged in the program of immune response and carcinogenicity. Nonetheless, the actual impacts of PANoptosis on clinical management and oncology immunity in hepatocellular carcinoma (HCC) are not fully grasped. RNA-seq-derived computations were conducted to sort out the molecular subtypes and elucidate the disparities based on PANoptosis molecules. Single-cell sequencing (scRNA-seq) tools including Cytotrace and Addmodulescore were extracted to characterize diversification potency and quantify the PANoptosis motion. Transcriptional factors were inferred by the pySCENIC package and Cellchat program scrutinized the intercellular exchange across cell compartments. The PANoptosis score system originated by incorporating 10 machine learning algorithms and 101 compositions to project clinical results and deteriorate tendencies. Circulatory PANoptosis-associated protein HSP90AA1 was determined by enzyme-linked immunosorbent assay (ELISA). HCC individuals could be categorized into low- and high-PANoptosis groups with diverse biogenic and pharmacotherapy heterogeneity. Individuals in the elevated PANoptosis subtype were characterized as “hot tumor” conveying the increased presence of immunogenicity while reiterating an explicit negative connection with tumor stemness. Compared to immune and stromal cells, cancerous cells showcased decreased PANoptosis and heightened PANoptosis malignant cell subgroups might be tied to a substantial level of genomic expression of SREBF2, JUND, GATAD1, ZBTB20, SMAD5 and implied a more aggressive potential. The PANoptosis index, derived from machine learning, has been established to provide succinct frameworks for predicting outcomes and clarified the noteworthy utility of conventional regimens, as the differentiated power of HCC occurred together with vascular invasion and hepatocellular adenoma (HCA). The experiment confirmed that the circulating HSP90AA1 was aberrantly augmented in HCC patients, thus demonstrating its potential as a discriminatory biomarker. We systematically deciphered the molecular and immune ecosystem traits of PANoptosis in bulk and scRNA-seq degrees, which may deliver advantageous insights for customized treatment, awareness of the pathological process and prognosis scrutiny for HCC patients.

Purpose: To characterize the histologic and molecular subtype distribution of, and survival from, breast cancer (BC) among Canadian women overall, and by stage and age at diagnosis. Methods: Invasive BC cases from the Canadian Cancer Registry for women aged 15–99 years between 2012 and 2017 in Canada, excluding Quebec, were examined using pre-existing mortality linkages. Stage at diagnosis, molecular, and histologic subtypes, and 5-year net survival (NS) by age, subtype, and stage were determined. Results: 107,271 women with BC were included. Luminal A was the most common subtype, present in increasing proportions as women aged, up to a maximum of 55% of cases in 70–74. Ductal and luminal A were most likely to be diagnosed at stage I, while HER2+ had the highest proportion of diagnosis at stage III; triple negative (TN) and unknown had the highest proportion of stage IV. For all stages combined, luminal A had a five-year NS of 98%, while TN was 74%. NS for stage I BC was 99–100% for all subtypes, excepting TN, which was 96%. Survival decreased with advancing stage, most markedly for TN, for which stage III was 47% and stage IV 7%. Survival by equivalent stage and subtype was comparable across age groups but declined in older age categories. Conclusions: The varying natural histories of BC subtypes and histologies can inform prognoses, health system economics, and screening practices. The NS of 96% or greater for stage I, regardless of subtype, highlights the importance of early detection for all subtypes of BC, especially in aggressive subtypes.

Autoimmune diseases (ADs) are characterized by their complexity and a wide range of clinical differences. Despite patients presenting with similar symptoms and disease patterns, their reactions to treatments may vary. The current approach of personalized medicine, which relies on molecular data, is seen as an effective method to address the variability in these diseases. This review examined the pathologic classification of ADs, such as multiple sclerosis and lupus nephritis, over time. Acknowledging the limitations inherent in pathologic classification, the focus shifted to molecular classification to achieve a deeper insight into disease heterogeneity. The study outlined the established methods and findings from the molecular classification of ADs, categorizing systemic lupus erythematosus (SLE) into four subtypes, inflammatory bowel disease (IBD) into two, rheumatoid arthritis (RA) into three, and multiple sclerosis (MS) into a single subtype. It was observed that the high inflammation subtype of IBD, the RA inflammation subtype, and the MS ''inflammation & EGF'' subtype share similarities. These subtypes all display a consistent pattern of inflammation that is primarily driven by the activation of the JAK-STAT pathway, with the effective drugs being those that target this signaling pathway. Additionally, by identifying markers that are uniquely associated with the various subtypes within the same disease, the study was able to describe the differences between subtypes in detail. The findings are expected to contribute to the development of personalized treatment plans for patients and establish a strong basis for tailored approaches to treating autoimmune diseases.

Small cell lung cancer (SCLC), one of the histological subtypes of lung cancer, is characterized by high proliferation, early metastasis, susceptibility to drug resistance and recurrence. For several years, SCLC has always been regarded as a homogeneous disease, treated with a unified radiotherapy and chemotherapy strategy. Despite significant early therapeutic effects, drug resistance and recurrence occur quickly, and there is a lack of satisfactory treatment results, which may be due to insufficient understanding of the tumor heterogeneity of SCLC at present. Recently, the concept of SCLC molecular subtype based on the definition of relatively high expression of lineage transcription factors has been proposed in preclinical studies. This article mainly elaborates on the current status and latest findings of SCLC molecular subtype, emphasizing the potential problems that molecular typing may encounter in clinical practice, aiming to promote understanding of the research progress of molecular subtype in SCLC.

Large cell neuroendocrine carcinoma (LCNEC) of lung is a rare neuroendocrine carcinoma subtype with difficulty in early diagnosis and poor prognosis which is treated with standard strategies of small cell lung cancer and non-small cell lung cancer. In recent years, the precise types of LCNEC and its response to therapy have been identified by next-generation sequencing. Some researches have also found the correlation between different subtypes of LCNEC and the efficacy of chemotherapy regimens. However, there is no consensual agreement of its therapy. Recently, immune checkpoint inhibitors (ICIs) has provided a new option for LCNEC patients based on some retrospective research data and case reports. In this review, we aimed to summarize the epidemiological characteristics, standard therapy, the advances of molecular subtypes and clinical applications of ICIs of LCNEC, so as to provide optimal systemic clinical decision-making for LCNEC patients.

Abstract Background With a series of clinical trials confirming the sensitivity of small cell lung cancer (SCLC) to immunotherapy, research on personalized treatment for SCLC has gained increasing attention. Currently, the most widely accepted subtype of SCLC is based on the expression levels of Achaete-Scute Family BHLH Transcription Factor 1 (ASCL1), neurogenic differentiation factor 1 (NEUROD1), and POU class 2 homeobox 3 (POU2F3). However, real-world studies on this classification remain limited. Methods We retrospectively collected biopsy specimens from patients who received immunotherapy at Shandong Provincial Hospital between January 2019 and July 2021. After determining the patient subtypes using immunohistochemistry, we analyzed the relationships between each subtype and survival as well as some clinical characteristics. Results In our study, we found that the subtype I achieved a significant survival advantage compared to the other groups. Additionally, the subtype A demonstrated a significant survival disadvantage. Among patients in the subtype I, there was a higher proportion of early brain metastasis and patients with a family history of tumors, while the subtype A had a lower proportion. Furthermore, the subtype A exhibited relatively poor immune infiltration. Conclusion In a diverse cohort of SCLC patients receiving immunotherapy, the subtype-I showed significant survival advantages while the subtype-A experienced a worse survival.

Abstract Background Pancreatic cancer is anatomically divided into pancreatic head and body/tail cancers, and some studies have reported differences in prognosis. However, whether this discrepancy is induced from the difference of tumor biology is hotly debated. Therefore, we aimed to evaluate the differences in clinical outcomes and tumor biology depending on the tumor location. Methods In this retrospective cohort study, we identified 800 patients with pancreatic ductal adenocarcinoma who had undergone upfront curative-intent surgery. Cox regression analysis was performed to explore the prognostic impact of the tumor location. Among them, 153 patients with sufficient tumor tissue and blood samples who provided informed consent for next-generation sequencing were selected as the cohort for genomic analysis. Results Out of the 800 patients, 500 (62.5%) had pancreatic head cancer, and 300 (37.5%) had body/tail cancer. Tumor location in the body/tail of the pancreas was not identified as a significant predictor of survival outcomes compared to that in the head in multivariate analysis (hazard ratio, 0.94; 95% confidence interval, 0.77–1.14; P = 0.511). Additionally, in the genomic analyses of 153 patients, there were no significant differences in mutational landscapes, distribution of subtypes based on transcriptomic profiling, and estimated infiltration levels of various immune cells between pancreatic head and body/tail cancers. Conclusions We could not find differences in prognosis and tumor biology depending on tumor location in pancreatic ductal adenocarcinoma. Discrepancies in prognosis may represent a combination of lead time, selection bias, and clinical differences, including the surgical burden between tumor sites.

Abstract Background Prostate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear. Methods Somatic mutations and copy number alterations of TEASEs were comprehensively analyzed. Subsequently, the transcripts of prostate cancer patients in TCGA and GEO databases were integrated to delineate new molecular subtypes. Followed by constructing a risk model containing nine characteristic genes through Lasso regression and Cox prognostic analysis among different subtypes. Various aspects including prognosis, tumor microenvironment (TME), landscape of immunity, tumor mutational burden (TMB), stem cell correlation, and median inhibitory concentration amongst different risk groups were compared. Finally, the expression, prognosis, and malignant biological behavior of ZW10 interactor (ZWINT) in vitro was explored. Results TEASEs exhibited a notably high mutation frequency. Three distinct molecular subtypes and two gene subclusters based on TEASEs were delineated, displaying significant associations with prognosis, immune function regulation, and clinical characteristics. Low-risk patients demonstrated superior prognosis and better response to immunotherapy. Conversely, high-risk patients exhibited higher TMB and stronger stem cell correlations. It was also found that the patients’ sensitivity to chemotherapy agents was impacted by the risk score. Finally, ZWINT’s potential as a novel diagnostic and prognostic biomarker for prostate cancer was validated. Conclusions TEASEs play a pivotal role in modulating immune regulation and immunotherapeutic responses, thereby significantly impacting the diagnosis, prognosis, and treatment strategies for affected patients.

Abstract Background As the most malignant tumor of the female reproductive system, ovarian cancer (OC) has garnered increasing attention. The Warburg effect, driven by glycolysis, accounts for tumor cell proliferation under aerobic conditions. However, the metabolic heterogeneity linked to glycolysis in OC remains elusive. Methods We integrated single-cell data with OC to score glycolysis level in tumor cell subclusters. This led to the identification of a subcluster predominantly characterized by glycolysis, with a strong correlation to patient prognosis. Core transcription factors were pinpointed using hdWGCNA and metaVIPER. A specific transcription factor regulatory network was then constructed. A glycolysis-related prognostic model was developed and tested for estimating OC prognosis with a total of 85 machine-learning combinations, focusing on specific upregulated genes of two subtypes. We identified IGF2 as a key within the prognostic model and investigated its impact on OC progression and drug resistance through in vitro experiments, including the transwell assay, lactate production detection, and the CCK-8 assay. Results Analysis showed that the Malignant 7 subcluster was primarily related to glycolysis. Two OC molecular subtypes, CS1 and CS2, were identified with distinct clinical, biological, and microenvironmental traits. A prognostic model was built, and IGF2 emerged as a key gene linked to prognosis. Experiments have proven that IGF2 can promote the glycolysis pathway and the malignant biological progression of OC cells. Conclusions We developed two novel OC subtypes based on glycolysis score, established a stable prognostic model, and identified IGF2 as the marker gene. These insights provided a new avenue for exploring OC’s molecular mechanisms and personalized treatment approaches.

Abstract Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis.

Abstract Background Amyotrophic Lateral Sclerosis (ALS) is a highly heterogenous neurodegenerative disorder that primarily affects upper and lower motor neurons, affecting additional cell types and brain regions. Underlying molecular mechanisms are still elusive, in part due to disease heterogeneity. Molecular disease subtyping through integrative analyses including RNA editing profiling is a novel approach for identification of molecular networks involved in pathogenesis. Methods We aimed to highlight the role of RNA editing in ALS, focusing on the frontal cortex and the prevalent molecular disease subtype (ALS-Ox), previously determined by transcriptomic profile stratification. We established global RNA editing (editome) and gene expression (transcriptome) profiles in control and ALS-Ox cases, utilizing publicly available RNA-seq data (GSE153960) and an in-house analysis pipeline. Functional annotation and pathway analyses identified molecular processes affected by RNA editing alterations. Pearson correlation analyses assessed RNA editing effects on expression. Similar analyses on additional ALS-Ox and control samples (GSE124439) were performed for verification. Targeted re-sequencing and qRT-PCR analysis targeting CACNA1C, were performed using frontal cortex tissue from ALS and control samples (n = 3 samples/group). Results We identified reduced global RNA editing in the frontal cortex of ALS-Ox cases. Differentially edited transcripts are enriched in synapses, particularly in the glutamatergic synapse pathway. Bioinformatic analyses on additional ALS-Ox and control RNA-seq data verified these findings. We identified increased recoding at the Q621R site in the GRIK2 transcript and determined positive correlations between RNA editing and gene expression alterations in ionotropic receptor subunits GRIA2, GRIA3 and the CACNA1C transcript, which encodes the pore forming subunit of a post-synaptic L-type calcium channel. Experimental data verified RNA editing alterations and editing-expression correlation in CACNA1C, highlighting CACNA1C as a target for further study. Conclusions We provide evidence on the involvement of RNA editing in the frontal cortex of an ALS molecular subtype, highlighting a modulatory role mediated though recoding and gene expression regulation on glutamatergic synapse related transcripts. We report RNA editing effects in disease-related transcripts and validated editing alterations in CACNA1C. Our study provides targets for further functional studies that could shed light in underlying disease mechanisms enabling novel therapeutic approaches.

Abstract Background Lupus nephritis (LN) is a severe complication of systemic lupus erythematosus (SLE) with poor treatment outcomes. The role and underlying mechanisms of ferroptosis in LN remain largely unknown. We aimed to explore ferroptosis-related molecular subtypes and assess their prognostic value in LN patients. Methods Molecular subtypes were classified on the basis of differentially expressed ferroptosis-related genes (FRGs) via the Consensus ClusterPlus package. The enriched functions and pathways, immune infiltrating levels, immune scores, and immune checkpoints were compared between the subgroups. A scoring algorithm based on the subtype-specific feature genes identified by artificial neural network machine learning, referred to as the NeuraLN, was established, and its immunological features, clinical value, and predictive value were evaluated in patients with LN. Finally, immunohistochemical analysis was performed to validate the expression and role of feature genes in glomerular tissues from LN patients and controls. Results A total of 10 differentially expressed FRGs were identified, most of which showed significant correlation. Based on the 10 FRGs, LN patients were classified into two ferroptosis subtypes, which exhibited significant differences in immune cell abundances, immune scores, and immune checkpoint expression. A NeuraLN-related protective model was established based on nine subtype-specific genes, and it exhibited a robustly predictive value in LN. The nomogram and calibration curves demonstrated the clinical benefits of the protective model. The high-NeuraLN group was closely associated with immune activation. Clinical specimens demonstrated the alterations of ALB, BHMT, GAMT, GSTA1, and HAO2 were in accordance with bioinformatics analysis results, GSTA1 and BHMT were negatively correlated with the severity of LN. Conclusion The classification of ferroptosis subtypes and the establishment of a protective model may form a foundation for the personalized treatment of LN patients.

Objective: Circulating tumor DNA (ctDNA) is increasingly being used as a potential prognostic biomarker in cancer patients. We aimed to assess the prognostic value of ctDNA in different subtypes of breast cancer patients throughout the whole treatment cycle. Materials and methods: PubMed, Web of Science, Embase, Cochrane Library, Scopus, and clinical trials.gov databases were searched from January 2016 to May 2022. The following search terms were used: ctDNA OR circulating tumor DNA AND breast cancer OR breast carcinoma. Only studies written in English were included. The following pre-specified criteria should be met for inclusion: (i) original articles, conference abstracts, etc.; (ii) patients with breast cancer; (iii) ctDNA measurement; and (iv) clinical outcome data such as recurrence-free survival (RFS) and overall survival (OS). The random-effects model was preferred considering the potential heterogeneity across studies. The main outcomes are ctDNA detection rate and postoperative long-term outcomes (RFS and OS). Results: A total of 24 studies were screened. At every measurement time, the ctDNA detection rate of the HR+ subgroup was similar to that of the HR- subgroup (P = 0.075; P = 0.458; P = 0.744; and P = 0.578), and the ctDNA detection rate of the HER2+ subgroup was similar to that of the HER2- subgroup (P = 0.805; P = 0.271; P = 0.807; and P = 0.703). In the HR+ subgroup, RFS and OS of ctDNA positive patients were similar to those of ctDNA negative patients (P = 0.589 and P = 0.110), while RFS and OS of the ctDNA positive group was significantly shorter than those of the ctDNA negative patients in the HR- subgroup (HR = 4.03, P < 0.001; HR = 3.21, P < 0.001). According to HER grouping, the results were the same as above. In the triple negative breast cancer (TNBC) subgroup, the RFS and OS of ctDNA-positive patients was significantly shorter than of the ctDNA negative patients before and after surgery. Conclusions: ctDNA was more predictive of recurrence-free survival and overall survival in the HR- subgroup than in the HR+ subgroup, and the same result was showed in the HER2- subgroup vs. HER2+ subgroup. The prognosis of the TNBC subtype is closely related to ctDNA before and after surgery.

Abstract Objective Examining the distribution of breast cancer (BC) stage and molecular subtype among women aged below ( 65 years) the recommended screening age range helps to understand the screening program's characteristics and contributes to enhancing the effectiveness of BC screening programs. Methods In this retrospective study, female patients with newly diagnosed BC from 2010 to 2020 were identified. The distribution of cases in terms of TNM stages, severity classes, and subtypes was analysed according to age groups. Results A total of 3282 women diagnosed with BC were included in the analysis. Among these cases 51.4% were detected outside the screening age group, and these were characterized by a higher TNM stage compared to those diagnosed within the screening age band. We observed significantly higher relative frequency of advanced BC in the older age group compared to both the screening age population and women younger than 45 years (14.9% vs. 8.7% and 7.7%, P

Abstract Background Colorectal cancer (CRC) is a serious global health burden because of its high morbidity and mortality rates. Hypoxia and massive lactate production are hallmarks of the CRC microenvironment. However, the effects of hypoxia and lactate metabolism on CRC have not been fully elucidated. This study aimed to develop a novel molecular subtyping based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs) and construct a signature to predict the prognosis of patients with CRC and treatment efficacy. Methods Bulk and single-cell RNA-sequencing and clinical data of CRC were downloaded from the TCGA and GEO databases. HRGs and LMRGs were obtained from the Molecular Signatures Database. The R software package DESeq2 was used to perform differential expression analysis. Molecular subtyping was performed using unsupervised clustering. A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses. Finally, the sensitivity of tumor cells to chemotherapeutic agents before and after hypoxia was verified using in vitro experiments. Results We classified 575 patients with CRC into three molecular subtypes and were able to distinguish their prognoses clearly. The C1 subtype, which exhibits high levels of hypoxia, has a low proportion of CD8 + T cells and a high proportion of macrophages. The expression of immune checkpoint genes is generally elevated in C1 patients with severe immune dysfunction. Subsequently, we constructed a predictive model, the HLM score, which effectively predicts the prognosis of patients with CRC and the efficacy of immunotherapy. The HLM score was validated in GSE39582, GSE106584, GSE17536, and IMvigor210 datasets. Patients with high HLM scores exhibit high infiltration of CD8 + exhausted T cells (Tex), especially terminal Tex, and oxidative phosphorylation (OXPHOS)−Tex in the immune microenvironment. Finally, in vitro experiments confirmed that CRC cell lines were less sensitive to 5-fluorouracil, oxaliplatin, and irinotecan under hypoxic conditions. Conclusion We constructed novel hypoxia- and lactate metabolism-related molecular subtypes and revealed their immunological and genetic characteristics. We also developed an HLM scoring system that could be used to predict the prognosis and efficacy of immunotherapy in patients with CRC.

Abstract Background Considering the age relevance of prostate cancer (PCa) and the involvement of the cGAS-STING pathway in aging and cancer, we aim to classify PCa into distinct molecular subtypes and identify key genes from the novel perspective of the cGAS-STING pathway. It is of significance to guide personalized intervention of cancer-targeting therapy based on genetic evidence. Methods The 430 patients with PCa from the TCGA database were included. We integrated 29 key genes involved in cGAS-STING pathway and analyzed differentially expressed genes and biochemical recurrence (BCR)-free survival-related genes. The assessments of tumor stemness and heterogeneity and tumor microenvironment (TME) were conducted to reveal potential mechanisms. Results PCa patients were classified into two distinct subtypes using AURKB, TREX1, and STAT6, and subtype 1 had a worse prognosis than subtype 2 (HR: 21.19, p

Abstract Ulcerative colitis (UC) is characterized by an abnormal immune response, and the pathogenesis lacks clear understanding. The cGAS-STING pathway is an innate immune signaling pathway that plays a significant role in various pathophysiological processes. However, the role of the cGAS-STING pathway in UC remains largely unclear. In this study, we obtained transcriptome sequencing data from multiple publicly available databases. cGAS-STING related genes were obtained through literature search, and differentially expressed genes (DEGs) were analyzed using R package limma. Hub genes were identified through protein–protein interaction (PPI) network analysis and module construction. The ConsensuClusterPlus package was utilized to identify molecular subtypes based on hub genes. The therapeutic response, immune microenvironment, and biological pathways of subtypes were further investigated. A total of 18 DEGs were found in UC patients. We further identified IFI16, MB21D1 (CGAS), TMEM173 (STING) and TBK1 as the hub genes. These genes are highly expressed in UC. IFI16 exhibited the highest diagnostic value and predictive value for response to anti-TNF therapy. The expression level of IFI16 was higher in non-responders to anti-TNF therapy. Furthermore, a cluster analysis based on genes related to the cGAS-STING pathway revealed that patients with higher gene expression exhibited elevated immune burden and inflammation levels. This study is a pioneering analysis of cGAS-STING pathway-related genes in UC. These findings provide new insights for the diagnosis of UC and the prediction of therapeutic response.

Background: With a series of clinical trials confirming the sensitivity of small cell lung cancer (SCLC) to immunotherapy, research on personalized treatment for SCLC has gained increasing attention. Currently, the most widely accepted subtype of SCLC is based on the expression levels of Achaete-Scute Family BHLH Transcription Factor 1 (ASCL1), neurogenic differentiation factor 1 (NEUROD1), and POU class 2 homeobox 3 (POU2F3). However, real-world studies on this classification remain limited. Methods: We retrospectively collected biopsy specimens from patients who received immunotherapy at Shandong Provincial Hospital between January 2019 and July 2021. After determining the patient subtypes using immunohistochemistry, we analyzed the relationships between each subtype and survival as well as some clinical characteristics. Results: In our study, we found that the subtype I achieved a significant survival advantage compared to the other groups. Additionally, the subtype A demonstrated a significant survival disadvantage. Among patients in the subtype I, there was a higher proportion of early brain metastasis and patients with a family history of tumors, while the subtype A had a lower proportion. Furthermore, the subtype A exhibited relatively poor immune infiltration. Conclusion: In a diverse cohort of SCLC patients receiving immunotherapy, the subtype-I showed significant survival advantages while the subtype-A experienced a worse survival. [ABSTRACT FROM AUTHOR]

Background: Pancreatic cancer is anatomically divided into pancreatic head and body/tail cancers, and some studies have reported differences in prognosis. However, whether this discrepancy is induced from the difference of tumor biology is hotly debated. Therefore, we aimed to evaluate the differences in clinical outcomes and tumor biology depending on the tumor location. Methods: In this retrospective cohort study, we identified 800 patients with pancreatic ductal adenocarcinoma who had undergone upfront curative-intent surgery. Cox regression analysis was performed to explore the prognostic impact of the tumor location. Among them, 153 patients with sufficient tumor tissue and blood samples who provided informed consent for next-generation sequencing were selected as the cohort for genomic analysis. Results: Out of the 800 patients, 500 (62.5%) had pancreatic head cancer, and 300 (37.5%) had body/tail cancer. Tumor location in the body/tail of the pancreas was not identified as a significant predictor of survival outcomes compared to that in the head in multivariate analysis (hazard ratio, 0.94; 95% confidence interval, 0.77–1.14; P = 0.511). Additionally, in the genomic analyses of 153 patients, there were no significant differences in mutational landscapes, distribution of subtypes based on transcriptomic profiling, and estimated infiltration levels of various immune cells between pancreatic head and body/tail cancers. Conclusions: We could not find differences in prognosis and tumor biology depending on tumor location in pancreatic ductal adenocarcinoma. Discrepancies in prognosis may represent a combination of lead time, selection bias, and clinical differences, including the surgical burden between tumor sites. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer ranks among the six most lethal malignancies worldwide. Telomerase, a reverse transcriptase enzyme, plays a pivotal role in extending cellular telomeres and is intimately associated with cell proliferation and division. However, the interconnection between prostate cancer and telomerase-related genes (TEASEs) remains unclear. Methods: Somatic mutations and copy number alterations of TEASEs were comprehensively analyzed. Subsequently, the transcripts of prostate cancer patients in TCGA and GEO databases were integrated to delineate new molecular subtypes. Followed by constructing a risk model containing nine characteristic genes through Lasso regression and Cox prognostic analysis among different subtypes. Various aspects including prognosis, tumor microenvironment (TME), landscape of immunity, tumor mutational burden (TMB), stem cell correlation, and median inhibitory concentration amongst different risk groups were compared. Finally, the expression, prognosis, and malignant biological behavior of ZW10 interactor (ZWINT) in vitro was explored. Results: TEASEs exhibited a notably high mutation frequency. Three distinct molecular subtypes and two gene subclusters based on TEASEs were delineated, displaying significant associations with prognosis, immune function regulation, and clinical characteristics. Low-risk patients demonstrated superior prognosis and better response to immunotherapy. Conversely, high-risk patients exhibited higher TMB and stronger stem cell correlations. It was also found that the patients' sensitivity to chemotherapy agents was impacted by the risk score. Finally, ZWINT's potential as a novel diagnostic and prognostic biomarker for prostate cancer was validated. Conclusions: TEASEs play a pivotal role in modulating immune regulation and immunotherapeutic responses, thereby significantly impacting the diagnosis, prognosis, and treatment strategies for affected patients. [ABSTRACT FROM AUTHOR]

Background: Mitochondrial dysfunction has been shown to play a critical role in cancer biology. However, its involvement in intrahepatic cholangiocarcinoma (iCCA) remains significantly understudied. Methods: RNA sequencing data of 30 pairs of iCCA and paracancerous tissues were collected from the First Affiliated Hospital of Wenzhou Medical University (WMU). The WMU cohort (n = 30) was integrated with public TCGA (n = 30) and GSE107943 (n = 30) datasets to establish a multi-center iCCA cohort. We merged the TCGA and GSE107943 cohorts into an exploration cohort to develop a mitochondria signature for prognosis assessment, and utilized the WMU cohort for external validation. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Hallmarker analyses were used for functional interpretation of iCCA associated mitochondria-related genes (MRGs). In addition, unsupervised clustering was performed to identify mitochondria-based iCCA subtypes with the data of three institutions. Further investigations were conducted to examine the impact of mitochondrial dysfunction on drug responses, alteration of the tumor immune microenvironment, and immune responses. Results: Two hundred and sixty-three iCCA-related MRGs were identified to be related to fatty acid metabolism, oxidative phosphorylation, and apoptosis. Through univariate and multivariate Cox, and LASSO analyses, a mitochondria signature with five optimal MRGs was established to evaluate the prognosis of iCCA patients with the AUC values ranged from 0.785 to 0.928 in the exploration cohort. The signature also exhibited satisfactory performance in the WMU cohort with AUC values of 0.817-0.871, and was identified as an independent risk predictor in both cohorts. Additionally, we found that patients with higher mitochondria score with poor prognosis presented lower infiltration levels of CD4+ T-cell, NK cells, and monocytes, and demonstrated higher sensitivity to targeted therapies, including sorafenib. Furthermore, two distant mitochondria-based subtypes were determined, and subtype 2 was associated with shorter survival time and immunosuppressive tumor microenvironment. Finally, the differential protein expression of five key MRGs was verified by Immunohistochemistry. Conclusion: We found mitochondrial dysfunction modulates aberrant metabolism, oxidative stress, immune responses, apoptosis, and drug sensitivity in iCCA. A mitochondria signature and two mitochondria-based iCCA subtypes were identified for clinical risk stratification and immunophenotyping. [ABSTRACT FROM AUTHOR]

Background: Breast cancer (BC) is the second most common etiology of brain metastases (BrM). We aimed to examine the incidence of BrM among all BC patients presenting to a large tertiary cancer centre over one decade. Methods: We included all BC patients presenting consecutively between 2009 and 2019 and cross referenced that cohort to a radiotherapy database, identifying patients treated for BrM at any time following their initial presentation. Cumulative incidences (CI) of BrM diagnoses were calculated using death as a competing risk and compared using the Fine-Gray method. Overall survival was estimated using the Kaplan Meier method. Results: We identified 12,995 unique patients. The CI of BrM in patients who initially presented with Stage 0–4 disease was 2.1%, 3.7%, 9.4%, 10.6%, and 28.7%, respectively at 10 years. For 8,951 patients with available molecular subtype data, 6,470 (72%), 961 (11%), 1,023 (11%), and 497 (6%) had hormone-receptor (HR)-positive/ERBB2-, HR-negative/ERBB2-, HR-positive/ERBB2 + , and HR-negative/ERBB2 + disease, respectively; the CI of BrM in each was 7.6%, 25.3%, 24.1%, and 26.6%, at 10 years following BC diagnosis, respectively. Median overall survival (OS) following BC diagnosis and BrM diagnosis was 28 years 95% CI [25, 32] and 10 months 95% CI [9, 12], respectively. Conclusions: From a large, registry-based study, we observed that patients with ERBB2 + and triple negative BC have the highest incidence of BrM. Our data supports prospective surveillance brain MRI studies. Given advancements in BrM treatment, clinicians should have a low threshold for brain imaging in BC patients with high risk subtypes. [ABSTRACT FROM AUTHOR]

Pancreatic ductal adenocarcinoma (PDAC) is a heterogeneous disease with distinct molecular subtypes described as classical/progenitor and basal‐like/squamous PDAC. We hypothesized that integrative transcriptome and metabolome approaches can identify candidate genes whose inactivation contributes to the development of the aggressive basal‐like/squamous subtype. Using our integrated approach, we identified endosome–lysosome associated apoptosis and autophagy regulator 1 (ELAPOR1/KIAA1324) as a candidate tumor suppressor in both our NCI‐UMD‐German cohort and additional validation cohorts. Diminished ELAPOR1 expression was linked to high histological grade, advanced disease stage, the basal‐like/squamous subtype, and reduced patient survival in PDAC. In vitro experiments demonstrated that ELAPOR1 transgene expression not only inhibited the migration and invasion of PDAC cells but also induced gene expression characteristics associated with the classical/progenitor subtype. Metabolome analysis of patient tumors and PDAC cells revealed a metabolic program associated with both upregulated ELAPOR1 and the classical/progenitor subtype, encompassing upregulated lipogenesis and downregulated amino acid metabolism. 1‐Methylnicotinamide, a known oncometabolite derived from S‐adenosylmethionine, was inversely associated with ELAPOR1 expression and promoted migration and invasion of PDAC cells in vitro. Taken together, our data suggest that enhanced ELAPOR1 expression promotes transcriptome and metabolome characteristics that are indicative of the classical/progenitor subtype, whereas its reduction associates with basal‐like/squamous tumors with increased disease aggressiveness in PDAC patients. These findings position ELAPOR1 as a promising candidate for diagnostic and therapeutic targeting in PDAC. [ABSTRACT FROM AUTHOR]

Glioblastoma (GBM) is a highly aggressive, infiltrative malignancy that cannot be completely cured by current treatment modalities, and therefore requires more precise molecular subtype signatures to predict treatment response for personalized precision therapy. Expression subtypes of GBM samples from the Cancer Genome Atlas (TCGA) were identified using BayesNM and compared with existing molecular subtypes of GBM. Biological features of the subtypes were determined by single-sample gene set enrichment analysis. Genomic and proteomic data from GBM samples were combined and Genomic Identification of Significant Targets in Cancer analysis was used to screen genes with recurrent somatic copy-number alterations phenomenon. The immune environment among subtypes was compared by assessing the expression of immune molecules and the infiltration of immune cells. Molecular subtypes adapted to immunotherapy were identified based on Tumor Immune Dysfunction and Exclusion (TIDE) score. Finally, least absolute shrinkage and selection operator (LASSO) logistic regression was performed on the expression profiles of S2, S3 and S4 in TCGA-GBM and RPPA to determine the respective corresponding best predictive model. Four novel molecular subtypes were classified. Specifically, S1 exhibited a low proliferative profile; S2 exhibited the profile of high proliferation, IDH1 mutation, TP53 mutation and deletion; S3 was characterized by high immune scores, innate immunity and adaptive immune infiltration scores, with the lowest TIDE score and was most likely to benefit from immunotherapy; S4 was characterized by high proliferation, EGFR amplification, and high protein abundance, and was the most suitable subtype for bevacizumab. LASSO analysis constructed the best prediction model composed of 13 genes in S2 with an accuracy of 96.7%, and the prediction model consisting of 17 genes in S3 with an accuracy of 86.7%, and screened 14 genes as components of the best prediction model in S4 with an accuracy of 93%. To conclude, our study classified reproducible and robust molecular subtypes of GBM, and these findings might contribute to the identification of patients responding to immunotherapy, thereby improving GBM prognosis. [ABSTRACT FROM AUTHOR]

Background: As the most malignant tumor of the female reproductive system, ovarian cancer (OC) has garnered increasing attention. The Warburg effect, driven by glycolysis, accounts for tumor cell proliferation under aerobic conditions. However, the metabolic heterogeneity linked to glycolysis in OC remains elusive. Methods: We integrated single-cell data with OC to score glycolysis level in tumor cell subclusters. This led to the identification of a subcluster predominantly characterized by glycolysis, with a strong correlation to patient prognosis. Core transcription factors were pinpointed using hdWGCNA and metaVIPER. A specific transcription factor regulatory network was then constructed. A glycolysis-related prognostic model was developed and tested for estimating OC prognosis with a total of 85 machine-learning combinations, focusing on specific upregulated genes of two subtypes. We identified IGF2 as a key within the prognostic model and investigated its impact on OC progression and drug resistance through in vitro experiments, including the transwell assay, lactate production detection, and the CCK-8 assay. Results: Analysis showed that the Malignant 7 subcluster was primarily related to glycolysis. Two OC molecular subtypes, CS1 and CS2, were identified with distinct clinical, biological, and microenvironmental traits. A prognostic model was built, and IGF2 emerged as a key gene linked to prognosis. Experiments have proven that IGF2 can promote the glycolysis pathway and the malignant biological progression of OC cells. Conclusions: We developed two novel OC subtypes based on glycolysis score, established a stable prognostic model, and identified IGF2 as the marker gene. These insights provided a new avenue for exploring OC's molecular mechanisms and personalized treatment approaches. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is among the most hackneyed malignancies. Even patients with identical clinical symptoms and the same TNM stage still exhibit radically different clinical outcomes after receiving equivalent treatment regimens, indicating extensive heterogeneity of CRC. Myriad molecular subtypes of CRC have been exploited for decades, including the most compelling consensus molecular subtype (CMS) classification that has been broadly applied for patient stratification and biomarker‐drug combination formulation. Encountering barriers to clinical translation, however, CMS classification fails to fully reflect inter‐ or intra‐tumor heterogeneity of CRC. As a consequence, addressing heterogeneity and precisely managing CRC patients with unique characteristics remain arduous tasks for clinicians. Review: In this review, we systematically summarize molecular subtypes of CRC and further elaborate on their clinical applications, limitations, and future orientations. Conclusion: In recent years, exploration of subtypes through cell lines, animal models, patient‐derived xenografts (PDXs), organoids, and clinical trials contributes to refining biological insights and unraveling subtype‐specific therapies in CRC. Therapeutic interventions including nanotechnology, clustered regulatory interspaced short palindromic repeat/CRISPR‐associated nuclease 9 (CRISPR/Cas9), gut microbiome, and liquid biopsy are powerful tools with the possibility to shift the immunologic landscape and outlook for CRC precise medicine. [ABSTRACT FROM AUTHOR]

Objective To explore the value of magnetic resonance relaxation time quantitative imaging in predicting molecular subtypes of invasive ductal carcinoma (IDC) of breast. Methods A total of 79 IDC patients underwent routine magnetic resonance imaging (MRI) scanning and relaxation time quantitative imaging. According to immunohistochemical results of lesions, patients were divided into different immunohistochemical index groups and molecular subtype groups. The differences in MRI signs, T1 values and T2 values of lesions were statistically compared between each group. Patient operating characteristic (ROC) curve was used to evaluate values of T1 and T2 alone and the combination of them in differential diagnosis of IDC molecular subtypes. Results There were 82 samples of lesion in 79 patients, in which, Luminal A type 16 (19.51%), Luminal B1 type 11 (13.41%), Luminal B2 type 27 (32.93%), tyrosine kinase receptor-2 overexpression type (Erb-B2) 14 (17.07%) and TNBC 14 (17.07%). There were no significant differences in patient age, lesion distribution, maximum diameter, morphology, margin and enhancement performance between patients with various molecular subtypes (P > 0.05). Among the immunohistochemical indexes, only the Ki-67 positive group had higher T1 value than the negative group (P < 0.05). In ROC analysis, the critical T1 value of Ki-67 positive lesions was 2 145 ms, Yoden index was 0.368, the sensitivity was 53.47%, the specificity was 83.33% and the area under the curve (AUC) was 0.640 (95%CI: 0.527-0.744). There were no significant differences in T1 and T2 values between Luminal A, Luminal B1, Luminal B2, Erb-B2 enriched and TNBC subtypes (P < 0.05). T1 and T2 values were lower in Luminal type lesion than those of TNBC type lesion (P＜0.05). ROC curve analysis showed that the combined T1 and T2 values were more effective in differentiating Luminal/TNBC type than those of T1 and T2 values alone. Conclusion T1 mapping can be used as one of the methods to predict the high or low expression levels of Ki67 in IDC tumors. The combination of T1 and T2 values can improve the ability to predict Luminal/TNBC subtypes. [ABSTRACT FROM AUTHOR]

Objective: Examining the distribution of breast cancer (BC) stage and molecular subtype among women aged below (< 45 years), within (45–65 years), and above (> 65 years) the recommended screening age range helps to understand the screening program's characteristics and contributes to enhancing the effectiveness of BC screening programs. Methods: In this retrospective study, female patients with newly diagnosed BC from 2010 to 2020 were identified. The distribution of cases in terms of TNM stages, severity classes, and subtypes was analysed according to age groups. Results: A total of 3282 women diagnosed with BC were included in the analysis. Among these cases 51.4% were detected outside the screening age group, and these were characterized by a higher TNM stage compared to those diagnosed within the screening age band. We observed significantly higher relative frequency of advanced BC in the older age group compared to both the screening age population and women younger than 45 years (14.9% vs. 8.7% and 7.7%, P < 0.001). HR−/HER2− and HER+ tumours were relatively more frequent among women under age 45 years (HR−/HER2−: 23.6%, HER2+: 20.5%) compared to those within the screening age range (HR−/HER2−: 13.4%, HER2+: 13.9%) and the older age group (HR−/HER2−: 10.4%, HER2+: 11.5%). Conclusions: The findings of our study shed light on potential areas for the improvement of BC screening programs (e.g., extending screening age group, adjusting screening frequency based on molecular subtype risk status) in Hungary and internationally, as well. [ABSTRACT FROM AUTHOR]

Background: Colorectal cancer (CRC) is a serious global health burden because of its high morbidity and mortality rates. Hypoxia and massive lactate production are hallmarks of the CRC microenvironment. However, the effects of hypoxia and lactate metabolism on CRC have not been fully elucidated. This study aimed to develop a novel molecular subtyping based on hypoxia-related genes (HRGs) and lactate metabolism-related genes (LMRGs) and construct a signature to predict the prognosis of patients with CRC and treatment efficacy. Methods: Bulk and single-cell RNA-sequencing and clinical data of CRC were downloaded from the TCGA and GEO databases. HRGs and LMRGs were obtained from the Molecular Signatures Database. The R software package DESeq2 was used to perform differential expression analysis. Molecular subtyping was performed using unsupervised clustering. A predictive signature was developed using univariate Cox regression, random forest model, LASSO, and multivariate Cox regression analyses. Finally, the sensitivity of tumor cells to chemotherapeutic agents before and after hypoxia was verified using in vitro experiments. Results: We classified 575 patients with CRC into three molecular subtypes and were able to distinguish their prognoses clearly. The C1 subtype, which exhibits high levels of hypoxia, has a low proportion of CD8 + T cells and a high proportion of macrophages. The expression of immune checkpoint genes is generally elevated in C1 patients with severe immune dysfunction. Subsequently, we constructed a predictive model, the HLM score, which effectively predicts the prognosis of patients with CRC and the efficacy of immunotherapy. The HLM score was validated in GSE39582, GSE106584, GSE17536, and IMvigor210 datasets. Patients with high HLM scores exhibit high infiltration of CD8 + exhausted T cells (Tex), especially terminal Tex, and oxidative phosphorylation (OXPHOS)−Tex in the immune microenvironment. Finally, in vitro experiments confirmed that CRC cell lines were less sensitive to 5-fluorouracil, oxaliplatin, and irinotecan under hypoxic conditions. Conclusion: We constructed novel hypoxia- and lactate metabolism-related molecular subtypes and revealed their immunological and genetic characteristics. We also developed an HLM scoring system that could be used to predict the prognosis and efficacy of immunotherapy in patients with CRC. [ABSTRACT FROM AUTHOR]

Introduction: IL4I1, also known as Interleukin-4-induced gene 1, is an enzyme that canmodulate the immune system by acting as a L-amino acid oxidase. Nevertheless, a precise understanding of the correlation of IL4I1 with immunological features and immunotherapy efficacy in bladder cancer (BLCA) remains incomplete. Methods: Weanalyzed RNA sequencing data fromthe Cancer GenomeAtlas (TCGA) to investigate the immune function and prognostic importance of IL4I1 across different cancer types. We further examined the TCGA-BLCA cohort for correlations between IL4I1 and various immunological characteristics of tumor microenvironment (TME), such as cancer immune cycle, immune cell infiltration, immune checkpoint expression and T cell inflamed score. Validation was conducted using two independent cohort, GSE48075 and E-MTAB-4321. Finally, RNAsequencing data from the IMvigor210 cohort and immunohistochemistry assays were employed to validate the predictive value of IL4I1 for the TME and immunotherapy efficacy. Results: In our findings, a positive correlation was observed between IL4I1 expression and immunomodulators expression, immune cell infiltration, the cancer immune cycle, and T cell inflamed score in BLCA, suggesting a significant link to the inflamed TME. In addition, studies have shown that IL4I1 elevated levels of individuals tend to be more performance for basal subtype and exhibit enhanced response rates to diverse treatment modalities, specifically immunotherapy. Clinical data from the IMvigor 210 cohort confirmed a higher rate of response to immunotherapy and better survival benefits in patients with high IL4I1 expression. Discussion: To summarize, our research showed that elevated IL4I1 levels are indicative of an inflamed TME, the basal subtype, and a more favorable response to various treatment methods, especially immune checkpoint blockade therapy in BLCA. [ABSTRACT FROM AUTHOR]

Predicting breast cancer molecular subtypes can help guide individualised clinical treatment of patients who need the rational preoperative treatment. This study aimed to investigate the efficacy of preoperative prediction of breast cancer molecular subtypes by contrast-enhanced mammography (CEM) radiomic features. This retrospective two-centre study included women with breast cancer who underwent CEM preoperatively between August 2016 and May 2022. We included 356 patients with 386 lesions, which were grouped into training (n = 162), internal test (n = 160) and external test sets (n = 64). Radiomics features were extracted from low-energy (LE) images and recombined (RC) images and selected. Three dichotomous tasks were established according to postoperative immunohistochemical results: Luminal vs. non-Luminal, human epidermal growth factor receptor (HER2)-enriched vs. non-HER2-enriched, and triple-negative breast cancer (TNBC) vs. non-TNBC. For each dichotomous task, the LE, RC, and LE+RC radiomics models were built by the support vector machine classifier. The prediction performance of the models was assessed by the area under the receiver operating characteristic curve (AUC). Then, the accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were calculated for the models. DeLong's test was utilised to compare the AUCs. Radiomics models based on CEM are valuable for predicting breast cancer molecular subtypes. The LE+RC model achieved the best performance in the test set. The LE+RC model predicted Luminal, HER2-enriched, and TNBC subtypes with AUCs of 0.93, 0.89, and 0.87 in the internal test set and 0.82, 0.83, and 0.69 in the external test set, respectively. In addition, the LE model performed more satisfactorily than the RC model. CEM radiomics features can effectively predict breast cancer molecular subtypes preoperatively, and the LE+RC model has the best predictive performance. [ABSTRACT FROM AUTHOR]