BACKGROUND: Evidence suggests that the circadian clock (CIC) is among the important factors for tumorigenesis. We aimed to provide new insights into CIC-mediated molecular subtypes and gene prognostic indexes for prostate cancer (PCa) patients undergoing radical prostatectomy (RP) or radical radiotherapy (RT).
METHODS: PCa data from TCGA was analyzed to identify differentially expressed genes (DEGs) with significant fold changes and p-values. A prognostic index called CIC-related gene prognostic index (CICGPI) was developed through clustering methods and survival analysis and validated on multiple data sets. The diagnostic accuracy of CICGPI for resistance to chemotherapy and radiotherapy was confirmed. Additionally, the interaction between tumor immune environment and CICGPI score was explored, along with their correlation with prognosis.
RESULTS: TOP2A, APOE, and ALDH2 were used to classify the PCa patients into two subtypes. Cluster 2 had a higher risk of biochemical recurrence (BCR) than cluster 1 for PCa patients undergoing RP or RT. A CIC-related gene prognostic index (CICGPI) was constructed using the above three genes for PCa patents in the TCGA database. The CICGPI score showed good prognostic value in the TCGA database and was externally confirmed by PCa patients in GSE116918, MSKCC2010 and GSE46602. In addition, the CICGPI score had a certain and high diagnostic accuracy for tumor chemoresistance (AUC: 0.781) and radioresistance (AUC: 0.988). For gene set variation analysis, we observed that both beta alanine metabolism and limonene and pinene degradation were upregulated in cluster 1 for PCa patients undergoing RP or RT. For PCa patients undergoing RP, cell cycle, homologous recombination, mismatch repair, and DNA replication were upregulated in cluster 2. A strongly positive relationship between cancer-related fibroblasts and CICGPI score was observed in PCa patients undergoing RP or RT. Moreover, a high density of CAFs was highly closely associated with poorer BCR-free survival of PCa patients.
CONCLUSIONS: In this study, we established CIC-related immunological prognostic index and molecular subtypes, which might be useful for the clinical practice.

Emerging evidence suggests that the APOBEC family is implicated in multiple cancers and might be utilized as a new target for cancer detection and treatment. However, the dysregulation and clinical implication of the APOBEC family in clear cell renal cell cancer (ccRCC) remain elusive. TCGA multiomics data facilitated a comprehensive exploration of the APOBEC family across cancers, including ccRCC. Remodeling analysis classified ccRCC patients into two distinct subgroups: APOBEC family pattern cancer subtype 1 (APCS1) and subtype 2 (APCS2). The study investigated differences in clinical parameters, tumor immune microenvironment, therapeutic responsiveness, and genomic mutation landscapes between these subtypes. An APOBEC family-related risk model was developed and validated for predicting ccRCC patient prognosis, demonstrating good sensitivity and specificity. Finally, the overview of APOBEC3B function was investigated in multiple cancers and verified in clinical samples. APCS1 and APCS2 demonstrated considerably distinct clinical features and biological processes in ccRCC. APCS1, an aggressive subtype, has advanced clinical stage and a poor prognosis. APCS1 exhibited an oncogenic and metabolically active phenotype. APCS1 also exhibited a greater tumor mutation load and immunocompromised condition, resulting in immunological dysfunction and immune checkpoint treatment resistance. The genomic copy number variation of APCS1, including arm gain and loss, was much more than that of APCS2, which may help explain the tired immune system. Furthermore, the two subtypes have distinct drug sensitivity patterns in clinical specimens and matching cell lines. Finally, we developed a predictive risk model based on subtype biomarkers that performed well for ccRCC patients and validated the clinical impact of APOBEC3B. Aberrant APOBEC family expression patterns might modify the tumor immune microenvironment by increasing the genome mutation frequency, thus inducing an immune-exhausted phenotype. APOBEC family-based molecular subtypes could strengthen the understanding of ccRCC characterization and guide clinical treatment. Targeting APOBEC3B may be regarded as a new therapeutic target for ccRCC.

Background: There is an association between LUAD and TB, and TB increases the risk of lung adenocarcinogenesis. However, the role of TB in the development of lung adenocarcinoma has not been clarified. Methods: DEGs from TB and LUAD lung samples were obtained to identify TB-LUAD-shared DEGs. Consensus Clustering was performed on the TCGA cohort to characterize unique changes in TB transcriptome-derived lung adenocarcinoma subtypes. Prognostic models were constructed based on TB signatures to explore the characterization of subgroups. Finally, experimental validation and single-cell analysis of potential markers were performed. Results: We characterized three molecular subtypes with unique clinical features, cellular infiltration, and pathway change manifestations. We constructed and validated TB-related Signature in six cohorts. TB-related Signature has characteristic alterations, and can be used as an effective predictor of immunotherapy response. Prognostically relevant novel markers KRT80, C1QTNF6, and TRPA1 were validated by RT-qPCR. The association between KRT80 and lung adenocarcinoma disease progression was verified in Bulk transcriptome and single-cell transcriptome. Conclusion: For the first time, a comprehensive bioinformatics analysis of tuberculosis signatures was used to identify subtypes of lung adenocarcinoma. The TB-related Signature predicted prognosis and identified potential markers. This result reveals a potential pathogenic association of tuberculosis in the progression of lung adenocarcinoma.

Cholangiocarcinoma (CCA) is a heterogeneous and aggressive malignancy with limited therapeutic options and poor prognosis. The identification of reliable prognostic biomarkers and a deeper understanding of the molecular subtypes are critical for the development of targeted therapies and improvement of patient outcomes. This study aims to uncover oxidative stress-related genes (ORGs) in CCA and develop a prognostic risk model using comprehensive transcriptomic analysis from The Cancer Genome Atlas (TCGA). Through LASSO regression analysis, we identified prognosis-related ORGs and constructed a prognostic signature consisting of six ORGs. This signature demonstrated strong predictive performance in survival analysis and ROC curve assessment. Functional enrichment and GSEA analyses revealed significant enrichment of immune-related pathways among different risk groups. GSVA analysis indicated reduced activity in inflammation and oxidative stress pathways in the high-risk subgroup, and xCell results showed lower immune cell infiltration levels in this group. Additionally, immune checkpoint genes and immune-related pathways were downregulated in the high-risk subgroup. Our research has developed a unique prognostic model focusing on oxidative stress, enabling accurate forecasting of patient outcomes and providing crucial insights and recommendations for the prognosis of individuals with CCA. Future studies should aim to validate these findings in clinical settings and further explore therapeutic targets within oxidative stress pathways.

Cervical cancer is a kind of tumor related to chronic HPV infection. Currently, the treatment of cervical cancer is guided mainly by clinicopathological factors. The role of tumor microenvironment in the prognosis and treatment of cervical cancer has been ignored. We aimed to use bioinformatics to identify the molecular subtypes in cervical cancer and construct a predictive nomogram combining a matrix-immune signature (MIS) and clinicopathological factors to support treatment decisions. Two cervical cancer subtypes with different prognoses were identified based on matrix- and immune-genes in TCGA-CESC. The MIS was developed using Cox regression and Lasso algorithm and verified in the Cancer Genome Characterization Initiative (CGCI) using time-dependent receiver operating characteristic (ROC) curve analysis. Multivariable analysis identified lymph node metastases, lymphovascular space invasion, and the MIS as independent prognostic factors, which were used to construct the predictive nomogram. The areas under the ROC curve of the model were 0.872, 0.879, and 0.803 for the 1-, 3-, and 5-year periods, respectively. The C-index was 0.845. Calibration curves confirmed the excellent prognosis prediction of the nomogram. The nomogram indicted a 3-year survival rate of > 90% in patients with a total score > 110.1. The constructed predictive nomogram has significant implications for prognostic assessment and treatment selection in cervical cancer.

BACKGROUND: Previous studies have shown that macrophage-mediated efferocytosis is involved in immunosuppression in acute myeloid leukemia (AML). However, the regulatory role of efferocytosis in AML remains unclear and needs further elucidation.
METHODS: We first identified the key efferocytosis-related genes (ERGs) based on the expression matrix. Efferocytosis-related molecular subtypes were obtained by consensus clustering algorithm. Differences in immune landscape and biological processes among molecular subtypes were further evaluated. The efferocytosis score model was constructed to quantify molecular subtypes and evaluate its value in prognosis prediction and treatment decision-making in AML.
RESULTS: Three distinct efferocytosis-related molecular subtypes were identified and divided into immune activation, immune desert, and immunosuppression subtypes based on the characteristics of the immune landscape. We evaluated the differences in clinical and biological features among different molecular subtypes, and the construction of an efferocytosis score model can effectively quantify the subtypes. A low efferocytosis score is associated with immune activation and reduced mutation frequency, and patients have a better prognosis. A high efferocytosis score reflects immune exhaustion, increased activity of tumor marker pathways, and poor prognosis. The prognostic predictive value of the efferocytosis score model was confirmed in six AML cohorts. Patients exhibiting high efferocytosis scores may derive therapeutic benefits from anti-PD-1 immunotherapy, whereas those with low efferocytosis scores tend to exhibit greater sensitivity towards chemotherapy. Analysis of treatment data in ex vivo AML cells revealed a group of drugs with significant differences in sensitivity between different efferocytosis score groups. Finally, we validated model gene expression in a clinical cohort.
CONCLUSIONS: This study reveals that efferocytosis plays a non-negligible role in shaping the diversity and complexity of the AML immune microenvironment. Assessing the individual efferocytosis-related molecular subtype in individuals will help to enhance our understanding of the characterization of the AML immune landscape and guide the establishment of more effective clinical treatment strategies.

OBJECTIVE: To investigate the correlation between programmed death ligand-1 (PD-L1) expression and tumor-infiltrating lymphocytes (TILs) and evaluate the prognostic value of PD-L1 and TILs in Chinese triple-negative breast cancer (TNBC) patients with different molecular subtype METHODS: This retrospective study was conducted at 2020. Specifically, the pre-chemotherapy clinical data and non-stained tissue blocks of 465 TNBC patients visited the Fudan University Shanghai Cancer Center (FUSCC) between 2008 and 2014 were collected, with their blocks sliced and stained using PD-L1(SP142), and the outcome of subsequent chemotherapy obtained in 2020. The relapse-free survival (RFS) of the study population was calculated. The baseline PD-L1 expression status correlations with TILs and molecular subtypes were assessed using Spearman\'s rank correlation analysis and the Kruskal-Wallis test. Kaplan-Meier survival analyses were undertaken to evaluate the prognosis value of TILs and PD-L1 expression.
RESULTS: PD-L1 expression status on IC was moderately and positively correlated with stromal tumor-infiltrating lymphocytes (sTILs) (rs = 0.502, P <0.001) and iTILs (rs = 0.410, P < 0.001), respectively. PD-L1 expression status and TILs showed significant differences among molecular subtypes (P < 0.001), with the highest proportion of PD-L1+ and high TILs patients observed in the immunomodulatory (IM) subtype. TILs were significantly associated with RFS. Moreover, sTILs could act as an independent predictor of RFS (RR 0.953, 95 % CI 0.920 ∼ 0.987, P = 0.007), while PD-L1 expression status did not show the same prognostic significance.
CONCLUSIONS: The incorporation of pre-treatment TILs and PD-L1 expression status as valuable tools for optimizing patient selection for immunotherapy and managing the risks associated with chemotherapy in Chinese TNBC patients.
METHODS: The data sets generated and analyzed during the current study are available from the corresponding author.

Molecular subtypes play a pivotal role in guiding preclinical and clinical risk assessment and treatment strategies in cancer. In this study, we extracted whole-tissue transcriptomic data from 1,987 ovarian cancer patients spanning 26 independent GEO cohorts. A total of four consensus subtypes (C1-C4) were identified, notably, subtype C1 samples exhibited a poor prognosis and higher M2 macrophages infiltration, whereas subtype C2 samples demonstrated the best prognosis and higher CD4 resting T cells infiltration. Additionally, we characterized cancer- and stromal-specific gene expression profiles, and conducted an analysis of ligand-receptor interactions within these compartments. Based on cancer compartment, subtype-specific interactions as well as gene signatures for each molecular subtype were identified. Leveraging single-cell transcriptomic data, we delineated malignant epithelial cells with four molecular subtypes and observed an increase in C1 cell proportions from primary to relapse to metastasis stages, with a corresponding decrease in C2 cell proportions. Furthermore, we investigated subtype-specific interaction with T cells through integrated analysis of bulk and single-cell datasets. Finally, we developed a robust 10-gene risk model based on subtype gene signatures for prognostic evaluation in ovarian cancer, demonstrating its efficacy across independent datasets. In summary, this study systematically explored ovarian cancer molecular subtypes and provided a framework for other cancer types.

UNASSIGNED: Bladder cancer (BLCA) was recognized as a significant public health challenge due to its high incidence and mortality rates. The influence of molecular subtypes on treatment outcomes was well-acknowledged, necessitating further exploration of their characterization and application. This study was aimed at enhancing the understanding of BLCA by mapping its molecular heterogeneity and developing a robust prognostic model using single-cell and bulk RNA sequencing data. Additionally, immunological characteristics and personalized treatment strategies were investigated through the risk score.
UNASSIGNED: Single-cell RNA sequencing (scRNA-seq) data from GSE135337 and bulk RNA-seq data from several sources, including GSE13507, GSE31684, GSE32894, GSE69795, and TCGA-BLCA, were utilized. Molecular subtypes, particularly the basal-squamous (Ba/Sq) subtype associated with poor prognosis, were identified. A prognostic model was constructed using LASSO and Cox regression analyses focused on genes linked with the Ba/Sq subtype. this model was validated across internal and external datasets to ensure predictive accuracy. High- and low-risk groups based on the risk score derived from TCGA-BLCA data were analyzed to examine their immune-related molecular profiles and treatment responses.
UNASSIGNED: Six molecular subtypes were identified, with the Ba/Sq subtype being consistently associated with poor prognosis. The prognostic model, based on basal-squamous subtype-related genes (BSSRGs), was shown to have strong predictive performance across diverse clinical settings with AUC values at 1, 3, and 5 years indicating robust predictability in training, testing, and entire datasets. Analysis of the different risk groups revealed distinct immune infiltration and microenvironments. Generally higher tumor mutation burden (TMB) scores and lower tumor immune dysfunction and exclusion (TIDE) scores were exhibited by the low-risk group, suggesting varied potentials for systemic drug response between the groups. Finally, significant differences in potential systemic drug response rates were also observed between risk groups.
UNASSIGNED: The study introduced and validated a new prognostic model for BLCA based on BSSRGs, which was proven effective in prognosis prediction. The potential for personalized therapy, optimized by patient stratification and immune profiling, was highlighted by our risk score, aiming to improve treatment efficacy. This approach was promised to offer significant advancements in managing BLCA, tailoring treatments based on detailed molecular and immunological insights.

There is a significant difference in prognosis and response to chemotherapy between basal and classical subtypes of pancreatic ductal adenocarcinoma (PDAC). Further biomarkers are required to identify subtypes of PDAC. We selected candidate biomarkers via review articles. Correlations between these candidate markers and the PDAC molecular subtype gene sets were analyzed using bioinformatics, confirming the biomarkers for identifying classical and basal subtypes. Subsequently, 298 PDAC patients were included, and their tumor tissues were immunohistochemically stratified using these biomarkers. Survival data underwent analysis, including Cox proportional hazards modeling. Our results indicate that the pairwise and triple combinations of KRT5/KRT17/S100A2 exhibit a higher correlation coefficient with the basal-like subtype gene set, whereas the corresponding combinations of GATA6/HNF4A/TFF1 show a higher correlation with the classical subtype gene set. Whether analyzing unmatched or propensity-matched data, the overall survival time was significantly shorter for the basal subtype compared with the classical subtype (p < .001), with basal subtype patients also facing a higher risk of mortality (HR = 4.017, 95% CI 2.675-6.032, p < .001). In conclusion, the combined expression of KRT5, KRT17, and S100A2, in both pairwise and triple combinations, independently predicts shorter overall survival in PDAC patients and likely identifies the basal subtype. Similarly, the combined expression of GATA6, HNF4A, and TFF1, in the same manner, may indicate the classical subtype. In our study, the combined application of established biomarkers offers valuable insights for the prognostic evaluation of PDAC patients.