Deciphering the tumor immune microenvironment from a multidimensional omics perspective: insight into next-generation CAR-T cell immunotherapy and beyond.

Tumor immune microenvironment (TIME) consists of intra-tumor immunological components and plays a significant role in tumor initiation, progression, metastasis, and response to therapy. Chimeric antigen receptor (CAR)-T cell immunotherapy has revolutionized the cancer treatment paradigm. Although CAR-T cell immunotherapy has emerged as a successful treatment for hematologic malignancies, it remains a conundrum for solid tumors. The heterogeneity of TIME is responsible for poor outcomes in CAR-T cell immunotherapy against solid tumors. The advancement of highly sophisticated technology enhances our exploration in TIME from a multi-omics perspective. In the era of machine learning, multi-omics studies could reveal the characteristics of TIME and its immune resistance mechanism. Therefore, the clinical efficacy of CAR-T cell immunotherapy in solid tumors could be further improved with strategies that target unfavorable conditions in TIME. Herein, this review seeks to investigate the factors influencing TIME formation and propose strategies for improving the effectiveness of CAR-T cell immunotherapy through a multi-omics perspective, with the ultimate goal of developing personalized therapeutic approaches.

Non-targeted metabolomics revealed novel links between serum metabolites and primary ovarian insufficiency: a Mendelian randomization study.

Background: Primary ovarian insufficiency (POI) is a common clinical endocrine disorder with a high heterogeneity in both endocrine hormones and etiological phenotypes. However, the etiology of POI remains unclear. Herein, we unraveled the causality of genetically determined metabolites (GDMs) on POI through Mendelian randomization (MR) study with the overarching goal of disclosing underlying mechanisms. Methods: Genetic links with 486 metabolites were retrieved from GWAS data of 7824 European participants as exposures, while GWAS data concerning POI were utilized as the outcome. Via MR analysis, we selected inverse-variance weighted (IVW) method for primary analysis and several additional MR methods (MR-Egger, weighted median, and MR-PRESSO) for sensitivity analyses. MR-Egger intercept and Cochran's Q statistical analysis were conducted to assess potential heterogeneity and pleiotropy. In addition, genetic variations in the key target metabolite were scrutinized further. We conducted replication, meta-analysis, and linkage disequilibrium score regression (LDSC) to reinforce our findings. The MR Steiger test and reverse MR analysis were utilized to assess the robustness of genetic directionality. Furthermore, to deeply explore causality, we performed colocalization analysis and metabolic pathway analysis. Results: Via IVW methods, our study identified 33 metabolites that might exert a causal effect on POI development. X-11437 showed a robustly significant relationship with POI in four MR analysis methods (P IVW=0.0119; P weighted-median =0.0145; PMR-Egger =0.0499; PMR-PRESSO =0.0248). Among the identified metabolites, N-acetylalanine emerged as the most significant in the primary MR analysis using IVW method, reinforcing its pivotal status as a serum biomarker indicative of an elevated POI risk with the most notable P-value (P IVW=0.0007; PMR-PRESSO =0.0022). Multiple analyses were implemented to further demonstrate the reliability and stability of our deduction of causality. Reverse MR analysis did not provide evidence for the causal effects of POI on 33 metabolites. Colocalization analysis revealed that some causal associations between metabolites and POI might be driven by shared genetic variants. Conclusion: By incorporating genomics with metabolomics, this study sought to offer a comprehensive analysis in causal impact of serum metabolome phenotypes on risks of POI with implications for underlying mechanisms, disease screening and prevention.

Predicting bladder cancer survival with high accuracy: insights from MAPK pathway-related genes.

The mitogen-activated protein kinase (MAPK) pathway plays a critical role in tumor development and immunotherapy. Nevertheless, additional research is necessary to comprehend the relationship between the MAPK pathway and the prognosis of bladder cancer (BLCA), as well as its influence on the tumor immune microenvironment. To create prognostic models, we screened ten genes associated with the MAPK pathway using COX and least absolute shrinkage and selection operator (LASSO) regression analysis. These models were validated in the Genomic Data Commons (GEO) cohort and further examined for immune infiltration, somatic mutation, and drug sensitivity characteristics. Finally, the findings were validated using The Human Protein Atlas (HPA) database and through Quantitative Real-time PCR (qRT-PCR). Patients were classified into high-risk and low-risk groups based on the prognosis-related genes of the MAPK pathway. The high-risk group had poorer overall survival than the low-risk group and showed increased immune infiltration compared to the low-risk group. Additionally, the nomograms built using the risk scores and clinical factors exhibited high accuracy in predicting the survival of BLCA patients. The prognostic profiling of MAPK pathway-associated genes represents a potent clinical prediction tool, serving as the foundation for precise clinical treatment of BLCA.

The underlying mechanism of chimeric antigen receptor (CAR)-T cell therapy triggering secondary T-cell cancers: Mystery of the Sphinx?

The U.S. Food and Drug Administration (FDA) has reported cases of T-cell malignancies, including CAR-positive lymphomas, in patients receiving B cell maturation antigen (BCMA)- or CD19-targeted autologous CAR-T cell immunotherapy. These reports were derived from clinical trials and/or post-marketing adverse event data. This finding has attracted widespread attention. Therefore, it is essential to explore the potential mechanisms by which chimeric antigen receptor (CAR)-T cell therapy triggers secondary T-cell cancers to further guarantee the safety of CAR-T cell therapy.

Deciphering the prognostic role of endoplasmic reticulum stress in lung adenocarcinoma: integrating prognostic prediction and immunotherapy strategies.

Endoplasmic reticulum stress (ERS) is a critical factor influencing lung adenocarcinoma (LUAD) progression and patient outcomes. In this study, we analyzed gene expression data from LUAD samples sourced from The Cancer Genomic Atlas and Gene Expression Omnibus databases. Utilizing advanced statistical methods including LASSO and Cox regression, we developed a ERS-associated signature (ERAS) based on ten ERS-related genes. This model stratified patients into high- and low-risk groups, with the high-risk group exhibiting decreased survival rates, elevated tumor mutational burden, and heightened chemotherapy sensitivity. Additionally, we observed lower immune and ESTIMATE scores in the high-ERAS group, indicating a potentially compromised immune response. Experimental validation through quantitative real-time polymerase chain reaction confirmed the utility of our model. Furthermore, we constructed a nomogram to predict 1-, 3-, and 5-year survival rates, providing clinicians with a valuable tool for personalized patient management. In conclusion, our study demonstrates the efficacy of the ERAS in identifying high-ERAS LUAD patients, offering promising implications for improved prognostication and treatment strategies.

Emerging role of immunogenic cell death in cancer immunotherapy: Advancing next-generation CAR-T cell immunotherapy by combination.

Immunogenic cell death (ICD) is a stress-driven form of regulated cell death (RCD) in which dying tumor cells' specific signaling pathways are activated to release damage-associated molecular patterns (DAMPs), leading to the robust anti-tumor immune response as well as a reversal of the tumor immune microenvironment from "cold" to "hot". Chimeric antigen receptor (CAR)-T cell therapy, as a landmark in anti-tumor immunotherapy, plays a formidable role in hematologic malignancies but falls short in solid tumors. The Gordian knot of CAR-T cells for solid tumors includes but is not limited to, tumor antigen heterogeneity or absence, physical and immune barriers of tumors. The combination of ICD induction therapy and CAR-T cell immunotherapy is expected to promote the intensive use of CAR-T cell in solid tumors. In this review, we summarize the characteristics of ICD, stress-responsive mechanism, and the synergistic effect of various ICD-based therapies with CAR-T cells to effectively improve anti-tumor capacity.

Omics-based molecular classifications empowering in precision oncology.

BACKGROUND: In the past decades, cancer enigmatical heterogeneity at distinct expression levels could interpret disparities in therapeutic response and prognosis. It built hindrances to precision medicine, a tactic to tailor customized treatment informed by the tumors' molecular profile. Single-omics analysis dissected the biological features associated with carcinogenesis to some extent but still failed to revolutionize cancer treatment as expected. Integrated omics analysis incorporated tumor biological networks from diverse layers and deciphered a holistic overview of cancer behaviors, yielding precise molecular classification to facilitate the evolution and refinement of precision medicine. CONCLUSION: This review outlined the biomarkers at multiple expression layers to tutor molecular classification and pinpoint tumor diagnosis, and explored the paradigm shift in precision therapy: from single- to multi-omics-based subtyping to optimize therapeutic regimens. Ultimately, we firmly believe that by parsing molecular characteristics, omics-based typing will be a powerful assistant for precision oncology.

Proteomics appending a complementary dimension to precision oncotherapy.

Recent advances in high-throughput proteomic profiling technologies have facilitated the precise quantification of numerous proteins across multiple specimens concurrently. Researchers have the opportunity to comprehensively analyze the molecular signatures in plentiful medical specimens or disease pattern cell lines. Along with advances in data analysis and integration, proteomics data could be efficiently consolidated and employed to recognize precise elementary molecular mechanisms and decode individual biomarkers, guiding the precision treatment of tumors. Herein, we review a broad array of proteomics technologies and the progress and methods for the integration of proteomics data and further discuss how to better merge proteomics in precision medicine and clinical settings.

Molecular subtypes of ischemic heart disease based on circadian rhythm.

Coronary atherosclerotic heart disease (CAD) is among the most prevalent chronic diseases globally. Circadian rhythm disruption (CRD) is closely associated with the progression of various diseases. However, the precise role of CRD in the development of CAD remains to be elucidated. The Circadian rhythm disruption score (CRDscore) was employed to quantitatively assess the level of CRD in CAD samples. Our investigation revealed a significant association between high CRDscore and adverse prognosis in CAD patients, along with a substantial correlation with CAD progression. Remarkably distinct CRDscore distributions were also identified among various subtypes. In summary, we have pioneered the revelation of the relationship between CRD and CAD at the single-cell level and established reliable markers for the development, treatment, and prognosis of CAD. A deeper understanding of these mechanisms may offer new possibilities for incorporating "the therapy of coronary heart disease based circadian rhythm" into personalized medical treatment regimens.

An assessment system for clinical and biological interpretability in ulcerative colitis.

Ulcerative colitis (UC) is a serious inflammatory bowel disease (IBD) with high morbidity and mortality worldwide. As the traditional diagnostic techniques have various limitations in the practice and diagnosis of early ulcerative colitis, it is necessary to develop new diagnostic models from molecular biology to supplement the existing methods. In this study, we developed a machine learning-based synthesis to construct an artificial intelligence diagnostic model for ulcerative colitis, and the correctness of the model is verified using an external independent dataset. According to the significantly expressed genes related to the occurrence of UC in the model, an unsupervised quantitative ulcerative colitis related score (UCRScore) based on principal coordinate analysis was established. The UCRScore is not only highly generalizable across UC bulk cohorts at different stages, but also highly generalizable across single-cell datasets, with the same effect in terms of cell numbers, activation pathways and mechanisms. As an important role of screening genes in disease occurrence, based on connectivity map analysis, 5 potential targeting molecular compounds were identified, which can be used as an additional supplement to the therapeutic of UC. Overall, this study provides a potential tool for differential diagnosis and assessment of bio-pathological changes in UC at the macroscopic level, providing an opportunity to optimize the diagnosis and treatment of UC.

A metabolome-wide Mendelian randomization study prioritizes causal circulating metabolites for reproductive disorders including primary ovarian insufficiency, polycystic ovary syndrome, and abnormal spermatozoa.

BACKGROUND: Accumulating studies have highlighted the significant role of circulating metabolomics in the etiology of reproductive system disorders. However, the causal effects between genetically determined metabolites (GDMs) and reproductive diseases, including primary ovarian insufficiency (POI), polycystic ovary syndrome (PCOS), and abnormal spermatozoa (AS), still await thorough clarification. METHODS: With the currently most comprehensive genome-wide association studies (GWAS) data of metabolomics, systematic two-sample Mendelian randomization (MR) analyses were conducted to disclose causal associations between 1,091 blood metabolites and 309 metabolite ratios with reproductive disorders. The inverse-variance weighted (IVW) method served as the primary analysis approach, and multiple effective MR methods were employed as complementary analyses including MR-Egger, weighted median, constrained maximum likelihood (cML-MA), contamination mixture method, robust adjusted profile score (MR-RAPS), and debiased inverse-variance weighted method. Heterogeneity and pleiotropy were assessed via MR-Egger intercept and Cochran's Q statistical analysis. Outliers were detected by Radial MR and MR-PRESSO methods. External replication and metabolic pathway analysis were also conducted. RESULTS: Potential causal associations of 63 GDMs with POI were unearthed, and five metabolites with strong causal links to POI were emphasized. Two metabolic pathways related to the pathogenesis of POI were pinpointed. Suggestive causal effects of 70 GDMs on PCOS were detected, among which 7 metabolites stood out for strong causality with elevated PCOS risk. Four metabolic pathways associated with PCOS mechanisms were recognized. For AS, 64 GDMs as potential predictive biomarkers were identified, particularly highlighting two metabolites for their strong causal connections with AS. Three pathways underneath the AS mechanism were identified. Multiple assessments were conducted to further confirm the reliability and robustness of our causal inferences. CONCLUSION: By extensively assessing the causal implications of circulating GDMs on reproductive system disorders, our study underscores the intricate and pivotal role of metabolomics in reproductive ill-health, laying a theoretical foundation for clinical strategies from metabolic insights.

AI hybrid survival assessment for advanced heart failure patients with renal dysfunction.

Renal dysfunction (RD) often characterizes the worse course of patients with advanced heart failure (AHF). Many prognosis assessments are hindered by researcher biases, redundant predictors, and lack of clinical applicability. In this study, we enroll 1736 AHF/RD patients, including data from Henan Province Clinical Research Center for Cardiovascular Diseases (which encompasses 11 hospital subcenters), and Beth Israel Deaconess Medical Center. We developed an AI hybrid modeling framework, assembling 12 learners with different feature selection paradigms to expand modeling schemes. The optimized strategy is identified from 132 potential schemes to establish an explainable survival assessment system: AIHFLevel. The conditional inference survival tree determines a probability threshold for prognostic stratification. The evaluation confirmed the system's robustness in discrimination, calibration, generalization, and clinical implications. AIHFLevel outperforms existing models, clinical features, and biomarkers. We also launch an open and user-friendly website www.hf-ai-survival.com , empowering healthcare professionals with enhanced tools for continuous risk monitoring and precise risk profiling.

The value of home-uroflowmetry in evaluation of voiding function in children with overactive bladder.

INTRODUCTION: Overactive bladder (OAB) in children is clinically common and seriously affects the physical and mental health of children. The voiding frequency (VF) is an important basis for the diagnosis of OAB. The emergence of home-uroflowmetry (HUF) has allowed the patients to record the VF while recording the uroflowmetry at home, and the voiding at home can show the real voiding situation. However, the use of HUF to assess OAB in children and its clinical significance has not been reported in the literature. Thus, this study investigate the value of HUF in evaluation of voiding function in children with OAB and survey the VF of healthy children in Mainland China. MATERIALS AND METHODS: From May 2021 to July 2023, 52 children with OAB aged 7-10 years, 48 age-matched volunteers (control group) accepted HUF. Daytime VF and nighttime VF, voided volume (VV) per time, 24-h voided volume (24h-VV), maximum flow rate (Qmax), voiding time (VT), and uroflow pattern were recorded and compute corrected maximum urine flow rate (cQmax). VF in 600 health pupils (7-10 years) from five primary schools in Henan Province China were selected for questionnaire survey by cross-sectional survey and multi-stage sampling methods. RESULTS: 52 children with OAB and 48 healthy children completed the available 48-h HUF recordings. 24-hour, daytime, and nighttime VF, and cQmax were higher in the OAB group than in the control group (P < 0.05). However, average VV, Qmax, and VT were lower in the OAB group than in the control group (P < 0.05). There was no significant difference in 24h-VV between two groups (P > 0.05). A total of 502 questionnaires qualified for statistical analysis, and the 24h-VF was 6.3 ± 0.95 times, daytime VF was 5.6 ± 0.89 times, and nighttime VF was 0.7 ± 0.59 times. There was no significant difference in the comparison of 24-h, daytime, and nighttime VF between boys and girls and in the comparison of VF by age (P > 0.05). Compared with the results of the questionnaire, the difference of VF in HUF control group was not statistically significant (P > 0.05). CONCLUSIONS: The VF in children is similar to that of adults and the HUF is a useful tool with the ability to more realistically record changes in voiding function in children with OAB.

Molecular subtypes of colorectal cancer in the era of precision oncotherapy: Current inspirations and future challenges.

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.