Prediction of miRNAs and diseases association based on sparse autoencoder and MLP.

Introduction: MicroRNAs (miRNAs) are small and non-coding RNA molecules which have multiple important regulatory roles within cells. With the deepening research on miRNAs, more and more researches show that the abnormal expression of miRNAs is closely related to various diseases. The relationship between miRNAs and diseases is crucial for discovering the pathogenesis of diseases and exploring new treatment methods. Methods: Therefore, we propose a new sparse autoencoder and MLP method (SPALP) to predict the association between miRNAs and diseases. In this study, we adopt advanced deep learning technologies, including sparse autoencoder and multi-layer perceptron (MLP), to improve the accuracy of predicting miRNA-disease associations. Firstly, the SPALP model uses a sparse autoencoder to perform feature learning and extract the initial features of miRNAs and diseases separately, obtaining the latent features of miRNAs and diseases. Then, the latent features combine miRNAs functional similarity data with diseases semantic similarity data to construct comprehensive miRNAs-diseases datasets. Subsequently, the MLP model can predict the unknown association among miRNAs and diseases. Result: To verify the performance of our model, we set up several comparative experiments. The experimental results show that, compared with traditional methods and other deep learning prediction methods, our method has significantly improved the accuracy of predicting miRNAs-disease associations, with 94.61% accuracy and 0.9859 AUC value. Finally, we conducted case study of SPALP model. We predicted the top 30 miRNAs that might be related to Lupus Erythematosus, Ecute Myeloid Leukemia, Cardiovascular, Stroke, Diabetes Mellitus five elderly diseases and validated that 27, 29, 29, 30, and 30 of the top 30 are indeed associated. Discussion: The SPALP approach introduced in this study is adept at forecasting the links between miRNAs and diseases, addressing the complexities of analyzing extensive bioinformatics datasets and enriching the comprehension contribution to disease progression of miRNAs.

BC-QNet: A quantum-infused ELM model for breast cancer diagnosis.

The timely and accurate diagnosis of breast cancer is pivotal for effective treatment, but current automated mammography classification methods have their constraints. In this study, we introduce an innovative hybrid model that marries the power of the Extreme Learning Machine (ELM) with FuNet transfer learning, harnessing the potential of the MIAS dataset. This novel approach leverages an Enhanced Quantum-Genetic Binary Grey Wolf Optimizer (Q-GBGWO) within the ELM framework, elevating its performance. Our contributions are twofold: firstly, we employ a feature fusion strategy to optimize feature extraction, significantly enhancing breast cancer classification accuracy. The proposed methodological motivation stems from optimizing feature extraction for improved breast cancer classification accuracy. The Q-GBGWO optimizes ELM parameters, demonstrating its efficacy within the ELM classifier. This innovation marks a considerable advancement beyond traditional methods. Through comparative evaluations against various optimization techniques, the exceptional performance of our Q-GBGWO-ELM model becomes evident. The classification accuracy of the model is exceptionally high, with rates of 96.54 % for Normal, 97.24 % for Benign, and 98.01 % for Malignant classes. Additionally, the model demonstrates a high sensitivity with rates of 96.02 % for Normal, 96.54 % for Benign, and 97.75 % for Malignant classes, and it exhibits impressive specificity with rates of 96.69 % for Normal, 97.38 % for Benign, and 98.16 % for Malignant classes. These metrics are reflected in its ability to classify three different types of breast cancer accurately. Our approach highlights the innovative integration of image data, deep feature extraction, and optimized ELM classification, marking a transformative step in advancing early breast cancer detection and enhancing patient outcomes.

NK cell transfer overcomes resistance to PD-(L)1 therapy in aged mice.

BACKGROUND: Cancer is the leading cause of death among older adults. Although the integration of immunotherapy has revolutionized the therapeutic landscape of cancer, the complex interactions between age and immunotherapy efficacy remain incompletely defined. Here, we aimed to elucidate the relationship between aging and immunotherapy resistance. METHODS: Flow cytometry was performed to evaluate the infiltration of immune cells in the tumor microenvironment (TME). In vivo T cell proliferation, cytotoxicity and migration assays were performed to evaluate the antitumor capacity of tumor antigen-specific CD8+ T cells in mice. Real-time quantitative PCR (qPCR) was used to investigate the expression of IFN-γ-associated gene and natural killer (NK)-associated chemokine. Adoptive NK cell transfer was adopted to evaluate the effects of NK cells from young mice in overcoming the immunotherapy resistance of aged mice. RESULTS: We found that elderly patients with advanced non-small cell lung cancer (aNSCLC) aged ≥ 75 years exhibited poorer progression-free survival (PFS), overall survival (OS) and a lower clinical response rate after immunotherapy. Mechanistically, we showed that the infiltration of NK cells was significantly reduced in aged mice compared to younger mice. Furthermore, the aged NK cells could also suppress the activation of tumor antigen-specific CD8+ T cells by inhibiting the recruitment and activation of CD103+ dendritic cells (DCs). Adoptive transfer of NK cells from young mice to aged mice promoted TME remodeling, and reversed immunotherapy resistance. CONCLUSION: Our findings revealed the decreased sensitivity of elderly patients to immunotherapy, as well as in aged mice. This may be attributed to the reduction of NK cells in aged mice, which inhibits CD103+ DCs recruitment and its CD86 expression and ultimately leads to immunotherapy resistance.

Breast cancer diagnosis using support vector machine optimized by improved quantum inspired grey wolf optimization.

A prompt diagnosis of breast cancer in its earliest phases is necessary for effective treatment. While Computer-Aided Diagnosis systems play a crucial role in automated mammography image processing, interpretation, grading, and early detection of breast cancer, existing approaches face limitations in achieving optimal accuracy. This study addresses these limitations by hybridizing the improved quantum-inspired binary Grey Wolf Optimizer with the Support Vector Machines Radial Basis Function Kernel. This hybrid approach aims to enhance the accuracy of breast cancer classification by determining the optimal Support Vector Machine parameters. The motivation for this hybridization lies in the need for improved classification performance compared to existing optimizers such as Particle Swarm Optimization and Genetic Algorithm. Evaluate the efficacy of the proposed IQI-BGWO-SVM approach on the MIAS dataset, considering various metric parameters, including accuracy, sensitivity, and specificity. Furthermore, the application of IQI-BGWO-SVM for feature selection will be explored, and the results will be compared. Experimental findings demonstrate that the suggested IQI-BGWO-SVM technique outperforms state-of-the-art classification methods on the MIAS dataset, with a resulting mean accuracy, sensitivity, and specificity of 99.25%, 98.96%, and 100%, respectively, using a tenfold cross-validation datasets partition.

Tumor-associated myeloid cells in cancer immunotherapy.

Tumor-associated myeloid cells (TAMCs) are among the most important immune cell populations in the tumor microenvironment, and play a significant role on the efficacy of immune checkpoint blockade. Understanding the origin of TAMCs was found to be the essential to determining their functional heterogeneity and, developing cancer immunotherapy strategies. While myeloid-biased differentiation in the bone marrow has been traditionally considered as the primary source of TAMCs, the abnormal differentiation of splenic hematopoietic stem and progenitor cells, erythroid progenitor cells, and B precursor cells in the spleen, as well as embryo-derived TAMCs, have been depicted as important origins of TAMCs. This review article provides an overview of the literature with a focus on the recent research progress evaluating the heterogeneity of TAMCs origins. Moreover, this review summarizes the major therapeutic strategies targeting TAMCs with heterogeneous sources, shedding light on their implications for cancer antitumor immunotherapies.

Synergistic immunotherapy targeting cancer-associated anemia: prospects of a combination strategy.

Cancer-associated anemia promotes tumor progression, leads to poor quality of life in patients with cancer, and even obstructs the efficacy of immune checkpoint inhibitors therapy. However, the precise mechanism for cancer-associated anemia remains unknown and the feasible strategy to target cancer-associated anemia synergizing immunotherapy needs to be clarified. Here, we review the possible mechanisms of cancer-induced anemia regarding decreased erythropoiesis and increased erythrocyte destruction, and cancer treatment-induced anemia. Moreover, we summarize the current paradigm for cancer-associated anemia treatment. Finally, we propose some prospective paradigms to slow down cancer-associated anemia and synergistic the efficacy of immunotherapy. Video Abstract.

DRSN4mCPred: accurately predicting sites of DNA N4-methylcytosine using deep residual shrinkage network for diagnosis and treatment of gastrointestinal cancer in the precision medicine era.

Introduction: The DNA N4-methylcytosine (4mC) site levels of those suffering from digestive system cancers were higher, and the pathogenesis of digestive system cancers may also be related to the changes in DNA 4mC levels. Identifying DNA 4mC sites is a very important step in studying the analysis of biological function and cancer prediction. Extracting accurate features from DNA sequences is the key to establishing a prediction model of effective DNA 4mC sites. This study sought to develop a new predictive model, DRSN4mCPred, which aimed to improve the performance of the predicting DNA 4mC sites. Methods: The model adopted multi-scale channel attention to extract features and used attention feature fusion (AFF) to fuse features. In order to capture features information more accurately and effectively, this model utilized Deep Residual Shrinkage Network with Channel-Wise thresholds (DRSN-CW) to eliminate noise-related features and achieve a more precise feature representation, thereby, distinguishing the sites in DNA with 4mC and non-4mC. Additionally, the predictive model incorporated an inverted residual block, a Multi-scale Channel Attention Module (MS-CAM), a Bi-directional Long Short Term Memory Network (Bi-LSTM), AFF, and DRSN-CW. Results and Discussion: The results indicated the predictive model DRSN4mCPred had extremely good performance in predicting the DNA 4mC sites across different species. This paper will potentially provide support for the diagnosis and treatment of gastrointestinal cancer based on artificial intelligence in the precise medical era.

CD200+ cytotoxic T lymphocytes in the tumor microenvironment are crucial for efficacious anti-PD-1/PD-L1 therapy.

Anti-PD-1/PD-L1 therapy, either by anti-PD-1 antibody or anti-PD-L1 antibody, has efficacy by reinvigorating tumor-infiltrating CD8+ T cells in a subset of patients with cancer, but it has unequal effects on heterogeneous CD8+ T cell populations. Hence, the subset crucial to efficacious PD-1 blockade therapy remains elusive. Here, we found an increase in tumor-infiltrating CD200+ cytotoxic T lymphocytes (CTLs) upon PD-1/PD-L1 blockade, with higher proportions of CD200+ T cells positively related to a favorable clinical outcome to anti-PD-1/PD-L1 therapy in three independent cohorts of patients with cancer. Using multiple mouse tumor models, we demonstrated that CD200+ CTLs are essential for efficacious anti-PD-L1 therapy. Mechanistically, we observed a unique chromatin landscape in CD200+ CTLs and found that these cells are enriched for tumor antigen-specific CTLs and have antitumor effector functions. Coinoculation of CD200+ CTLs with tumor cells led to robust tumor regression in two transplanted mouse models. Clinically, we found that infiltration of CD200+ CTLs into tumors could predict immunotherapy efficacy in six patient cohorts. Together, our findings reveal that CD200+ CTLs in the tumor microenvironment are crucial for efficacious anti-PD-1/PD-L1 therapy and could serve as a predictor of successful immunotherapy in the clinic.

IGWO-IVNet3: DL-Based Automatic Diagnosis of Lung Nodules Using an Improved Gray Wolf Optimization and InceptionNet-V3.

Artificial intelligence plays an essential role in diagnosing lung cancer. Lung cancer is notoriously difficult to diagnose until it has progressed to a late stage, making it a leading cause of cancer-related mortality. Lung cancer is fatal if not treated early, making this a significant issue. Initial diagnosis of malignant nodules is often made using chest radiography (X-ray) and computed tomography (CT) scans; nevertheless, the possibility of benign nodules leads to wrong choices. In their first phases, benign and malignant nodules seem very similar. Additionally, radiologists have a hard time viewing and categorizing lung abnormalities. Lung cancer screenings performed by radiologists are often performed with the use of computer-aided diagnostic technologies. Computer scientists have presented many methods for identifying lung cancer in recent years. Low-quality images compromise the segmentation process, rendering traditional lung cancer prediction algorithms inaccurate. This article suggests a highly effective strategy for identifying and categorizing lung cancer. Noise in the pictures was reduced using a weighted filter, and the improved Gray Wolf Optimization method was performed before segmentation with watershed modification and dilation operations. We used InceptionNet-V3 to classify lung cancer into three groups, and it performed well compared to prior studies: 98.96% accuracy, 94.74% specificity, as well as 100% sensitivity.

High-throughput single-сell sequencing in cancer research.

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Tumor-induced erythroid precursor-differentiated myeloid cells mediate immunosuppression and curtail anti-PD-1/PD-L1 treatment efficacy.

Despite the unprecedented success of immune checkpoint inhibitors (ICIs) as anti-cancer therapy, it remains a prevailing clinical need to identify additional mechanisms underlying ICI therapeutic efficacy and potential drug resistance. Here, using lineage tracking in cancer patients and tumor-bearing mice, we demonstrate that erythroid progenitor cells lose their developmental potential and switch to the myeloid lineage. Single-cell transcriptome analyses reveal that, notwithstanding quantitative differences in erythroid gene expression, erythroid differentiated myeloid cells (EDMCs) are transcriptionally indistinguishable from their myeloid-originated counterparts. EDMCs possess multifaceted machinery to curtail T cell-mediated anti-tumor responses. Consequently, EDMC content within tumor tissues is negatively associated with T cell inflammation for the majority of solid cancers; moreover, EDMC enrichment, in accordance with anemia manifestation, is predictive of poor prognosis in various cohorts of patients undergoing ICI therapy. Together, our findings reveal a feedforward mechanism by which tumors exploit anemia-triggered erythropoiesis for myeloid transdifferentiation and immunosuppression.

Heterogeneity of the tumor immune microenvironment and its clinical relevance.

During the course of tumorigenesis and subsequent metastasis, malignant cells gradually diversify and become more heterogeneous. Consequently, the tumor mass might be infiltrated by diverse immune-related components, including the cytokine/chemokine environment, cytotoxic activity, or immunosuppressive elements. This immunological heterogeneity is universally presented spatially or varies temporally along with tumor evolution or therapeutic intervention across almost all solid tumors. The heterogeneity of anti-tumor immunity shows a profound association with the progression of disease and responsiveness to treatment, particularly in the realm of immunotherapy. Therefore, an accurate understanding of tumor immunological heterogeneity is essential for the development of effective therapies. Facilitated by multi-regional and -omics sequencing, single cell sequencing, and longitudinal liquid biopsy approaches, recent studies have demonstrated the potential to investigate the complexity of immunological heterogeneity of the tumors and its clinical relevance in immunotherapy. Here, we aimed to review the mechanism underlying the heterogeneity of the immune microenvironment. We also explored how clinical assessments of tumor heterogeneity might facilitate the development of more effective personalized therapies.

Effect of Comorbidity on Outcomes of Patients with Advanced Non-Small Cell Lung Cancer Undergoing Anti-PD1 Immunotherapy.

BACKGROUND Comorbidities are reportedly related to the survival of patients with non-small cell lung cancer (NSCLC). The purpose of this study was to explore the impact of comorbidity, assessed by the Charlson comorbidity index (CCI) and the simplified comorbidity scores (SCS) on clinical outcomes of patients with NSCLC treated with immune checkpoint inhibitors. MATERIAL AND METHODS Sixty-six patients with NSCLC who received programmed cell death protein 1 (PD1) inhibitors in our institution in the past 2 years were enrolled in this retrospective study. Data on comorbidity (CCI and SCS) and clinical outcomes, including progression-free survival (PFS), immunotherapy responses, and immunotherapy-related adverse events, were analyzed. RESULTS The disease control rate was obviously higher among patients in the CCI <1 group than the CCI ≥1 group (P<0.001), but were similar between the SCS <8 group and SCS ≥8 group (P=0.585). The median PFS in the CCI <1 group was 271.0 days (95% CI: 214.3-327.7 days) compared with 232.0 days (95% CI: 66.2-397.8 days) for the CCI ≥1 group (P=0.0084). However, the median PFS showed no difference between the groups with SCS <8 at 271.0 days (95% CI: 138.7-403.3 days) versus SCS ≥8 at 222.0 days (95% CI: 196.2-247.8 days), P=0.2106). The incidence of adverse events was similar among patients with high versus low comorbidity indexes (CCI: 35.8% versus 23.6%, P=0.286, respectively; and SCS: 28.0% versus 29.3%, respectively, P=0.912). CONCLUSIONS The comorbidity burden might be a predictor for survival in patients with NSCLC undergoing PD1 inhibitor immunotherapy.

Influence of Hypertension on the Survival of Non-Small Cell Lung Cancer Patients with Type 2 Diabetes Mellitus.

BACKGROUND Hypertension and diabetes mellitus (DM) are both the risk factors for cancer. This study aimed to explore the prognostic value of fasting blood glucose (FBG) and hypertension in type 2 DM (T2DM) patients with advanced non-small cell lung cancer (NSCLC) who had received chemotherapy treatment. MATERIAL AND METHODS There were 181 advanced NSCLC patients with T2DM between 2010 and 2019 included in this study. Their laboratory and clinical data were retrospectively analyzed. The predictive value of FBG and hypertension was evaluated. The Kaplan-Meier method was used to evaluate progression-free survival (PFS). RESULTS The median PFS was 168.0 days (95% CI: 137.9-198.7 days) in patients with FBG ≥7 mmol/L compared to 154.0 days (95% CI: 126.7-181.3 days) for patients with FBG <7 mmol/L (hazard ratio [HR]=1.054; 95% CI: 0.7669-1.452; P=0.7447). Median PFS was longer in non-hypertensive patients than in hypertensive patients [179.0 days (95% CI: 137.3-220.7 days) versus 128.0 days (95% CI: 96.3-159.7 days); P=0.0189]. The existence of hypertension (HR=1.478; 95% CI: 1.063-2.055; P=0.020) was an independent predictor for shorter PFS in the multivariate analysis. Decreased hemoglobin was the major adverse event (over 95% patients). The incidence of all grades of adverse reactions was similar between hypertensive and non-hypertensive patients (all P>0.05) except diarrhea (P=0.020). CONCLUSIONS Complication of hypertension might confer a poor survival for advanced NSCLC patients with T2DM. Further prospective research is needed to confirm these findings.

A pooled analysis of primary hepatic angiosarcoma.

BACKGROUND: Angiosarcoma is an aggressive and malignant neoplasm. Primary hepatic angiosarcoma is extremely rare and accounts for only approximately 5% of all angiosarcomas. Therefore, many doctors do not know enough about this disease; this lack of knowledge motivated us to perform this study. METHODS: We carried out a systematic review of the literature published worldwide from 1990 to 2019 to study the main characteristics, demographics, treatment and prognosis of primary hepatic angiosarcoma. RESULT: A total of 219 patients were included in this study. Patients were mainly middle-aged and elderly at diagnosis, with an average age at onset of 56.7 years. The vast majority of patients (61.5%) presented with abdominal pain or distension. Of 143 patients with clear records of metastasis, 31.5% (45 patients) had distant metastasis. The median overall survival time was only 6 months, and the 1- and 2-year survival rates were 30.4 and 17.3%, respectively. Sex, age, tumor size and metastasis at diagnosis showed no correlation with survival rate. Hepatic rupture was a significant predictor of survival. Surgery is a major treatment choice, and adjuvant chemotherapy can improve the prognosis of patients. Hepatic artery embolization is mainly used in cases of tumor rupture. However, liver transplantation is not advised. CONCLUSION: We presented an overview of the demographics, tumor characteristics and treatment outcomes of the largest number of primary hepatic angiosarcoma patients investigated to date. We highlight the use of routine physical examinations and surgery combined with adjuvant chemotherapy to improve the outcomes in these cases.

The mechanisms of pathological extramedullary hematopoiesis in diseases.

Extramedullary hematopoiesis (EMH) is the expansion and differentiation of hematopoietic stem and progenitor cells outside of the bone marrow. In postnatal life, as a compensatory mechanism for ineffective hematopoiesis of the bone marrow, pathological EMH is triggered by hematopoietic disorders, insufficient hematopoietic compensation, and other pathological stress conditions, such as infection, advanced tumors, anemia, and metabolic stress. Pathological EMH has been reported in many organs, and the sites of pathological EMH may be related to reactivation of the embryonic hematopoietic structure in these organs. As a double-edged sword (blood and immune cell supplementation as well as clinical complications), pathological EMH has been widely studied in recent years. In particular, pathological EMH induced by late-stage tumors contributes to tumor immunosuppression. Thus, a deeper understanding of the mechanism of pathological EMH may be conducive to the development of therapies against the pathological processes that induce EMH. This article reviews the recent progress of research on the cellular and molecular mechanisms of pathological EMH in specific diseases.

C3a-C3aR signaling promotes breast cancer lung metastasis via modulating carcinoma associated fibroblasts.

BACKGROUND: Mounting evidence suggests that complement components promote tumor progression via modulating immune suppression, angiogenesis, or tumor cell proliferation. However, the role of C3a-C3aR signaling in regulating lung metastasis of breast cancer remains unknown. METHODS: We performed various ex-vivo and in-vivo assays. Genetic and pharmacological C3aR blockade models were applied to investigate the role of C3a-C3aR in metastasis of breast cancer. RESULTS: C3a-C3aR signaling in CAFs facilitates the metastasis of breast cancer. Mechanically, C3a-C3aR signaling augments pro-metastatic cytokine secretion and extracellular matrix components expression of CAFs via the activation of PI3K-AKT signaling. Genetic or pharmacological blockade of C3aR signaling effectively inhibited lung metastasis of breast cancer in mouse models. CONCLUSIONS: C3a-C3aR signaling in CAFs facilitates the metastasis of breast cancer. Targeting C3aR signaling is a potential anti-metastasis strategy for breast cancer therapy.

Antitumor effects of targeting myeloid-derived suppressive cells.

Myeloid-derived suppressor cells (MDSCs) are a heterogeneous population of immature myeloid cells with major regulatory functions, which are expanded in pathological conditions, including cancers, infections and autoimmune diseases. Evidence has identified MDSCs as critical cells driving immune suppression in tumor microenvironments. Treatments targeting MDSCs have shown promising results in preclinical studies and some clinical trials. In this review, we discuss therapeutic approaches targeting MDSCs, which may benefit future study.