Machine Learning Predictor of Immune Checkpoint Blockade Response in Gastric Cancer

Proteomic profiling and biomarker discovery for predicting the response to PD-1 inhibitor immunotherapy in gastric cancer patients

Background: Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment; however, a significant proportion of gastric cancer (GC) patients do not respond to this therapy. Consequently, there is an urgent need to elucidate the mechanisms underlying resistance to ICIs and identify robust biomarkers capable of predicting the response to ICIs at treatment initiation. Methods: In this study, we collected GC tissues from 28 patients prior to the administration of anti-programmed death 1 (PD-1) immunotherapy and conducted protein quantification using high-resolution mass spectrometry (MS). Subsequently, we analyzed differences in protein expression, pathways, and the tumor microenvironment (TME) between responders and non-responders. Furthermore, we explored the potential of these differences as predictive indicators. Finally, using machine learning algorithms, we screened for biomarkers and constructed a predictive model. Results: Our proteomics-based analysis revealed that low activity in the complement and coagulation cascades pathway (CCCP) and a high abundance of activated CD8 T cells are positive signals corresponding to ICIs. By using machine learning, we successfully identified a set of 10 protein biomarkers, and the constructed model demonstrated excellent performance in predicting the response in an independent validation set (N = 14; area under the curve [AUC] = 0.959). Conclusion: In summary, our proteomic analyses unveiled unique potential biomarkers for predicting the response to PD-1 inhibitor immunotherapy in GC patients, which may provide the impetus for precision immunotherapy.

Exploring the current landscape of single-cell RNA sequencing applications in gastric cancer research

Gastric cancer (GC) represents a major global health burden and is responsible for a significant number of cancer-related fatalities. Its complex nature, characterized by heterogeneity and aggressive behaviour, poses considerable challenges for effective diagnosis and treatment. Single-cell RNA sequencing (scRNA-seq) has emerged as an important technique, offering unprecedented precision and depth in gene expression profiling at the cellular level. By facilitating the identification of distinct cell populations, rare cells and dynamic transcriptional changes within GC, scRNA-seq has yielded valuable insights into tumour progression and potential therapeutic targets. Moreover, this technology has significantly improved our comprehension of the tumour microenvironment (TME) and its intricate interplay with immune cells, thereby opening avenues for targeted therapeutic strategies. Nonetheless, certain obstacles, including tumour heterogeneity and technical limitations, persist in the field. Current endeavours are dedicated to refining protocols and computational tools to surmount these challenges. In this narrative review, we explore the significance of scRNA-seq in GC, emphasizing its advantages, challenges and potential applications in unravelling tumour heterogeneity and identifying promising therapeutic targets. Additionally, we discuss recent developments, ongoing efforts to overcome these challenges, and future prospects. Although further enhancements are required, scRNA-seq has already provided valuable insights into GC and holds promise for advancing biomedical research and clinical practice.

Predicting Immunotherapy Outcomes in Glioblastoma Patients through Machine Learning

Glioblastoma is a highly aggressive cancer associated with a dismal prognosis, with a mere 5% of patients surviving beyond five years post diagnosis. Current therapeutic modalities encompass surgical intervention, radiotherapy, chemotherapy, and immune checkpoint inhibitors (ICBs). However, the efficacy of ICBs remains limited in glioblastoma patients, necessitating a proactive approach to anticipate treatment response and resistance. In this comprehensive study, we conducted a rigorous analysis involving two distinct glioblastoma patient cohorts subjected to PD-1 blockade treatments. Our investigation revealed that a significant portion (60%) of patients exhibit persistent disease progression despite ICB intervention. To elucidate the underpinnings of resistance, we characterized the immune profiles of glioblastoma patients with continued cancer progression following anti-PD1 therapy. These profiles revealed multifaceted defects, encompassing compromised macrophage, monocyte, and T follicular helper responses, impaired antigen presentation, aberrant regulatory T cell (Tregs) responses, and heightened expression of immunosuppressive molecules (TGFB, IL2RA, and CD276). Building upon these resistance profiles, we leveraged cutting-edge machine learning algorithms to develop predictive models and accompanying software. This innovative computational tool achieved remarkable success, accurately forecasting the progression status of 82.82% of the glioblastoma patients in our study following ICBs, based on their unique immune characteristics. In conclusion, our pioneering approach advocates for the personalization of immunotherapy in glioblastoma patients. By harnessing patient-specific attributes and computational predictions, we offer a promising avenue for the enhancement of clinical outcomes in the realm of immunotherapy. This paradigm shift towards tailored therapies underscores the potential to revolutionize the management of glioblastoma, opening new horizons for improved patient care.

Immune checkpoints signature-based risk stratification for prognosis of patients with gastric cancer

Until now, few researches have comprehensive explored the role of immune checkpoints (ICIs) and tumor microenvironment (TME) in gastric cancer (GC) patients based on the genomic data. RNA-sequence data and clinical information were obtained from The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) database, GSE84437 and GSE84433. Univariate Cox analysis identified 60 ICIs with prognostic values, and these genes were then subjected to NMF cluster analysis and the GC samples (n = 804) were classified into two distinct subtypes (Cluster 1: n = 583; Cluster 2: n = 221). The Kaplan-Meier curves for OS analysis indicated that C1 predicted a poorer prognosis. The C2 subtype illustrated a relatively better prognosis and characteristics of "hot tumors," including high immune score, overexpression of immune checkpoint molecules, and enriched tumor-infiltrated immune cells, indicating that the NMF clustering in GC was robust and stable. Regarding the patient's heterogeneity, an ICI-score was constructed to quantify the ICI patterns in individual patients. Moreover, the study found that the low ICI-score group contained mostly MSI-low events, and the high ICI-score group contained predominantly MSI-high events. In addition, the ICI-score groups had good responsiveness to CTLA4 and PD-1 based on The Cancer Immunome Atlas (TCIA) database. Our research firstly constructed ICIs signature, as well as identified some hub genes in GC patients.

Rise of Deep Learning Clinical Applications and Challenges in Omics Data: A Systematic Review

This research aims to review and evaluate the most relevant scientific studies about deep learning (DL) models in the omics field. It also aims to realize the potential of DL techniques in omics data analysis fully by demonstrating this potential and identifying the key challenges that must be addressed. Numerous elements are essential for comprehending numerous studies by surveying the existing literature. For example, the clinical applications and datasets from the literature are essential elements. The published literature highlights the difficulties encountered by other researchers. In addition to looking for other studies, such as guidelines, comparative studies, and review papers, a systematic approach is used to search all relevant publications on omics and DL using different keyword variants. From 2018 to 2022, the search procedure was conducted on four Internet search engines: IEEE Xplore, Web of Science, ScienceDirect, and PubMed. These indexes were chosen because they offer enough coverage and linkages to numerous papers in the biological field. A total of 65 articles were added to the final list. The inclusion and exclusion criteria were specified. Of the 65 publications, 42 are clinical applications of DL in omics data. Furthermore, 16 out of 65 articles comprised the review publications based on single- and multi-omics data from the proposed taxonomy. Finally, only a small number of articles (7/65) were included in papers focusing on comparative analysis and guidelines. The use of DL in studying omics data presented several obstacles related to DL itself, preprocessing procedures, datasets, model validation, and testbed applications. Numerous relevant investigations were performed to address these issues. Unlike other review papers, our study distinctly reflects different observations on omics with DL model areas. We believe that the result of this study can be a useful guideline for practitioners who look for a comprehensive view of the role of DL in omics data analysis.

Identification of immune microenvironment subtypes and signature genes for Alzheimer's disease diagnosis and risk prediction based on explainable machine learning

Background

Using interpretable machine learning, we sought to define the immune microenvironment subtypes and distinctive genes in AD.

Methods

ssGSEA, LASSO regression, and WGCNA algorithms were used to evaluate immune state in AD patients. To predict the fate of AD and identify distinctive genes, six machine learning algorithms were developed. The output of machine learning models was interpreted using the SHAP and LIME algorithms. For external validation, four separate GEO databases were used. We estimated the subgroups of the immunological microenvironment using unsupervised clustering. Further research was done on the variations in immunological microenvironment, enhanced functions and pathways, and therapeutic medicines between these subtypes. Finally, the expression of characteristic genes was verified using the AlzData and pan-cancer databases and RT-PCR analysis.

Results

It was determined that AD is connected to changes in the immunological microenvironment. WGCNA revealed 31 potential immune genes, of which the greenyellow and blue modules were shown to be most associated with infiltrated immune cells. In the testing set, the XGBoost algorithm had the best performance with an AUC of 0.86 and a P-R value of 0.83. Following the screening of the testing set by machine learning algorithms and the verification of independent datasets, five genes (CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12) that were closely associated with AD pathological biomarkers and allowed for the accurate prediction of AD progression were found to be immune microenvironment-related genes. The feature gene-based nomogram may provide clinical advantages to patients. Two immune microenvironment subgroups for AD patients were identified, subtype2 was linked to a metabolic phenotype, subtype1 belonged to the immune-active kind. MK-866 and arachidonyltrifluoromethane were identified as the top treatment agents for subtypes 1 and 2, respectively. These five distinguishing genes were found to be intimately linked to the development of the disease, according to the Alzdata database, pan-cancer research, and RT-PCR analysis.

Conclusion

The hub genes associated with the immune microenvironment that are most strongly associated with the progression of pathology in AD are CXCR4, PPP3R1, HSP90AB1, CXCL10, and S100A12. The hypothesized molecular subgroups might offer novel perceptions for individualized AD treatment.

Genomic and Transcriptomic Predictors of Response to Immune Checkpoint Inhibitors in Melanoma Patients: A Machine Learning Approach

Immune checkpoint inhibitors (ICIs) became one of the most revolutionary cancer treatments, especially in melanoma. While they have been proven to prolong survival with lesser side effects compared to chemotherapy, the accurate prediction of response remains to be an unmet gap. Thus, we aim to identify accurate clinical and transcriptomic biomarkers for ICI response in melanoma. We also provide mechanistic insight into how high-performing markers impose their effect on the tumor microenvironment (TME). Clinical and transcriptomic data were retrieved from melanoma studies administering ICIs from cBioportal and GEO databases. Four machine learning models were developed using random-forest classification (RFC) entailing clinical and genomic features (RFC7), differentially expressed genes (DEGs, RFC-Seq), survival-related DEGs (RFC-Surv) and a combination model. The xCELL algorithm was used to investigate the TME. A total of 212 ICI-treated melanoma patients were identified. All models achieved a high area under the curve (AUC) and bootstrap estimate (RFC7: 0.71, 0.74; RFC-Seq: 0.87, 0.75; RFC-Surv: 0.76, 0.76, respectively). Tumor mutation burden, GSTA3, and VNN2 were the highest contributing features. Tumor infiltration analyses revealed a direct correlation between upregulated genes and CD8+, CD4+ T cells, and B cells and inversely correlated with myeloid-derived suppressor cells. Our findings confirmed the accuracy of several genomic, clinical, and transcriptomic-based RFC models, that could further support the use of TMB in predicting response to ICIs. Novel genes (GSTA3 and VNN2) were identified through RFC-seq and RFC-surv models that could serve as genomic biomarkers after robust validation.

Identification of Glucose Metabolism-Related Genes in the Progression from Nonalcoholic Fatty Liver Disease to Hepatocellular Carcinoma

Nonalcoholic fatty liver disease (NAFLD) is a manifestation of hepatic metabolic syndrome that varies in severity. Hepatocellular carcinoma progresses from NAFLD when there is heterogeneity in the infiltration of immune cells and molecules. A precise molecular classification of NAFLD remains lacking, allowing further exploration of the link between NAFLD and hepatocellular carcinoma. In this work, a weighted gene coexpression network analysis was used to identify two coexpression modules based on multiple omics data used to differentiate NAFLD subtypes. Additionally, key genes in the process of glucose metabolism and NAFLD were used to construct a prognostic model in a cohort of patients with hepatocellular carcinoma. Furthermore, the specific expression of signature genes in hepatocellular carcinoma cells was analyzed using a single-cell RNA sequencing approach. A total of 19 liver tissues of NAFLD patients were obtained from the GEO database, and 81 glucose metabolism-related genes were downloaded from the CTD database. In addition, based on nine signature genes, we constructed a prognostic model to divide the HCC cohort into high and low-risk groups. We also demonstrated a significant correlation between prognostic models and clinical phenotypes. Furthermore, we integrated single-cell RNA-sequencing data and immunology data to assess potential relationships between different molecular subtypes and hepatocellular carcinoma. Finally, our study discovered that the glucose metabolism pathway may play an important role in the process of NAFLD-hepatocellular carcinoma. In addition, three glucose metabolism-related genes (SERPINE1, VCAN, and TFPI2) may be the potential targets for the immunotherapy of patients with NAFLD-hepatocellular carcinoma.