Traditional bulk sequencing methods are limited to measuring the average signal in a group of cells, potentially masking heterogeneity, and rare populations. The single-cell resolution, however, enhances our understanding of complex biological systems and diseases, such as cancer, the immune system, and chronic diseases. However, the single-cell technologies generate massive amounts of data that are often high-dimensional, sparse, and complex, thus making analysis with traditional computational approaches difficult and unfeasible. To tackle these challenges, many are turning to deep learning (DL) methods as potential alternatives to the conventional machine learning (ML) algorithms for single-cell studies. DL is a branch of ML capable of extracting high-level features from raw inputs in multiple stages. Compared to traditional ML, DL models have provided significant improvements across many domains and applications. In this work, we examine DL applications in genomics, transcriptomics, spatial transcriptomics, and multi-omics integration, and address whether DL techniques will prove to be advantageous or if the single-cell omics domain poses unique challenges. Through a systematic literature review, we have found that DL has not yet revolutionized the most pressing challenges of the single-cell omics field. However, using DL models for single-cell omics has shown promising results (in many cases outperforming the previous state-of-the-art models) in data preprocessing and downstream analysis. Although developments of DL algorithms for single-cell omics have generally been gradual, recent advances reveal that DL can offer valuable resources in fast-tracking and advancing research in single-cell.

Although the ever-increasing number of cancer patients pose substantial challenges worldwide, finding a treatment with the highest response rate and the lowest number of side effects is still undergoing research. Compared to chemotherapy, the relatively low side effects of cancer immunotherapy have provided ample opportunity for immunotherapy to become a promising approach for patients with malignancy. However, the clinical translation of immune-based therapies requires robust anti-tumoral immune responses. Immune checkpoints have substantial roles in the induction of an immunosuppressive tumor microenvironment and tolerance against tumor antigens. Identifying and targeting these inhibitory axes, which can be established between tumor cells and tumor-infiltrating lymphocytes, can facilitate the development of anti-tumoral immune responses. Bispecific T-cell engagers, which can attract lymphocytes to the tumor microenvironment, have also paved the road for immunological-based tumor elimination. The development of CAR-T cells and their gene editing have brought ample opportunity to recognize tumor antigens, independent from immune checkpoints and the major histocompatibility complex (MHC). Indeed, there have been remarkable advances in developing various CAR-T cells to target tumoral cells. Knockout of immune checkpoints via gene editing in CAR-T cells might be designated for a breakthrough for patients with malignancy. In the midst of this fast progress in cancer immunotherapies, there is a need to provide up-to-date information regarding immune checkpoints, bispecific T-cell engagers, and CAR-T cells. Therefore, this review aims to provide recent findings of immune checkpoints, bispecific T-cell engagers, and CAR-T cells in cancer immunotherapy and discuss the pertained clinical trials.

Immunotherapy is a new pillar of cancer therapy that provides novel opportunities to treat solid tumors. In this context, the development of new drugs targeting immune checkpoints is considered a promising approach in colorectal cancer (CRC) treatment because it can be induce specific and durable anti-cancer effects. Despite many advances in the immunotherapy of CRC, there are still limitations and obstacles to successful treatment. The immunosuppressive function of the tumor microenvironment (TME) is one of the causes of poor response to treatment in CRC patients. For this reason, checkpoint-blocking antibodies have shown promising outcomes in CRC patients by blocking inhibitory immune checkpoints and enhancing immune responses against tumors. This review summarizes recent advances in immune checkpoint inhibitors (ICIs), such as CTLA-4, PD-1, PD-L1, LAG-3, and TIM-3 in CRC, and it discusses various therapeutic strategies with ICIs, including the double blockade of ICIs, combination therapy of ICIs with other immunotherapies, and conventional treatments. This review also delineates a new hopeful path in the combination of anti-PD-1/anti-PD-L1 with other ICIs such as anti-CTLA-4, anti-LAG-3, and anti-TIM-3 for CRC treatment.

Cytotoxic T lymphocyte antigen-4 (CTLA-4) is an inhibitory immune checkpoint that can be expressed in tumor-infiltrating lymphocytes and colorectal cancer (CRC) cells. This immune checkpoint can attenuate anti-tumoral immune responses and facilitate tumor growth and metastasis. Although capecitabine is an effective chemotherapeutic agent for treating CRC, its effect on the tumoral CTLA-4 expression remains unclear. In the current research, we applied the GSE110224 and GSE25070 datasets to characterize CTLA-4 expression in CRC patients. Then, we analyzed CTLA-4 expression in CRC samples, HT-29, HCT-166, and SW480 cell lines using real-time PCR. Our bioinformatic results have highlighted the overexpression of CTLA-4 in the CRC tissues compared to the adjacent non-tumoral tissues. Our in vitro studies have indicated that SW480 cells can substantially overexpress CTLA-4 compared to HT-29 and HCT 116 cells. In addition, capecitabine can remarkably downregulate the expression of CTLA-4 in SW480 cells. Collectively, capecitabine can inhibit the expression of CTLA-4 in CRC cells and might bridge the immunotherapy approaches with chemotherapy.

Ovarian cancer (OC) is one of the worrisome gynecological cancers worldwide. Given its considerable mortality rate, it is necessary to investigate its oncogenesis. In this study, we used systems biology approaches to describe the key gene modules, hub genes, and regulatory drugs associated with serous OC as the novel biomarkers using weighted gene co-expression network analysis (WGCNA). Our findings have demonstrated that the blue module genes (r = 0.8, p-value = 1e-16) are involved in OC progression. Based on gene enrichment analysis, the genes in this module are frequently involved in biological processes such as the Cyclic adenosine monophosphate (cAMP) signaling pathway and the cellular response to transforming growth factor-beta stimulation. The co-expression network has been built using the correlated module's top hub genes, which are ADORA1, ANO9, CD24P4, CLDN3, CLDN7, ELF3, KLHL14, PRSS8, RASAL1, RIPK4, SERINC2, and WNT7A. Finally, a drug-target network has been built to show the interaction of the FDA-approved drugs with hub genes. Our results have discovered that ADORA1, ANO9, SERINC2, and KLHL14 are hub genes associated with serous OC. These genes can be considered as novel candidate target genes for treating OC.

The coronavirus disease-2019 (COVID-19) pandemic has caused an enormous loss of lives. Various clinical trials of vaccines and drugs are being conducted worldwide; nevertheless, as of today, no effective drug exists for COVID-19. The identification of key genes and pathways in this disease may lead to finding potential drug targets and biomarkers. Here, we applied weighted gene co-expression network analysis and LIME as an explainable artificial intelligence algorithm to comprehensively characterize transcriptional changes in bronchial epithelium cells (primary human lung epithelium (NHBE) and transformed lung alveolar (A549) cells) during severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Our study detected a network that significantly correlated to the pathogenicity of COVID-19 infection based on identified hub genes in each cell line separately. The novel hub gene signature that was detected in our study, including PGLYRP4 and HEPHL1, may shed light on the pathogenesis of COVID-19, holding promise for future prognostic and therapeutic approaches. The enrichment analysis of hub genes showed that the most relevant biological process and KEGG pathways were the type I interferon signaling pathway, IL-17 signaling pathway, cytokine-mediated signaling pathway, and defense response to virus categories, all of which play significant roles in restricting viral infection. Moreover, according to the drug–target network, we identified 17 novel FDA-approved candidate drugs, which could potentially be used to treat COVID-19 patients through the regulation of four hub genes of the co-expression network. In conclusion, the aforementioned hub genes might play potential roles in translational medicine and might become promising therapeutic targets. Further in vitro and in vivo experimental studies are needed to evaluate the role of these hub genes in COVID-19.