Characterization of sialylation-related long noncoding RNAs to develop a novel signature for predicting prognosis, immune landscape, and chemotherapy response in colorectal cancer

The prognostic value of sialylation-related long non-coding RNAs in lung adenocarcinoma

There has been increasing interest in the role of epigenetic modification in cancers recently. Among the various modifications, sialylation has emerged as a dominant subtype implicated in tumor progression, metastasis, immune evasion, and chemoresistance. The prognostic significance of sialylation-related molecules has been demonstrated in colorectal cancer. However, the potential roles and regulatory mechanisms of sialylation in lung adenocarcinoma (LUAD) have not been thoroughly investigated. Through Pearson correlation, univariate Cox hazards proportional regression, and random survival forest model analyses, we identified several prognostic long non-coding RNAs (lncRNAs) associated with aberrant sialylation and tumor progression, including LINC00857, LINC00968, LINC00663, and ITGA9-AS1. Based on the signatures of four lncRNAs, we classified patients into two clusters with different landscapes using a non-negative matrix factorization approach. Collectively, patients in Cluster 1 (C1) exhibited worse prognoses than those in Cluster 2 (C2), as well as heavier tumor mutation burden. Functional enrichment analysis showed the enrichment of several pro-tumor pathways in C1, differing from the upregulated Longevity and programmed cell death pathways in C2. Moreover, we profiled immune infiltration levels of important immune cell lineages in two subgroups using MCPcounter scores and single sample gene set enrichment analysis scores, revealing a relatively immunosuppressive microenvironment in C1. Risk analysis indicated that LINC00857 may serve as a pro-tumor regulator, while the other three lncRNAs may be protective contributors. Consistently, we observed upregulated LINC00857 in C1, whereas increased expressive levels of LINC00968, LINC00663, and ITGA9-AS1 were observed in C2. Finally, drug sensitivity analysis suggested that patients in the two groups may benefit from different therapeutic strategies, contributing to precise treatment in LUAD. By integrating multi-omics data, we identified four core sialylation-related lncRNAs and successfully established a prognostic model to distinguish patients with different characterizations. These findings may provide some insights into the underlying mechanism of sialylation, and offer a new stratification way as well as clinical guidance in LUAD.

Synergistic therapeutic strategies and engineered nanoparticles for anti-vascular endothelial growth factor therapy in cancer

Angiogenesis is one of the defining characteristics of cancer. Vascular endothelial growth factor (VEGF) is crucial for the development of angiogenesis. A growing interest in cancer therapy is being caused by the widespread use of antiangiogenic drugs in treating several types of human cancer. However, this therapeutic approach can worsen resistance, invasion, and overall survival. As we proceed, refining combination strategies and addressing the constraint of targeted treatments are paramount. Therefore, major challenges in using novel combinations of antiangiogenic agents with cytotoxic treatments are currently focused on illustrating the potential of synergistic therapeutic strategies, alongside advancements in nanomedicine and gene therapy, present opportunities for more precise interference with angiogenesis pathways and tumor environments. Nanoparticles have the potential to regulate several crucial activities and improve several drug limitations such as lack of selectivity, non-targeted cytotoxicity, insufficient drug delivery at tumor sites, and multi-drug resistance based on their unique features. The goal of this updated review is to illustrate the enormous potential of novel synergistic therapeutic strategies and the targeted nanoparticles as an alternate strategy for t treating a variety of tumors employing antiangiogenic therapy.

PPARG and the PTEN-PI3K/AKT Signaling Axis May Cofunction in Promoting Chemosensitivity in Hypopharyngeal Squamous Cell Carcinoma

It has been demonstrated that PPARG may interact with the PTEN-PI3K/AKT pathway, contributing to its involvement in the chemotherapy treatment of hypopharyngeal squamous cell carcinoma (HSCC). However, the underlying mechanism remains largely unknown. In this study, gene expression profiles of 17 HSCC patients, comprising 8 chemotherapy-sensitive patients (CSP) and 9 chemotherapy-nonsensitive patients (CNSP), were collected and analyzed to investigate expression patterns, correlations, influencing factors of the PPARG-PTEN-PI3K/AKT pathway, and its role in regulating chemosensitivity. The results revealed significantly increased expression (p < 0.04) of AKT1, AKT2, AKT3, PIK3CA, PPARG, and PTEN in the CSP group compared to the CNSP group. Specifically, AKT2 exhibited significant overexpression in tumor tissue (p = 0.01), while AKT2, AKT3, PPARG, and PTEN displayed significant increases in normal tissue (p ≤ 0.04). Positive correlations (R ∈ [0.43, 0.71], p < 0.014) were observed between PIK3CA, AKT1, AKT2, AKT3, and PTEN, with AKT2, AKT3, and PTEN also showing significant correlations with PPARG (R ∈ [0.35, 0.47], p < 0.04). Age, gender, and disease stage had no influence on PPARG, PIK3CA, and PTEN expression, but they may affect AKT expressions. Pathway analysis revealed that PPARG may interact with the PTEN-PI3K/AKT signaling pathway, playing a crucial role in regulating chemosensitivity in the normal tissue microenvironment. Our results suggest that AKT1 and PIK3CA may be associated with chemosensitivity in HSCC tumor cells, while PPARG and PTEN might exhibit a correlation with a specific segment of the PI3K/AKT pathway, potentially influencing chemosensitivity in the normal tissue microenvironment of HSCC patients.

Development of a prognostic Neutrophil Extracellular Traps related lncRNA signature for soft tissue sarcoma using machine learning

Background

Soft tissue sarcoma (STS) is a highly heterogeneous musculoskeletal tumor with a significant impact on human health due to its high incidence and malignancy. Long non-coding RNA (lncRNA) and Neutrophil Extracellular Traps (NETs) have crucial roles in tumors. Herein, we aimed to develop a novel NETsLnc-related signature using machine learning algorithms for clinical decision-making in STS.

Methods

We applied 96 combined frameworks based on 10 different machine learning algorithms to develop a consensus signature for prognosis and therapy response prediction. Clinical characteristics, univariate and multivariate analysis, and receiver operating characteristic curve (ROC) analysis were used to evaluate the predictive performance of our models. Additionally, we explored the biological behavior, genomic patterns, and immune landscape of distinct NETsLnc groups. For patients with different NETsLnc scores, we provided information on immunotherapy responses, chemotherapy, and potential therapeutic agents to enhance the precision medicine of STS. Finally, the gene expression was validated through real-time quantitative PCR (RT-qPCR).

Results

Using the weighted gene co-expression network analysis (WGCNA) algorithm, we identified NETsLncs. Subsequently, we constructed a prognostic NETsLnc signature with the highest mean c-index by combining machine learning algorithms. The NETsLnc-related features showed excellent and stable performance for survival prediction in STS. Patients in the low NETsLnc group, associated with improved prognosis, exhibited enhanced immune activity, immune infiltration, and tended toward an immunothermal phenotype with a potential immunotherapy response. Conversely, patients with a high NETsLnc score showed more frequent genomic alterations and demonstrated a better response to vincristine treatment. Furthermore, RT-qPCR confirmed abnormal expression of several signature lncRNAs in STS.

Conclusion

In conclusion, the NETsLnc signature shows promise as a powerful approach for predicting the prognosis of STS. which not only deepens our understanding of STS but also opens avenues for more targeted and effective treatment strategies.

NIFK as a potential prognostic biomarker in colorectal cancer correlating with immune infiltrates

BACKGROUND

Immune-related initiation, progress, metastasis and sensitivity to treatment associated with poor prognosis of patients with colorectal cancer (CRC). The role of Nucleolar protein interacting with the FHA domain of MKI67 (NIFK) in CRC remained to be investigated. We explore whether NIFK correlates with tumor immune infiltration and plays an important role in CRC patient prognosis.

METHODS

The data of samples involved in our study was obtained from TCGA and GEO and samples for protein expression detection and clinical information analysis were obtained from our hospital. NIFK expression, association with patient prognosis, correlation with infiltration of immune cells and its correlated genes involved in signaling pathways were analyzed using bioinformatics method along with experimental validation and clinical correlation analysis.

RESULTS

Results indicated that the expression of NIFK in tumor tissues was significantly increased compared with normal samples. colon and rectal cancer patients with high NIFK expression have poor survival compared with those with low NIFK expression. Results of cell experiments indicated that NIFK is positively correlated with cell proliferation and migration in CRC. NIFK negatively correlated with T cell CD8+, Tregs, Neutrophil and macrophage significantly. DARS and NKRF were positively correlated with NIFK and DARS correlated with CD8 + T cell, CD4 + T cell, macrophage and Neutrophil, NKRF correlated with CD8 + T cell, CD4 + T cell and macrophage in colon and rectal cancer. NIFK along with its correlated genes as DARS and NKRF were involved in Wnt, PI3K-Akt, NF-κB signaling and Intestinal immune network for lgA production.

CONCLUSIONS

Our results suggested that NIFK might be a biomarker associated with poor prognosis of CRC patients, and it would be a potential target for CRC therapy.

Integrating Bulk and Single-Cell RNA Sequencing Reveals Heterogeneity, Tumor Microenvironment, and Immunotherapeutic Efficacy Based on Sialylation-Related Genes in Bladder Cancer

Background

As known abnormal sialylation exerts crucial roles in the growth, metastasis, and immune evasion of cancers, but the molecular characteristics and roles in bladder cancer (BLCA) remain unclear. This study intends to establish BLCA risk stratification based on sialylation-related genes and elucidate its role in prognosis, tumor microenvironment, and immunotherapy of BLCA.

Methods

Bulk RNA-seq and scRNA-seq data were downloaded from open-access databases. The scRNA-seq data were processed using the R package "Seurat" to identify the core cell types. The tumor sub-typing of BLCA samples was performed by the R package "ConsensusClusterPlus" in the bulk RNA-seq data. Signature genes were identified by the R package "limma" and univariate regression analysis to calculate risk scores using the R package "GSVA" and establish risk stratification of BLCA patients. Finally, the differences in clinicopathological characteristics, tumor microenvironment, and immunotherapy efficacy between the different groups were investigated.

Results

5 core cell types were identified in the scRNA-seq dataset, with monocytes and macrophages presenting the greatest percentage, sialylation-related gene expression, and sialylation scores. The bulk RNA-seq samples were classified into 3 tumor subtypes based on 19 prognosis-related sialylation genes. The 10 differential expressed genes (DEGs) with the smallest p-values were collected as signature genes, and the risk score was calculated, with the samples divided into high and low-risk score groups. The results showed that patients in the high-risk score group exhibited worse survival outcomes, higher tumor grade, more advanced stage, more frequency of gene mutations, higher expression levels of immune checkpoints, and lower immunotherapy response.

Conclusion

We established a novel risk stratification of BLCA from a glycomics perspective, which demonstrated good accuracy in determining the prognostic outcome, clinicopathological characteristics, immune microenvironment, and immunotherapy efficacy of patients, and we are proposing to apply it to direct the choice of clinical treatment options for patients.

Gut Microbial Sialidases and Their Role in the Metabolism of Human Milk Sialylated Glycans

Sialic acids (SAs) are α-keto-acid sugars with a nine-carbon backbone present at the non-reducing end of human milk oligosaccharides and the glycan moiety of glycoconjugates. SAs displayed on cell surfaces participate in the regulation of many physiologically important cellular and molecular processes, including signaling and adhesion. Additionally, sialyl-oligosaccharides from human milk act as prebiotics in the colon by promoting the settling and proliferation of specific bacteria with SA metabolism capabilities. Sialidases are glycosyl hydrolases that release α-2,3-, α-2,6- and α-2,8-glycosidic linkages of terminal SA residues from oligosaccharides, glycoproteins and glycolipids. The research on sialidases has been traditionally focused on pathogenic microorganisms, where these enzymes are considered virulence factors. There is now a growing interest in sialidases from commensal and probiotic bacteria and their potential transglycosylation activity for the production of functional mimics of human milk oligosaccharides to complement infant formulas. This review provides an overview of exo-alpha-sialidases of bacteria present in the human gastrointestinal tract and some insights into their biological role and biotechnological applications.

Prognosis and Nomogram Prediction for Patients with Oral Squamous Cell Carcinoma: A Cohort Study

The TNM staging system is often used to predict the prognosis of patients with oral squamous cell carcinoma (OSCC). However, we have found that patients under the same TNM staging may exhibit tremendous differences in survival rates. Therefore, we aimed to investigate the prognosis of postoperative OSCC patients, establish a nomogram survival prediction model, and verify its effectiveness. Operative logs were reviewed for patients who underwent surgical treatment for OSCC at the Peking University School and Hospital of Stomatology. Patient demographic and surgical records were obtained, and they were followed up for overall survival (OS). A total of 432 patients with oral squamous cell carcinoma were included in the study, with a median follow-up time of 47 months. Based on the results of the Cox regression analysis, we constructed and verified the nomogram prediction model, which includes gender, BMI, OPMDs, pain score, SCC grade, and N stage. The C-index value of the 3-year and 5-year prediction models was 0.782 and 0.770, respectively, proving that the model has a certain level of prediction stability. The new nomogram prediction model has potential clinical significance for predicting the postoperative survival of OSCC patients.