KLRK1 as a prognostic biomarker for lung adenocarcinoma cancer

ZIF-8-Encapsulated Pexidartinib Delivery via Targeted Peptide-Modified M1 Macrophages Attenuates MDSC-Mediated Immunosuppression in Osteosarcoma

Adoptive cellular therapy is a promising strategy for cancer treatment. However, the effectiveness of this therapy is limited by its intricate and immunosuppressive tumor microenvironment. In this study, a targeted therapeutic strategy for macrophage loading of drugs is presented to enhance anti-tumor efficacy of macrophages. K7M2-target peptide (KTP) is used to modify macrophages to enhance their affinity for tumors. Pexidartinib-loaded ZIF-8 nanoparticles (P@ZIF-8) are loaded into macrophages to synergistically alleviate the immunosuppressive tumor microenvironment synergistically. Thus, the M1 macrophages decorated with KTP carried P@ZIF-8 and are named P@ZIF/M1-KTP. The tumor volumes in the P@ZIF/M1-KTP group are significantly smaller than those in the other groups, indicating that P@ZIF/M1-KTP exhibited enhanced anti-tumor efficacy. Mechanistically, an increased ratio of CD4+ T cells and a decreased ratio of MDSCs in the tumor tissues after treatment with P@ZIF/M1-KTP indicated that it can alleviate the immunosuppressive tumor microenvironment. RNA-seq further confirms the enhanced immune cell function. Consequently, P@ZIF/M1-KTP has great potential as a novel adoptive cellular therapeutic strategy for tumors.

RNA biomarkers in cancer therapeutics: The promise of personalized oncology

Cancer therapy is a rapidly evolving and constantly expanding field. Current approaches include surgery, conventional chemotherapy and novel biologic agents as in immunotherapy, that together compose a wide armamentarium. The plethora of choices can, however, be clinically challenging in prescribing the most suitable treatment for any given patient. Fortunately, biomarkers can greatly facilitate the most appropriate selection. In recent years, RNA-based biomarkers have proven most promising. These molecules that range from small noncoding RNAs to protein coding gene transcripts can be valuable in cancer management and especially in cancer therapeutics. Compared to their DNA counterparts which are stable throughout treatment, RNA-biomarkers are dynamic. This allows prediction of success prior to treatment start and can identify alterations in expression that could reflect response. Moreover, improved nucleic acid technology allows RNA to be extracted from practically every biofluid/matrix and evaluated with exceedingly high analytic sensitivity. In addition, samples are largely obtained by minimally invasive procedures and as such can be used serially to assess treatment response real-time. This chapter provides the reader insight on currently known RNA biomarkers, the latest research employing Artificial Intelligence in the identification of such molecules and in clinical decisions driving forward the era of personalized oncology.

The role of KLRG1: a novel biomarker and new therapeutic target

Killer cell lectin-like receptor G1 (KLRG1) is an immune checkpoint receptor expressed predominantly in NK and T-cell subsets that downregulates the activation and proliferation of immune cells and participates in cell-mediated immune responses. Accumulating evidence has demonstrated the importance of KLRG1 as a noteworthy disease marker and therapeutic target that can influence disease onset, progression, and prognosis. Blocking KLRG1 has been shown to effectively mitigate the effects of downregulation in various mouse tumor models, including solid tumors and hematologic malignancies. However, KLRG1 inhibitors have not yet been approved for human use, and the understanding of KLRG1 expression and its mechanism of action in various diseases remains incomplete. In this review, we explore alterations in the distribution, structure, and signaling pathways of KLRG1 in immune cells and summarize its expression patterns and roles in the development and progression of autoimmune diseases, infectious diseases, and cancers. Additionally, we discuss the potential applications of KLRG1 as a tool for tumor immunotherapy.

A comprehensive analysis of the KLRB1 expression and its clinical implication in testicular germ cell tumors: A review

Testicular germ cell tumors (TGCT) are the most common testicular malignancies. KLRB1 is considered to influence the development and progression of a number of cancers. However, it is unclear how the KLRB1 gene functions in TGCT. First, it was determined the expression level of KLRB1 in TGCT using The Cancer Genome Atlas (TCGA) (The Cancer Genome Atlas) dataset and GTEx (Genotype-Tissue Expression) dataset. The clinical significance and biological functions of KLRB1 were explored using the TCGA dataset, and we analyzed the correlation of the KLRB1 gene with tumor immunity and infiltrating immune cells using gene set variation analysis and the TIMER database. We found that the expression level of KLRB1 was upregulated in TGCT malignant tissues with the corresponding normal tissues as controls, and KLRB1 expression correlated with clinicopathologic features of TGCT. Functional enrichment analysis suggested that KLRB1 might be involved in immune response and inflammatory response. KLRB1 was highly positively correlated with natural killer cell activation in immune response and positively correlated with tumor-infiltrating immune cells. This study demonstrated for the first time the role of KLRB1 in TGCT, which may serve as a new biomarker associated with immune infiltration and provide a potential therapeutic target for the treatment of TGCT.

The effects of the prognostic biomarker SAAL1 on cancer growth and its association with the immune microenvironment in lung adenocarcinoma

BACKGROUND

Inhibition of Serum Amyloid A-like 1 (SAAL1) expression could inhibit cancer progression and improve the prognosis of cancer patients. At present, the correlation between SAAL1 and lung adenocarcinoma (LAC) remains unclear. Therefore, this study surveyed the worth and pathway of SAAL1 in LAC progression and immunity.

METHODS

Bioinformatics and immunohistochemistry were used to identify the SAAL1 expression in LAC. The roles of SAAL1 expression in the existence values of LAC patients were explored, and the nomograms were constructed. Clinical values of SAAL1 co-expressed genes were evaluated by COX regression, survival, and Receiver operating characteristic (ROC) analysis. EDU and western blotting methods were used to inquiry the functions and pathways of the SAAL1 in cell growths. The correlation between the SAAL1 level and immune microenvironment was visualized using correlation research.

RESULTS

SAAL1 level was elevated in LAC tissues, and was observed in cancer tissues of dead patients. SAAL1 overexpression had something to do with shorter overall survival, progression-free interval, and disease-specific survival in LAC. The area under the curve of SAAL1 was 0.902 in normal tissues and cancer tissues. Inhibition of SAAL1 expression could inhibit cancer cell proliferation, which may be related to the decreased expression of cyclin D1 and Bcl-2 proteins. In LAC, SAAL1 level had something to do with stromal, immune, and estimate scores, and correlated with macrophages, T cells, Th2 cells, CD8 T cells, NK CD56dim cells, DC, eosinophils, NK CD56bright cells, pDC, iDC, cytotoxic cells, Tgd, aDC cells, B cells, Tcm, and TFH levels. SAAL1 overexpression had something to do with existence values and the immunity in LAC.

CONCLUSIONS

Inhibition of SAAL1 expression could regulate cancer growth via cyclin D1 and Bcl-2. SAAL1 is a promising prognostic biomarker in LAC patients.

Integrating the Epigenome and Transcriptome of Hepatocellular Carcinoma to Identify Systematic Enhancer Aberrations and Establish an Aberrant Enhancer-Related Prognostic Signature

Recently, emerging evidence has indicated that aberrant enhancers, especially super-enhancers, play pivotal roles in the transcriptional reprogramming of multiple cancers, including hepatocellular carcinoma (HCC). In this study, we performed integrative analyses of ChIP-seq, RNA-seq, and whole-genome bisulfite sequencing (WGBS) data to identify intergenic differentially expressed enhancers (DEEs) and genic differentially methylated enhancers (DMEs), along with their associated differentially expressed genes (DEE/DME-DEGs), both of which were also identified in independent cohorts and further confirmed by HiC data. Functional enrichment and prognostic model construction were conducted to explore the functions and clinical significance of the identified enhancer aberrations. We identified a total of 2,051 aberrant enhancer-associated DEGs (AE-DEGs), which were highly concurrent in multiple HCC datasets. The enrichment results indicated the significant overrepresentations of crucial biological processes and pathways implicated in cancer among these AE-DEGs. A six AE-DEG-based prognostic signature, whose ability to predict the overall survival of HCC was superior to that of both clinical phenotypes and previously published similar prognostic signatures, was established and validated in TCGA-LIHC and ICGC-LIRI cohorts, respectively. In summary, our integrative analysis depicted a landscape of aberrant enhancers and associated transcriptional dysregulation in HCC and established an aberrant enhancer-derived prognostic signature with excellent predictive accuracy, which might be beneficial for the future development of epigenetic therapy for HCC.