Silencing KIF18B enhances radiosensitivity: identification of a promising therapeutic target in sarcoma

Correlations between the modification patterns mediated by pyroptosis-related genes, tumor microenvironment, and immunotherapy in soft tissue sarcoma

Soft tissue sarcoma (STS) incidence, progression, and metastasis are tightly linked to the tumor microenvironment (TME). The modification patterns mediated by pyroptosis-related genes (PRGs) in STS are unknown regarding the immune cell infiltration landscape of TME, immunotherapy effect, and prognostic value. First, we downloaded STS samples from the Cancer Genome Atlas (TCGA) and gene-expression omnibus (GEO) databases. Based on 52 PRGs, 2 pyroptosis modification patterns were analyzed, and the associations of pyroptosis modification patterns with immune cell infiltration in the TME were elucidated systematically. To quantify PRG modification patterns in STS patients, we generated a pyroptosis scoring system using principal component analysis (PCA). We identified 2 distinct pyroptosis modification patterns in STS. Compared to PRG cluster A, the prognosis of cluster B was better. These 2 pyroptosis modification patterns corresponded to different characteristics of immune cell infiltration in the TME and biological behaviors. In the pyroptosis scoring system, a high pyroptosis score was connected to higher immune cell infiltration, stronger immune surveillance, immune-killing effects on tumor cells, and better clinical benefits. The results from 3 anti-PD1/PD-L1-treated immune cohorts demonstrated that higher pyroptosis scores are also closely connected to better immunotherapy results. We demonstrated that pyroptosis modification is essential to the STS microenvironment. Moreover, the pyroptosis score is a reliable and independent prognostic factor in STS patients, enabling a richer understanding of the STS microenvironment and the screening of immunotherapy candidates, predicting the immunotherapeutic effects for individual STS patients, and guiding the use of chemotherapy drugs.

Differential Expression of KIF18B in Gastric Cancer and Its Role in Chemotherapy Sensitivity

Gastric cancer (GC) is a main cause of cancer death in the world, and improving the chemotherapy sensitivity can enhance the chemotherapy efficacy of GC. The study objective is to explore the differential KIF18B expression in GC and its effect on GC chemotherapy sensitivity. The KIF18B expression in GC tissues and adjacent normal tissues was analyzed by real-time quantitative polymerase chain reaction. The relationship between differential KIF18B expression and different clinicopathological features was detected. It was found that KIF18B was highly expressed in GC tissues, and KIF18B expression was differential in patients with different clinicopathological features. The upregulation of KIF18B has a positive correlation with the poor therapeutic effect and high KIF18 was associated with lower 3-year overall survival and disease-free survival. The KIF18B-downregulated NCI-N87 cells were constructed and tested by cell counting kit-8 assay and colony formation. Cell migration and invasion were detected by Transwell assay. The xenograft tumor model was established to observe the effect of KIF18B on the efficacy of chemotherapy. The upregulation of KIF18B reduced the chemotherapy sensitivity of GC cells and enhanced their proliferation, migration, and invasion. Silencing KIF18B inhibited tumor growth and promoted chemotherapy efficacy in vivo. In summary, KIF18B inhibitor may have a potential function for improving the efficacy of chemotherapy in GC.

Multi-omics analysis reveals the association between elevated KIF18B expression and unfavorable prognosis, immune evasion, and regulatory T cell activation in nasopharyngeal carcinoma

Background

Nasopharyngeal carcinoma (NPC) is prevalent in Southern China. The expression profile and functions of kinesin family member 18B (KIF18B) remain unclear in NPC.

Methods

Bulk and single-cell transcriptome data for NPC were downloaded. KIF18B expression differences in NPC and normal tissues and its prognostic value were validated by immunohistochemistry and Cox model. We performed multi-faceted functional enrichment analysis on KIF18B. Immune infiltration was analyzed comprehensively by the CIBERSORT, EPIC, and quanTIseq algorithms and the BisqueRNA package and confirmed by immunofluorescence assay. The intercellular communication were investigated by the CellChat package. We explored the dynamics of KIF18B expression by pseudotime trajectory. M6A modification analysis rely on SRAMP platform. The treatment response were evaluated by Tumor Immune Dysfunction and Exclusion (TIDE) score, immunophenoscore and IC50 value.

Results

KIF18B overexpression in NPC led to unfavorable prognosis, and significantly associated with advanced T, N, and stage classifications. Functional analysis demonstrated that KIF18B was involved in immune suppression, epithelial-mesenchymal transition (EMT), N6-methyladenosine (m6A) modification and therapeutic responses. The deconvolution algorithm indicated that activated regulatory T cells (Tregs) had the strongest positive correlation with KIF18B among immune cells (R = 0.631). Validated by immunofluorescence assay, the high KIF18B expression group displayed a notable rise in Tregs infiltration, accompanied by a substantial decrease in the infiltration of CD8+ T cells and macrophages. In the intercellular communication network, malignant cells with high KIF18B expression implicated in more interactions, and activated and recruited Tregs by modulating cytokines, chemokines, and immune checkpoints. KIF18B was upregulated in more advanced malignant cells and influenced EMT by regulating ITGA6, VIM, and ZEB1/2. KIF18B expression was positively related to m6A "writer" and "reader" genes, and negatively related to "eraser" genes. The KIF18B high expression group exhibited a higher TIDE score and elevated IC50 values for the commonly used chemotherapy drugs, gemcitabine, oxaliplatin, and 5-fluorouracil.

Conclusion

KIF18B is a significant prognostic marker in NPC, and may modulate immune evasion and EMT. M6A modification may account for the aberrant overexpression of KIF18B in NPC. Furthermore, KIF18B may predict response to immunotherapy and chemotherapy.

Novel pyroptosis-associated genes signature for predicting the prognosis of sarcoma and validation

BACKGROUND

Sarcoma is a rare mesenchymal malignant tumor. Recently, pyroptosis has been reported to be a mode of programmed cell death. Nonetheless, levels of pyroptosis-associated genes in sarcoma and its relevance to prognostic outcomes are yet to be elucidated.

RESULTS

Sarcoma cases were classified into two subtypes with regards to differentially expressed genes. We established a profile composed of seven genes and classified the sarcoma patients into low- and high-risk groups through least absolute shrinkage and selection operator Cox regression. Survival rate of low-risk sarcoma patients was markedly higher, relative to high-risk group (P<0.001). In combination with clinical features, the risk score was established to be an independent predictive factor for OS of sarcoma patients. Chemotherapeutic drug sensitivity response analysis found 65 drugs with higher drug sensitivity in low-risk, than in high-risk group and 14 drugs with higher drug sensitivity in the high-risk patient group, compared with low-risk patient group. In addition, functional enrichment, pathway and gene mutation of the two modules were analyzed. Finally, we used qRT-PCR to detect the expression of seven pyroptosis-related genes in tumor cells, and human skeletal muscle cells, compared with human skeletal muscle cells, PODXL2, LRRC17, GABRA3, SCUBE3 and RFLNB genes show high expression levels in tumor cells, while IGHG2 and hepatic leukemia factor show low expression levels in tumor cells.

CONCLUSIONS

Our research suggest that pyroptosis is closely associated with sarcoma, and these findings confirm that pyroptosis-associated seven genes have a critical role in sarcoma and are potential prognostic factors for sarcoma.

Chromatin Separation Regulators Predict the Prognosis and Immune Microenvironment Estimation in Lung Adenocarcinoma

Background: Abnormal chromosome segregation is identified to be a common hallmark of cancer. However, the specific predictive value of it in lung adenocarcinoma (LUAD) is unclear.

Method: The RNA sequencing and the clinical data of LUAD were acquired from The Cancer Genome Atlas (TACG) database, and the prognosis-related genes were identified. The Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) were carried out for functional enrichment analysis of the prognosis genes. The independent prognosis signature was determined to construct the nomogram Cox model. Unsupervised clustering analysis was performed to identify the distinguishing clusters in LUAD-samples based on the expression of chromosome segregation regulators (CSRs). The differentially expressed genes (DEGs) and the enriched biological processes and pathways between different clusters were identified. The immune environment estimation, including immune cell infiltration, HLA family genes, immune checkpoint genes, and tumor immune dysfunction and exclusion (TIDE), was assessed between the clusters. The potential small-molecular chemotherapeutics for the individual treatments were predicted via the connectivity map (CMap) database.

Results: A total of 2,416 genes were determined as the prognosis-related genes in LUAD. Chromosome segregation is found to be the main bioprocess enriched by the prognostic genes. A total of 48 CSRs were found to be differentially expressed in LUAD samples and were correlated with the poor outcome in LUAD. Nine CSRs were identified as the independent prognostic signatures to construct the nomogram Cox model. The LUAD-samples were divided into two distinct clusters according to the expression of the 48 CSRs. Cell cycle and chromosome segregation regulated genes were enriched in cluster 1, while metabolism regulated genes were enriched in cluster 2. Patients in cluster 2 had a higher score of immune, stroma, and HLA family components, while those in cluster 1 had higher scores of TIDES and immune checkpoint genes. According to the hub genes highly expressed in cluster 1, 74 small-molecular chemotherapeutics were predicted to be effective for the patients at high risk.

Conclusion: Our results indicate that the CSRs were correlated with the poor prognosis and the possible immunotherapy resistance in LUAD.

Defining Diffuse Large B-Cell Lymphoma Immunotypes by CD8+ T Cells and Natural Killer Cells

Background

There is a poor prognosis for diffuse large B-cell lymphoma (DLBCL), one of the most common types of non-Hodgkin lymphoma (NHL). Through gene expression profiles, this study intends to reveal potential subtypes among patients with DLBCL by evaluating their prognostic impact on immune cells.

Methods

Immune subtypes were developed based on CD8+ T cells and natural killer cells calculated from gene expression profiles. The comparison of prognoses and enriched pathways was made between immune subtypes. Following this validation step, samples from the independent data set were analyzed to determine the correlation between immune subtype and prognosis and immune checkpoint blockade (ICB) response. To provide a model to predict the DLBCL immune subtypes, machine learning methods were used. The virtual screening and molecular docking were adopted to identify small molecules to target the immune subtype biomarkers.

Results

A training data set containing 432 DLBCL samples from five data sets and a testing dataset containing 420 DLBCL samples from GSE10846 were used to develop and validate immune subtypes. There were two novel immune subtypes identified in this study: an inflamed subtype (IS) and a noninflamed subtype (NIS). When compared with NIS, IS was associated with higher levels of immune cells and a better prognosis for immunotherapy. Based on the random forest algorithm, a robust machine learning model has been established by 12 hub genes, and the area under the curve (AUC) value is 0.948. Three small molecules were selected to target NIS biomarkers, including VGF, RAD54L, and FKBP8.

Conclusion

This study assessed immune cells as prognostic factors in DLBCL, constructed an immune subtype that could be used to identify patients who would benefit from ICB, and constructed a model to predict the immune subtype.

KIF18B as a regulator in tumor microenvironment accelerates tumor progression and triggers poor outcome in hepatocellular carcinoma

BACKGROUND

The tumor microenvironment plays an important role in the progression and recurrence of tumors and immunotherapy outcomes. The use of immune checkpoint blockers to improve the overall survival rate of patients with advanced hepatocellular carcinoma has yielded inconsistent outcomes. We examined the tumor microenvironment-related genes for their clinical significance and biological functions in hepatocellular carcinoma.

METHODS

Bioinformatic analysis was performed to screen the differentially expressed genes and to identify the core gene of the tumor microenvironment in hepatocellular carcinoma. The expression of KIF18B in hepatocellular carcinoma cell lines and tumor samples was determined using western blotting, quantitative real-time polymerase chain reaction, and immunohistochemistry. The malignancy-promoting ability of KIF18B was evaluated using Cell Counting Kit-8, colony formation, cell proliferation, migration and invasion, and xenograft tumor assays.

RESULTS

KIF18B was identified as one of the core genes in the hepatocellular carcinoma microenvironment and was significantly associated with infiltrating immune cell subtypes and tumor cell stemness. Upregulation of KIF18B was associated with poor clinicopathological characteristics and poor patient outcomes; its downregulation inhibited the proliferation ability of hepatocellular carcinoma cells, which was consistent with the findings of in vivo experiments. Knockdown of KIF18B inhibited epithelial-mesenchymal transition which reduced the migration and invasion abilities of tumor cells. A pulmonary metastasis model confirmed that the downregulation of KIF18B inhibited hepatocellular carcinoma cell metastasis in vivo.

CONCLUSION

KIF18B could be a useful marker for determining the treatment outcomes of immune checkpoint blockers in the context of hepatocellular carcinoma.