Clinical powers of Aminoacyl tRNA Synthetase Complex Interacting Multifunctional Protein 1 (AIMP1) for head-neck squamous cell carcinoma

Efficacy and safety of PD-1 inhibitors as second-line treatment for advanced squamous esophageal cancer: a systematic review and network meta-analysis with a focus on PD-L1 expression levels

Background

PD-1 inhibitors have shown promising efficacy in enhancing OS and AEs as second-line therapies for patients with advanced esophageal squamous cell carcinoma (ESCC). However, there remains no clear consensus on which PD-1 inhibitor provides the best balance between efficacy and safety. To address this key issue in the second-line treatment of ESCC, we conducted a network meta-analysis (NMA) with a focus on OS benefits, particularly in patients with different levels of PD-L1 expression.

Methods

A systematic search of relevant literature was conducted in Web of Science, Embase, PubMed, and Cochrane Library, covering publications from the inception of these database to June 2024. The evaluated endpoints included OS, progression-free survival (PFS), objective response rate (ORR), AEs, and Grade ≥ 3 adverse events (Grade ≥ 3 AEs). A systematic review and Bayesian network meta-analysis were performed to assess the efficacy and safety of various immunotherapy regimens in patients with advanced ESCC. To ensure transparency, novelty, and reliability, this study was prospectively registered in the systematic review registry (CRD42024540581).

Results

Five randomized controlled trials (RCTs), encompassing 2,078 patients and six treatment regimens, were included in this study. Among advanced ESCC patients not selected based on PD-L1 expression, Sintilimab demonstrated the greatest OS benefit (HR = 0.70, 95% CI: 0.50-0.98). Camrelizumab showed the most favorable improvement in PFS compared to chemotherapy (HR = 0.64, 95% CI: 0.47-0.87) and also achieved the best ORR benefit (OR = 3.72, 95% CI: 1.98-6.99). In terms of safety, Nivolumab (OR = 0.10, 95% CI: 0.05-0.19) and Tislelizumab (OR = 0.18, 95% CI: 0.10-0.33) exhibited significant safety advantages over chemotherapy concerning AEs. Moreover, Nivolumab (OR = 0.13, 95% CI: 0.08-0.20) was associated with a markedly lower risk of Grade ≥ 3 AEs compared to chemotherapy. Subgroup analysis based on PD-L1 expression revealed that Tislelizumab (HR = 0.53, 95% CI: 0.37-0.76) offered the greatest OS benefit for patients with PD-L1 ≥ 10%, while Camrelizumab (HR = 0.71, 95% CI: 0.57-0.89) was the most likely regimen to provide an OS advantage for patients with PD-L1 < 10%.

Conclusion

Compared to chemotherapy, PD-1 inhibitors may provide improved survival outcomes for patients with advanced ESCC. Among patients not selected based on PD-L1 expression, Sintilimab is most likely to deliver the best survival benefit. For patients with PD-L1 expression ≥ 10%, Tislelizumab is expected to offer the greatest efficacy, while Camrelizumab appears to be the most effective for those with PD-L1 < 10%.

Systematic Review Registration

https://www.crd.york.ac.uk/PROSPERO/, identifier CRD42024540581.

Data-mining-based biomarker evaluation and experimental validation of SHTN1 for bladder cancer

BACKGROUND

Gene therapy in bladder cancer (BLCA) remains an area ripe for exploration. Recent studies have highlighted the crucial role of SHTN1 in the initiation and progression of various cancers and SHTN1 may have interacted with the FGFR gene. However, its specific function in BLCA remains unclear.

MATERIALS AND METHODS

We investigated the association between SHTN1 expression and prognosis, immune infiltration, and the tumor microenvironment (TME) across multiple malignancies using 433 BLCA samples from The Cancer Genome Atlas (TCGA). Differential gene expression analysis, functional annotation via Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed for SHTN1-related genes by using R packages. Immune response and TME scores, along with drug sensitivity profiles of SHTN1, were analyzed using R packages. Immunohistochemistry (IHC) and western blotting were conducted to assess SHTN1 expression in surgical specimens from BLCA patients.CCK8 assay and cells wound healing assay were performed.The bioinformatics was analyzed by R software. Significant differences were evaluated using unpaired t test.

RESULTS

SHTN1 expression levels were significantly elevated in BLCA associated with poor prognosis (p < 0.01). Receiver operating characteristic (ROC) curves and nomograms demonstrated the diagnostic and prognostic efficacy of SHTN1 in BLCA. Notably, SHTN1 expression was higher in high-grade BLCA compared to lower-grade (p = 5.6e-10), a finding corroborated by IHC and western blotting. Pathway enrichment analysis revealed significant involvement of the Neuroactive ligand-receptor interaction and Chemical carcinogenesis - DNA adducts signaling pathways among SHTN1 differentially expressed genes. In terms of immune infiltration, T cells CD8, T cells follicular helper, and dendritic cells were predominant in the SHTN1 low-expression group, whereas macrophages M0 and M2, and mast cells were predominant in the high-expression group. Multivariate Cox regression analysis identified SHTN1 as an independent prognostic factor for overall survival (HR = 2.93; 95 % CI = 1.40-6.13; p = 0.004).CCK8 and wound healing experiments showed that SHTN1 knockdown reduced the cell proliferation and migration. Western blot showed that the EMT pathway was clearly associated with SHTN1.

CONCLUSIONS

Our findings suggest that SHTN1 holds promise as a prognostic and diagnostic biomarker for BLCA. Moreover, it is closely associated with elevated immune infiltration and distinct characteristics of the tumor microenvironment in BLCA.

Identification of molecular targets of Hypericum perforatum in blood for major depressive disorder: a machine-learning pharmacological study

BACKGROUND

Major depressive disorder (MDD) is one of the most common psychiatric disorders worldwide. Hypericum perforatum (HP) is a traditional herb that has been shown to have antidepressant effects, but its mechanism is unclear. This study aims to identify the molecular targets of HP for the treatment of MDD.

METHODS

We performed differential analysis and weighted gene co-expression network analysis (WGCNA) with blood mRNA expression cohort of MDD and healthy control to identify DEGs and significant module genes (gene list 1). Three databases, CTD, DisGeNET, and GeneCards, were used to retrieve MDD-related gene intersections to obtain MDD-predicted targets (gene list 2). The validated targets were retrieved from the TCMSP database (gene list 3). Based on these three gene lists, 13 key pathways were identified. The PPI network was constructed by extracting the intersection of genes and HP-validated targets on all key pathways. Key therapeutic targets were obtained using MCODE and machine learning (LASSO, SVM-RFE). Clinical diagnostic assessments (Nomogram, Correlation, Intergroup expression), and gene set enrichment analysis (GSEA) were performed for the key targets. In addition, immune cell analysis was performed on the blood mRNA expression cohort of MDD to explore the association between the key targets and immune cells. Finally, molecular docking prediction was performed for the targets of HP active ingredients on MDD.

RESULTS

Differential expression analysis and WGCNA module analysis yielded 933 potential targets for MDD. Three disease databases were intersected with 982 MDD-predicted targets. The TCMSP retrieved 275 valid targets for HP. Separate enrichment analysis intersected 13 key pathways. Five key targets (AKT1, MAPK1, MYC, EGF, HSP90AA1) were finally screened based on all enriched genes and HP valid targets. Combined with the signaling pathway and immune cell analysis suggested the effect of peripheral immunity on MDD and the important role of neutrophils in immune inflammation. Finally, the binding of HP active ingredients (quercetin, kaempferol, and luteolin) and all 5 key targets were predicted based on molecular docking.

CONCLUSIONS

The active constituents of Hypericum perforatum can act on MDD and key targets and pathways of this action were identified.

The MCIB Model: A Novel Theory for Describing the Spatial Heterogeneity of the Tumor Microenvironment

The tumor microenvironment (TME) can be regarded as a complex and dynamic microecosystem generated by the interactions of tumor cells, interstitial cells, the extracellular matrix, and their products and plays an important role in the occurrence, progression and metastasis of tumors. In a previous study, we constructed an IEO model (prI-, prE-, and pOst-metastatic niche) according to the chronological sequence of TME development. In this paper, to fill the theoretical gap in spatial heterogeneity in the TME, we defined an MCIB model (Metabolic, Circulatory, Immune, and microBial microenvironment). The MCIB model divides the TME into four subtypes that interact with each other in terms of mechanism, corresponding to the four major links of metabolic reprogramming, vascular remodeling, immune response, and microbial action, providing a new way to assess the TME. The combination of the MCIB model and IEO model comprehensively depicts the spatiotemporal evolution of the TME and can provide a theoretical basis for the combination of clinical targeted therapy, immunotherapy, and other comprehensive treatment modalities for tumors according to the combination and crosstalk of different subtypes in the MCIB model and provide a powerful research paradigm for tumor drug-resistance mechanisms and tumor biological behavior.

Is the voltage-gated sodium channel β3 subunit (SCN3B) a biomarker for glioma?

Recent studies suggest a need for reliable biomarkers enhancing prognosis prediction and treatment strategies in cancer. Here, we performed a data analysis bearing on the expression of SCN3B, voltage-gated sodium channel (VGSC) β3 subunit, as a possible candidate for the development of a glioma biomarker for the first time. This extends our previous review article that mentioned the potential of SCN3B as a prognostic biomarker for glioma survival, further examining its association with existing indicators and immune responses. We utilized clinical and genomic data from multiple glioma cohorts. These include the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). We employed analytical techniques including time-dependent receiver operating characteristic (ROC) analysis, decision curves analysis (DCA), and correlation studies with immune checkpoint markers. Our findings indicate a differential SCN3B expression between glioma grades, and that this significantly correlates with patient survival, particularly in oligodendroglioma subtypes. The DCA curves suggested that the inclusion of SCN3B in the prognostic model would improve decision-making in these subtypes. Moreover, SCN3B expression positively correlated with the presence of key immune cells and negatively correlated with several immune checkpoint inhibitors. This suggests potential roles in modulating immune responses in glioma. Thus, SCN3B emerges as a promising potential prognostic biomarker for glioma, especially for oligodendroglioma. Its dual correlations with prognosis and immune regulation present a compelling case for further experimental and clinical investigations to establish its utility in enhancing glioma management strategies. These findings underscore the importance of integrating novel biomarkers with traditional prognostic models to refine treatment paradigms and improve patient outcomes.

Targeting SEZ6L2 in Colon Cancer: Efficacy of Bexarotene and Implications for Survival

BACKGROUND

Bexarotene, also recognized as Targretin, is categorized as a retinoid, a type of cancer drug. Nevertheless, the precise mechanisms of bexarotene in relation to colon cancer remain unclear. In colon cancer, SEZ6L2 was suggested as one of the biomarkers and targets. This study presents a comprehensive exploration of the role of SEZ6L2 in colon cancer.

METHODS

We utilized both TCGA data and a cohort of Chinese patients. In a meticulous analysis of 478 colon cancer cases, SEZ6L2 expression levels were examined in relation to clinical characteristics, staging parameters, and treatment outcomes. Additionally, we investigated the pharmacological impact of bexarotene on SEZ6L2, demonstrating a significant downregulation of SEZ6L2 at both mRNA and protein levels in colon cancer patients following bexarotene treatment.

RESULTS

SEZ6L2 consistently overexpresses in colon cancer, serving as a potential universal biomarker with prognostic significance, validated in a diverse Chinese cohort. In vitro, SEZ6L2 promotes cell viability without affecting migration. Bexarotene treatment inhibits SEZ6L2 expression, correlating with reduced viability both in vitro and in vivo. SEZ6L2 overexpression accelerates declining survival rates in an in vivo context. Bexarotene's efficacy is context-dependent, effective in parental cells but not with SEZ6L2 overexpression. Computational predictions suggest a direct SEZ6L2-bexarotene interaction, warranting further experimental exploration.

CONCLUSION

The study provides valuable insights into SEZ6L2 as a prognostic biomarker in colon cancer, revealing its intricate relationship with clinical parameters, treatment outcomes, and bexarotene effects. Context-dependent therapeutic responses emphasize the nuanced understanding required for SEZ6L2's role in colon cancer, paving the way for targeted therapeutic strategies.

Identification of the novel exhausted T cell CD8 + markers in breast cancer

Cancer is one of the most concerning public health issues and breast cancer is one of the most common cancers in the world. The immune cells within the tumor microenvironment regulate cancer development. In this study, single immune cell data sets were used to identify marker gene sets for exhausted CD8 + T cells (CD8Tex) in breast cancer. Machine learning methods were used to cluster subtypes and establish the prognostic models with breast cancer bulk data using the gene sets to evaluate the impacts of CD8Tex. We analyzed breast cancer overexpressing and survival-associated marker genes and identified CD8Tex hub genes in the protein-protein-interaction network. The relevance of the hub genes for CD8 + T-cells in breast cancer was evaluated. The clinical associations of the hub genes were analyzed using bulk sequencing data and spatial sequencing data. The pan-cancer expression, survival, and immune association of the hub genes were analyzed. We identified biomarker gene sets for CD8Tex in breast cancer. CD8Tex-based subtyping systems and prognostic models performed well in the separation of patients with different immune relevance and survival. CRTAM, CLEC2D, and KLRB1 were identified as CD8Tex hub genes and were demonstrated to have potential clinical relevance and immune therapy impact. This study provides a unique view of the critical CD8Tex hub genes for cancer immune therapy.

MAPK signaling pathway-based glioma subtypes, machine-learning risk model, and key hub proteins identification

An early diagnosis and precise prognosis are critical for the treatment of glioma. The mitogen‑activated protein kinase (MAPK) signaling pathway potentially affects glioma, but the exploration of the clinical values of the pathway remains lacking. We accessed data from TCGA, GTEx, CGGA, etc. Up-regulated MAPK signaling pathway genes in glioma were identified and used to cluster the glioma subtypes using consensus clustering. The subtype differences in survival, cancer stemness, and the immune microenvironment were analyzed. A prognostic model was trained with the identified genes using the LASSO method and was validated with three external cohorts. The correlations between the risk model and cancer-associated signatures in cancer were analyzed. Key hub genes of the gene set were identified by hub gene analysis and survival analysis. 47% of the MAPK signaling pathway genes were overexpressed in glioma. Subtypes based on these genes were distinguished in survival, cancer stemness, and the immune microenvironment. A risk model was calculated with high confidence in the prediction of overall survival and was correlated with multiple cancer-associated signatures. 12 hub genes were identified and 8 of them were associated with survival. The MAPK signaling pathway was overexpressed in glioma with prognostic value.

Expression and potential immune involvement of cuproptosis in kidney renal clear cell carcinoma

Cuproptosis is a newly identified programmed cell death pathway mediated by intracellular free copper. Cuproptosis genes were studied in this study for a better insight into the role of cuproptosis in cancers. The analysis identified kidney renal clear cell carcinoma (KIRC) as a cancer type most likely to be affected by cuproptosis. This study analyzed the multi-omic data to explore the cancer-noncancer expression pattern and potential immune involvement of the cuproptosis pathway in KIRC. This study clustered the TCGA KIRC samples based on the gene set of 12 cuproptosis genes to study the role of cuproptosis in the KIRC immune microenvironment and found the potential value of cuproptosis signature for immunotherapy prognosis. This study concluded that cuproptosis might affect KIRC and had potential application value in immune therapy. Hopefully, this study can contribute to the application of cuproptosis in the clinical therapy of KIRC.

Screening and Validation of a Carvacrol-Targeting Viability-Regulating Protein, SLC6A3, in Liver Hepatocellular Carcinoma

Background

Liver hepatocellular carcinoma (LIHC) is the second leading cause of tumor-related death in the world. Carvacrol was also found to inhibit multiple cancer types. Here, we proposed that Carvacrol inhibited LIHC.

Methods

We used MTT assay to determine the inhibition of Carvacrol on LIHC cells. BATMAN-TCM was used to predict targets of Carvacrol. These targets were further screened by their survival association and expression in cancer using TCGA data. The bioinformatic screened candidates were further validated in in vitro experiments and clinical samples. Finally, docking models of the interaction of Carvacrol and target protein were conducted.

Results

Carvacrol inhibited the viability of LIHC cell lines. 40 target genes of Carvacrol were predicted, 8 of them associated with survival. 4 genes were found differentially expressed in LIHC vs. normal liver. Among these genes, the expression of SLC6A3 and SCN4A was found affected by Carvacrol in LIHC cells, but only SLC6A3 correlated with the viability inhibition of Carvacrol on LIHC cell lines. A docking model of the interaction of Carvacrol and SLC6A3 was established with a good binding affinity. SLC6A3 knockdown and expression revealed that SLC6A3 promoted the viability of LIHC cells.

Conclusion

Carvacrol inhibited the viability of LIHC cells by downregulating SLC6A3.

A Pan-Cancer Bioinformatic Analysis of RAD51 Regarding the Values for Diagnosis, Prognosis, and Therapeutic Prediction

Background

RAD51, a critical protein for DNA repairment, has been found to associate with multiple cancer types, but, so far, a systematic pan-cancer analysis of RAD51 has not been done yet.

Methods

Data were obtained from multiple open databases and genetic alteration, gene expression, survival association, functional enrichment, stemness, mutation association, immunity association, and drug therapy association of RAD51were analyzed. A prognostic model of RAD51 for overall glioma was constructed as an example application of RAD51 as a biomarker.

Results

RAD51 was overexpressed in 28 types of cancers and was associated with worse overall survival in 11 cancer types. RAD51 correlated genes were enriched in cell cycle terms. RAD51 was associated with cancer stemness, tumor mutational burden, and multiple immunomodulators in different cancer types. RAD51 expression was different across immune subtypes in 11 cancer types. RAD51 was closely associated with cancer immune microenvironments in some cancer types. Proliferating T cells was the cell type that expressed highest RAD51 across most of the cancer samples analyzed. RAD51 expression had an AUC of over 0.5 in 12 of the 23 ICB subcohorts. The Tumor Immune Dysfunction and Exclusion of 9 cancer types were different between RAD51 high and low groups. RAD51 expression showed negative correlations with the sensitivity of most drugs. A prognostic nomogram was constructed with a high confidence.

Conclusion

RAD51 is a clinical valuable biomarker for multiple cancer types, regarding its potential power for diagnosis, prognosis, and therapeutic prediction.