Janus Kinase-Signal Transducer and Activator of Transcription Inhibitors for the Treatment and Management of Cancer

Alteration of the gut microbiota in patients with lung cancer accompanied by chronic obstructive pulmonary diseases

Aim

To explore the abundance and diversity of the gut microbiota in patients with lung cancer accompanied by chronic obstructive pulmonary disease (LC-COPD).

Methods

The study cohort comprised 15 patients with LC-COPD, 49 patients with lung cancer, and 18 healthy control individuals. ELISA was used to detect inflammatory factors in venous blood. 16S rDNA sequencing was performed to determine the abundance and diversity of the gut microbiota. Gas chromatography-mass spectrometry was used to determine the concentration of short-chain fatty acids (SCFAs) in feces samples.

Results

The α-diversity index indicated that the richness and diversity of the gut microbiota were lower in patients with LC-COPD compared with patients with lung cancer and controls. Principal component analysis revealed significant differences among the three groups (P < 0.05). The linear discriminant analysis effect size algorithm indicated that the o_Lactobacillales, g_Lactobaccillus, f_Lactobaccillaceae, s_Lactobaccillus_oris, c_Bacilli, g_Anaerofustis, s_uncultured organism, and s_bacterium_P1C10 species were prevalent in patients with LC-COPD, while the g_Clostridium_XIVa and g_Butyricicoccus species were prevalent in patients with lung cancer. Furthermore, the concentrations of the SCFAs butyric acid, isobutyric acid, isovaleric acid, and valeric acid tended to be lower in patients with LC-COPD compared with patients with lung cancer and healthy controls, although these intergroup differences were not significant (P > 0.05). Patients with lung cancer had the lowest serum concentration of tumor necrosis factor (TNF)-a. There were no intergroup differences in the concentrations of other inflammatory factors.

Conclusions

The present study indicated that the abundance and structure of the gut microbiota is altered, and the concentrations of SCFAs may be decreased in patients with LC-COPD. In addition, patients with lung cancer had the lowest serum concentration of TNF-a.

A clinical prognostic model of oxidative stress-related genes linked to tumor immune cell infiltration and the prognosis of ovarian cancer patients

Background

According to statistics, ovarian cancer (OV) is the most prevalent type of gynecologic malignancy and has the highest mortality rate of all gynecologic tumors. Although several studies have shown that oxidative stress (OS) contributes significantly to the onset and progression of cancer, the role of OS in OV needs to be investigated further. Thus, it is critical to comprehend the function of OS-related genes in OV.

Methods

In this study, all data related to the transcriptome and clinical status of the patients were retrieved from "The Cancer Genome Atlas" (TCGA) and "Gene Expression Omnibus" (GEO) databases. Using the unsupervised cluster analysis technique, all patients with OV were classified into two different subtypes (categories) based on the OS gene. All hub genes were screened using the weighted gene co-expression network analysis (WGCNA). Since the hub genes and the differentially expressed genes (DEGs) in both categories were found to intersect, the univariate Cox regression analysis was implemented. A multivariate Cox analysis was also performed to construct a novel clinical prognosis model, which was validated using data from the GEO cohort. In addition, the relationship between risk score and immune cell infiltration level was evaluated using CIBERSORT. Finally, qRT-PCR was used to confirm the expression of the genes used to construct the model.

Results

Two subtypes of OS were obtained. The findings indicated that OS-C1 had a better survival outcome than OS-C2. The results of WGCNA yielded 112 hub genes. For univariate COX regression analyses, 49 OS-related trait genes were obtained. Finally, a clinical prognostic model containing two genes was constructed. This model could differentiate between patients with OV having varying years of survival in the TCGA and GEO cohorts. The model risk score was verified as an independent prognostic indicator. According to the results of CIBERSORT, many tumor-infiltrating immune cells were found to be significantly related to the risk score. Furthermore, the results revealed that patients with low-risk OV in the CTLA4 treatment group had a high likelihood of benefiting from immunotherapy. qRT-PCR results also showed that the expression of MARVELD1 and VSIG4 was high in the OV samples.

Conclusions

Analysis of the results suggested that the newly developed model, which contained two characteristic OS-related genes, could successfully predict the survival outcomes of all patients with OV. The findings of this study could offer valuable information and insights into the refinement of personalized therapy and immunotherapy for OV in the future.

Identification and immunological role of cuproptosis in osteoporosis

Background

osteoporosis is a skeletal disorder disease features low bone mass and poor bone architecture, which predisposes to increased risk of fracture. Copper death is a newly recognized form of cell death caused by excess copper ions, which presumably involve in various disease. Accordingly, we intended to investigate the molecular clusters related to the cuproptosis in osteoporosis and to construct a predictive model.

Methods

we investigated the expression patterns of cuproptosis regulators and immune signatures in osteoporosis based on the GSE56815 dataset. Through analysis of 40 osteoporosis samples, we investigated molecular clustering on the basis of cuproptosis--related genes, together with the associated immune cell infiltration. The WGCNA algorithm was applied to detect cluster-specific differentially expressed genes. Afterwards, the optimum machine model was selected by calculating the performance of the support vector machine model, random forest model, eXtreme Gradient Boosting and generalized linear model. Nomogram, decision curve analysis, calibration curves, and the GSE7158 dataset was utilizing to confirm the prediction efficiency.

Results

Differences between osteoporotic and non-osteoporotic controls confirm poorly adjusted copper death-related genes and triggered immune responses. In osteoporosis, two clusters of molecules in connection with copper death proliferation were outlined. The assessed levels of immune infiltration showed prominent heterogeneity between the different clusters. Cluster 2 was characterized by a raised immune score accompanied with relatively high levels of immune infiltration. The functional analysis we performed showed a close relationship between the different immune responses and specific differentially expressed genes in cluster 2. The random forest machine model showed the optimum discriminatory performance due to relatively low residuals and root mean square errors. Finally, a random forest model based on 5 genes was built, showing acceptable performance in an external validation dataset (AUC = 0.750). Calibration curve, Nomogram, and decision curve analyses also evinced fidelity in predicting subtypes of osteoporosis.

Conclusion

Our study identifies the role of cuproptosis in OP and essentially illustrates the underlying molecular mechanisms that lead to OP heterogeneity.

Pretreatment Circulating Albumin, Platelet, and RDW-SD Associated with Worse Disease-Free Survival in Patients with Breast Cancer

Objective

Breast cancer is the second most common malignancy globally and a leading cause of cancer death in women. Analysis of factors related to disease-free survival (DFS) has improved understanding of the disease and characteristics related to recurrence. The aim of this study was to investigate the predictors of DFS in patients with breast cancer to enable the identification of patients at high risk who may benefit from prevention interventions.

Methods

We retrospectively analyzed 559 women with breast cancer who underwent treatment between 2004 and 2022. The study endpoint was DFS. Recurrence was defined as local recurrence, regional recurrence, distant metastases, contralateral breast cancer, other second primary cancer, and death. Baseline tumor-related characteristics, treatment-related characteristics, sociodemographic and biochemical data were analyzed using Cox proportional hazards analysis.

Results

The median DFS was 45 months (range, 2 to 225 months). Breast cancer recurred in 86 patients (15.4%), of whom 10 had local recurrence, 10 had regional recurrence, 17 had contralateral breast cancer, 29 had distant metastases, 10 had second primary cancer, and 10 patients died. Multivariate forward stepwise Cox regression analysis showed that AJCC stage III, Ki67 ≥14%, albumin, platelet, and red cell distribution width-standard deviation (RDW-SD) were predictors of worse DFS. In addition, the effects of albumin, platelet, and RDW-SD on disease recurrence were confirmed by structural equation model (SEM) analysis.

Conclusion

In addition to the traditional predictors of worse DFS such as AJCC stage III and Ki67 ≥14%, lower pretreatment circulating albumin, higher pretreatment circulating platelet count and RDW-SD could significantly predict worse DFS in this study, and SEM delineated possible causal pathways and inter-relationships of albumin, platelet, and RDW-SD contributing to the disease recurrence among Chinese women with breast cancer.

HOTAIR: A key regulator of the Wnt/β-catenin signaling cascade in cancer progression and treatment

The long non-coding RNA (lncRNA) HOTAIR occupies a central position in the complex domain of cancer biology, particularly concerning its intricate interplay with the Wnt/β-catenin signaling pathway. This comprehensive review explores the multifaceted interactions between HOTAIR and the Wnt/β-catenin cascade, elucidating their profound function in cancer growth, progression, and therapeutic strategies. The study commences by underscoring the pivotal role of the Wnt/β-catenin cascade in governing essential cellular activities, emphasizing its dysregulation as a linchpin in cancer initiation and advancement. It introduces HOTAIR as a crucial regulatory entity, influencing gene expression in both healthy and diseased. The core of this review plunges into the intricacies of HOTAIR's engagement with Wnt/β-catenin signaling. It unravels how HOTAIR, through epigenetic modifications and transcriptional control, exerts its influence over key pathway constituents, including β-catenin, Wnt ligands, and target genes. This influence drives unchecked cancer cell growth, invasion, and metastasis. Furthermore, the review underscores the clinical significance of the HOTAIR-Wnt/β-catenin interplay, elucidating its associations with diverse cancer subtypes, patient prognoses, and prospects as a therapy. It provides insights into ongoing research endeavors to develop HOTAIR-targeted treatments and initiatives to facilitate aberrant Wnt/β-catenin activation. Concluding on a forward-looking note, the article accentuates the broader implications of HOTAIR's involvement in cancer biology, including its contributions to therapy resistance and metastatic dissemination. It underscores the importance of delving deeper into these intricate molecular relationships to pave the way for groundbreaking cancer treatment.

Exploring the lncRNA-VEGF axis: Implications for cancer detection and therapy

Cancer is a complicated illness that spreads indefinitely owing to epigenetic, genetic, and genomic alterations. Cancer cell multidrug susceptibility represents a severe barrier in cancer therapy. As a result, creating effective therapies requires a better knowledge of the mechanisms driving cancer development, progress, and resistance to medications. The human genome is predominantly made up of long non coding RNAs (lncRNAs), which are currently identified as critical moderators in a variety of biological functions. Recent research has found that changes in lncRNAs are closely related to cancer biology. The vascular endothelial growth factor (VEGF) signalling system is necessary for angiogenesis and vascular growth and has been related to an array of health illnesses, such as cancer. LncRNAs have been identified to alter a variety of cancer-related processes, notably the division of cells, movement, angiogenesis, and treatment sensitivity. Furthermore, lncRNAs may modulate immune suppression and are being investigated as possible indicators for early identification of cancer. Various lncRNAs have been associated with cancer development and advancement, serving as cancer-causing or suppressing genes. Several lncRNAs have been demonstrated through research to impact the VEGF cascade, resulting in changes in angiogenesis and tumor severity. For example, the lncRNA nuclear paraspeckle assembly transcript 1 (NEAT1) has been shown to foster the formation of oral squamous cell carcinoma and the epithelial-mesenchymal transition by stimulating the VEGF-A and Notch systems. Plasmacytoma variant translocation 1 (PVT1) promotes angiogenesis in non-small-cell lung cancer by affecting miR-29c and boosting the VEGF cascade. Furthermore, lncRNAs regulate VEGF production and angiogenesis by interacting with multiple downstream signalling networks, including Wnt, p53, and AKT systems. Identifying how lncRNAs engage with the VEGF cascade in cancer gives beneficial insights into tumor biology and possible treatment strategies. Exploring the complicated interaction between lncRNAs and the VEGF pathway certainly paves avenues for novel ways to detect better accurately, prognosis, and cure cancers. Future studies in this area could open avenues toward the creation of innovative cancer therapy regimens that enhance the lives of patients.