High Expression of TLR2 in the serum of patients with tuberculosis and lung cancer, and can promote the progression of lung cancer

The diagnosis interval influences risk factors of mortality in patients with co-existent active tuberculosis and lung cancer: a retrospective study

BACKGROUND

Previous studies reported that tuberculosis (TB) is associated with an increased risk of lung cancer or the survival and mortality of lung cancer. However, the impact of coexisting TB on the survival of lung cancer patients was controversial. We aimed to identify risk factors on the survival rate of patients with co-existent active TB and lung cancer.

METHODS

One hundred seventy-three patients diagnosed with active TB and lung cancer from January 2016 to August 2021 in Shanghai pulmonary hospital were selected and divided into two groups (≤ 6 months, > 6 months) according to the diagnosis interval between active TB and lung cancer (the order of diagnosis is not considered). The clinical characteristics and survival were analyzed. Univariate and multivariate logistic regression analyses were used to identify the risk factors for overall survival (OS).

RESULTS

One hundred seventy-three patients were diagnosed with lung cancer and active TB. The study population exhibited a median age of 64 years, with a majority of 81.5% being male, 58.0% of patients had a history of smoking. Among those involved, 93.6% had pulmonary TB, 91.9% were diagnosed with non-small cell lung cancer (NSCLC), 76.9% were Eastern Cooperative Oncology Group (ECOG) 0-2 and 12.7% were ECOG 3-4. We observed better survival in the > 6 months group compared with the ≤ 6 months group (hazard ratio [HR] 0.456, 95% confidence interval [CI]:0.234-0.889, P = 0.017). The 1-, 3-, and 5- year OS rates were 94.2%, 80.3%, and 77.6%, respectively, in the > 6 months group and 88.3%, 63.8%, and 58.5%, respectively, in the ≤ 6 months group. Surgery (HR 0.193, [95% CI, 0.038-0.097]; P = 0.046) and ECOG Performance Status (HR 12.866, [95% CI, 2.730-60.638]; P = 0.001) were independent prognostic factors in the > 6 months group.

CONCLUSIONS

Patients diagnosed with lung cancer and active TB for more than half a year have a significantly better prognosis than those diagnosed within half a year. ECOG Performance Status and surgery might possibly affect the outcomes of patients with co-existent active TB and lung cancer.

The role of IL-17 in lung cancer growth

Interleukin 17 (IL-17) is an inflammatory cytokine with multiple roles in immune protection, immunopathology, and inflammation-related tumors. Lung cancer is inflammation-related cancer, and a large number of studies have shown that IL-17 contributes to the metastasis and progression of lung cancer. However, some studies have shown that IL17 inhibits the occurrence of lung cancer. At present, there is still some controversy about the role of IL17 in the occurrence and development of lung cancer. This review introduces the basic characteristics of IL-17 and focuses on its role in lung cancer, in order to provide a certain theoretical basis for the prevention, diagnosis, and treatment of lung cancer.

Network pharmacology based research of mechanism of Fuzi Lizhong pills for treatment of intestinal tuberculosis

BACKGROUND

Conventional tuberculosis chemotherapy regimens used in clinical practice have significant side effects when treating intestinal tuberculosis (ITB). Fuzi Lizhong pills are a traditional Chinese medicine commonly used to treat ITB. Studying its exact mechanism of action can help further the research on the treatment of ITB.

AIM

To study the mechanism of Fuzi Lizhong pills for treatment of ITB based on network pharmacology.

METHODS

The active components of five main medicinal materials of Fuzi Lizhong pills were screened from the TCMSP database, and the effective component-related targets were collected from the TCMSP and Drugbank databases. The targets related to ITB were collected from the Genecards database. Through the Venny2.1.0 online website, the overlapping targets of drug active components and disease targets were selected as potential therapeutic targets for the treatment of ITB. Cytoscape3.9.1 software was used to construct a network of "drug-active ingredients-targets-disease". The protein-protein interaction (PPI) network of drug potential therapeutic targets was constructed in the online database String. Then, the topology and visualization were analyzed with Cytoscape3.9.1 software, and the core targets were further selected. The potential therapeutic targets were analyzed by Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses using the "clusterProfiler" package in R 4.1.2.

RESULTS

A total of 108 active drug components of Fuzi Lizhong pills, 254 drug action targets, and 2579 disease targets were screened from the public database. A total of 134 potential therapeutic targets and 10 core targets (AKT1, IL-6, TP53, VEGFA, IL1B, JUN, CASP3, PTGS2, PPARG, and MAPK3) were selected. GO and KEGG enrichment analyses suggested that the biological mechanism of Fuzi Lizhong pills for the treatment of ITB may be related to cellular oxidative stress, immune regulation involving cytokines, and functional pathways including the IL-17 signal pathway, oxidative stress pathway, and so on.

CONCLUSION

The mechanism of Fuzi Lizhong pills for treatment of ITB is related to oxidative stress and immune regulation of cytokines.

Small-Molecule Modulators Targeting Toll-like Receptors for Potential Anticancer Therapeutics

Toll-like receptors (TLRs) are key components of the innate immune system and serve as a crucial link between innate and acquired immunity. In addition to immune function, TLRs are involved in other important pathological processes, including tumorigenesis. TLRs have dual regulatory effects on tumor immunity by activating nuclear factor κ-B signaling pathways, which induce tumor immune evasion or enhance the antitumor immune response. Therefore, TLRs have become a popular target for cancer prevention and treatment, and TLR agonists and antagonists offer considerable potential for drug development. The TLR7 agonist imiquimod (1) has been approved by the U.S. Food and Drug Administration as a treatment for malignant skin cancer. Herein, the structure, signaling pathways, and function of the TLR family are summarized, and the structure-activity relationships associated with TLR selective and multitarget modulators and their potential application in tumor therapy are systematically discussed.

Comprehensive analysis reveals the potential value of inflammatory response genes in the prognosis, immunity, and drug sensitivity of lung adenocarcinoma

Background

Although the relationship between inflammatory response and tumor has been gradually recognized, the potential implications of of inflammatory response genes in lung adenocarcinoma (LUAD) remains poorly investigated.

Methods

RNA sequencing and clinical data were obtained from multiple independent datasets (GSE29013, GSE30219, GSE31210, GSE37745, GSE42127, GSE50081, GSE68465, GSE72094, TCGA and GTEx). Unsupervised clustering analysis was used to identify different tumor subtypes, and LASSO and Cox regression analysis were applied to construct a novel scoring tool. We employed multiple algorithms (ssGSEA, CIBERSORT, MCP counter, and ESTIMATE) to better characterize the LUAD tumor microenvironment (TME) and immune landscapes. GSVA and Metascape analysis were performed to investigate the biological processes and pathway activity. Furthermore, ‘pRRophetic’ R package was used to evaluate the half inhibitory concentration (IC50) of each sample to infer drug sensitivity.

Results

We identified three distinct tumor subtypes, which were related to different clinical outcomes, biological pathways, and immune characteristics. A scoring tool called inflammatory response gene score (IRGS) was established and well validated in multiple independent cohorts, which could well divide patients into two subgroups with significantly different prognosis. High IRGS patients, characterized by increased genomic variants and mutation burden, presented a worse prognosis, and might show a more favorable response to immunotherapy and chemotherapy. Additionally, based on the cross-talk between TNM stage, IRGS and patients clinical outcomes, we redefined the LUAD stage, which was called ‘IRGS-Stage’. The novel staging system could distinguish patients with different prognosis, with better predictive ability than the conventional TNM staging.

Conclusions

Inflammatory response genes present important potential value in the prognosis, immunity and drug sensitivity of LUAD. The proposed IRGS and IRGS-Stage may be promising biomarkers for estimating clinical outcomes in LUAD patients.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01340-7.

Explainable Machine Learning Solution for Observing Optimal Surgery Timings in Thoracic Cancer Diagnosis

In this paper, we introduce an AI-based procedure to estimate and assist in choosing the optimal surgery timing, in the case of a thoracic cancer diagnostic, based on an explainable machine learning model trained on a knowledge base. This decision is usually taken by the surgeon after examining a set of clinical parameters and their evolution in time. Therefore, it is sometimes subjective, it depends heavily on the previous experience of the surgeon, and it might not be confirmed by the histopathological exam. Therefore, we propose a pipeline of automatic processing steps with the purpose of inferring the prospective result of the histopathologic exam, generating an explanation of why this inference holds, and finally, evaluating it against the conclusive opinion of an experienced surgeon. To obtain an accurate practical result, the training dataset is labeled manually by the thoracic surgeon, creating a training knowledge base that is not biased towards clinical practice. The resulting intelligent system benefits from both the precision of a classical expert system and the flexibility of deep neural networks, and it is supposed to avoid, at maximum, any possible human misinterpretations and provide a factual estimate for the proper timing for surgical intervention. Overall, the experiments showed a 7% improvement on the test set compared with the medical opinion alone. To enable the reproducibility of the AI system, complete handling of a case study is presented from both the medical and technical aspects.

The relationship between previous pulmonary tuberculosis and risk of lung cancer in the future

Various investigations have expanded the views that tuberculosis is an important risk factor for lung cancer occurrence. Lung cancer originates from chronic inflammation and infection. It is becoming clearer that Mycobacterium tuberculosis (M.tb) in tuberculosis patients meticulously schemes multiple mechanisms to induce tumor formation and is indispensable to participate in the occurrence of lung cancer. In addition, some additional factors such as age, sex and smoking, accelerate the development of lung cancer after Mycobacterium tuberculosis infection. The clarification of these insights is fostering new diagnoses and therapeutic approaches to prevention of the patients developing from tuberculosis into lung cancer.

Is Mycobacterium tuberculosis carcinogenic to humans?

We highlight the ability of the tuberculosis (TB) causing bacterial pathogen, Mycobacterium tuberculosis (Mtb), to induce key characteristics that are associated with established IARC classified Group 1 and Group 2A carcinogenic agents. There is sufficient evidence from epidemiological case-control, cohort and meta-analysis studies of increased lung cancer (LC) risk in pre-existing/active/old TB cases. Similar to carcinogens and other pathogenic infectious agents, exposure to aerosol-containing Mtb sprays in mice produce malignant transformation of cells that result in squamous cell carcinoma. Convincing, mechanistic data show several characteristics shared between TB and LC which include chronic inflammation, genomic instability and replicative immortality, just to name a few cancer hallmarks. These hallmarks of cancer may serve as precursors to malignant transformation. Together, these findings form the basis of our postulate that Mtb is a complete human pulmonary carcinogen. We also discuss how Mtb may act as both an initiating agent and promoter of tumor growth. Forthcoming experimental studies will not only serve as proof-of-concept but will also pivot our understanding of how to manage/treat TB cases as well as offer solutions to clinical conundrums of TB lesions masquerading as tumors. Clinical validation of our concept may also help pave the way for next generation personalized medicine for the management of pulmonary TB/cancer particularly for cases that are not responding well to conventional chemotherapy or TB drugs.

Convolutional neural network with group theory and random selection particle swarm optimizer for enhancing cancer image classification

As an epitome of deep learning, convolutional neural network (CNN) has shown its advantages in solving many real-world problems. Successful CNN applications on medical prognosis and diagnosis have been achieved in recent years. Their common goal is to recognize the insights from the subtle details from medical images by building a suitable CNN model with maximum accuracy and minimum error. The CNN performance is extremely sensitive to the parameter tuning for any given network structure. To approach this concern, a novel self-tuning CNN model is proposed with a significant characteristic of having a metaheuristic-based optimizer. The most optimal set of parameters is often found via our proposed method, namely group theory and random selection-based particle swarm optimization (GTRS-PSO). The insights of symmetric essentials of model structure and parameter correlation are extracted, followed by the hierarchical partitioning of parameter space, and four operators on those partitions are designed for moving neighborhoods and formulating the swarm topology accordingly. The parameters are updated by a random selection strategy at each interval of partitions during the search process. Preliminary experiments over two radiology image datasets: breast cancer and lung cancer, are conducted for a comprehensive comparison of GTRS-PSO versus other optimization algorithms. The results show that CNN with GTRS-PSO optimizer can achieve the best performance for cancer image classifications, especially when there are symmetric components inside the data properties and model structures.

Toll-like receptor 2 (TLR2) is a candidate prognostic factor in testicular germ cell tumors as well as an indicator of immune function in the tumor microenvironment

Testicular cancer is the most common malignant tumor in young men, and its incidence has increased in recent years. The tumor microenvironment (TME) plays a crucial role in the development and progression of tumors; however, the TME of testicular germ cell tumor (TGCT) is poorly understood. In this study, we downloaded information for 156 TGCT cases from The Cancer Genome Atlas (TCGA) database, used the ESTIMATE method to determine immune and stromal scores, and used CIBERSORT to calculate the proportion of tumor-infiltrating immune cells (TICs). The differentially expressed genes were subjected to a COX regression analysis and used for the construction of a protein–protein interaction (PPI) network. Toll-like receptor 2 (TLR2) was identified as a predictive marker by combining the results of the Cox regression analysis and PPI network. A survival analysis showed that TLR2 was positively correlated with TGCT survival. A gene set enrichment analysis indicated that genes in the high TLR2 expression group were enriched for cell adhesion molecules (CAMs) and the chemokine signaling pathway, and genes in the low TLR2 expression group were mainly enriched in the spliceosome. Regarding proportions of TICs, naive B cells and follicular helper T cells were negatively correlated with the expression of TLR2. This suggests that as TLR2 expression increases, the immunocompetence of the TME decreases. The expression of TLR2 may affect the prognosis of TGCT, suggesting that this locus can be used as a prognostic factor for TGCT.

Exposition to Biological Control Agent Trichoderma stromaticum Increases the Development of Cancer in Mice Injected With Murine Melanoma

Biological control agents (BCA) are an alternative to chemical pesticides and an emerging strategy to safely eliminate plant pathogens. Trichoderma spp. are the most common fungi used as BCAs. They produce spores that are released into the air and can potentially interact with immune system of mammals. We previously showed that Trichoderma affects expression of genes encoding pattern recognition receptors (PRRs) and cytokines in mice. PRRs are involved in the recognition of microorganisms and can lead to pro-tumoral signaling. Here, we evaluated if mice injected with low doses of murine melanoma exhibited increased development of lung tumor when treated with conidia of T. stromaticum. Mice treated with T. stromaticum and inoculated with B16-F10 melanoma cells exhibited significant increase in tumor uptake (p = 0.006) and increased number of visible nodules in the lungs (p = 0.015). We also analyzed mRNA expression levels of genes encoding PRRs in lung of mice exposed to T. stromaticum and demonstrated that mice treated with T. stromaticum conidia exhibited lower expression levels of Clec7a and increased expression of Tlr4 (toll like receptor 4) compared to non-treated controls. The expression levels of Clec7a and Tlr2 were increased in mice treated with T. stromaticum and inoculated with murine melanoma compared to controls only inoculated with melanoma. Our results demonstrate that intranasal exposition to T. stromaticum increases tumor in the B16-F10 model, which may raise concerns regarding the safety of its use in agriculture.