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Immunotherapy resistance is a major barrier in the application of immune checkpoint inhibitors (ICI) in lung adenocarcinoma (LUAD) patients. Although recent studies have found several mechanisms and potential genes responsible for immunotherapy resistance, ways to solve this problem are still lacking. Tumor immune dysfunction and exclusion (TIDE) algorithm is a newly developed method to calculate potential regulators and indicators of ICI resistance. In this article, we combined TIDE and weighted gene co-expression network analysis (WGCNA) to screen potential modules and hub genes that are highly associated with immunotherapy resistance using the Cancer Genome Atlas (TCGA) dataset of LUAD patients. We identified 45 gene co-expression modules, and the pink module was most correlated with TIDE score and other immunosuppressive features. After considering the potential factors in immunotherapy resistance, we found that the pink module was also highly related to cancer stemness. Further analysis showed enriched immunosuppressive cells in the extracellular matrix (ECM), immunotherapy resistance indicators, and common cancer-related signaling pathways in the pink module. Seven hub genes in the pink module were shown to be significantly upregulated in tumor tissues compared with normal lung tissue, and were related to poor survival of LUAD patients. Among them, THY1 was the gene most associated with TIDE score, a gene highly related to suppressive immune states, and was shown to be strongly expressed in late-stage patients. Immunohistochemistry (IHC) results demonstrated that THY1 level was higher in the progressive disease (PD) group of LUAD patients receiving a PD-1 monoclonal antibody (mAb) and positively correlated with SOX9. Collectively, we identified that THY1 could be a critical biomarker in predicting ICI efficiency and a potential target for avoiding tumor immunotherapy resistance.

Complex interactions between the immune system and tumor cells exist throughout the initiation and development of cancer. Although the immune system eliminates malignantly transformed cells in the early stage, surviving tumor cells evade host immune defense through various methods and even reprogram the anti-tumor immune response to a pro-tumor phenotype to obtain unlimited growth and metastasis. The high proliferation rate of tumor cells increases the demand for local nutrients and oxygen. Poorly organized vessels can barely satisfy this requirement, which results in an acidic, hypoxic, and glucose-deficient tumor microenvironment. As a result, lipids in the tumor microenvironment are activated and utilized as a primary source of energy and critical regulators in both tumor cells and related immune cells. However, the exact role of lipid metabolism reprogramming in tumor immune response remains unclear. A comprehensive understanding of lipid metabolism dysfunction in the tumor microenvironment and its dual effects on the immune response is critical for mapping the detailed landscape of tumor immunology and developing specific treatments for cancer patients. In this review, we have focused on the dysregulation of lipid metabolism in the tumor microenvironment and have discussed its contradictory roles in the tumor immune response. In addition, we have summarized the current therapeutic strategies targeting lipid metabolism in tumor immunotherapy. This review provides a comprehensive summary of lipid metabolism in the tumor immune response.

In this retrospective clinical study, the authors investigated the impact of cytokine-induced killer (CIK) cell-based immunotherapies on the long-term survival of patients with esophageal squamous cell carcinoma (ESCC).A total of 87 patients with ESCC who received comprehensive treatment were enrolled in the study. Of these patients, 43 were in the control group and 44 were in the CIK treatment group. Flow cytometry analysis was performed to detect the phenotype and anti-tumor function of CIK cells. Clinical characteristics were compared between these two groups, and the survival estimates of ESCC patients were determined using Kaplan-Meier analysis.CIK cells contained a high proportion of the main functional fraction (CD3These results demonstrate that CIK cells combined with conventional treatments potentially prolong long-term survival of patients and may serve as a combined therapeutic approach for the treatment of early-stage ESCC.

Background Esophageal squamous cell carcinoma (ESCC) is the main pathological subtype of esophageal cancer with high incidence and mortality. Immune and stromal cells in the tumor microenvironment (TME) profoundly affect the development of ESCC. Methods In this study, we used the ESTIMATE algorithm to calculate the immune and stromal scores of ESCC samples in The Cancer Genome Atlas (TCGA) database. Next, we used the R package limma to identify differentially expressed genes (DEGs) from high- versus low-immune/stromal score groups and these DEGs were further utilized to analyze the functional annotations, protein-protein interaction (PPI) networks and overall survival of patients with ESCC. Finally, we identified the biological roles of core gene C3AR1 in the TME of ESCC using the TCGA database and in vitro experiments. Results We obtained the immune and stromal scores of ESCC samples and further evaluated the impact of these scores on the prognosis and clinical parameters of patients with ESCC. Next, we identified 410 DEGs from high- versus low-immune/stromal score groups and to gain better understanding of the biological functions and characteristics of DEGs. Among these DEGs, 69 were correlated with the overall survival of patients with ESCC and C3AR1 was identified as a core gene for the regulation of most genes in the network. We found that C3AR1 was positively correlated with M2 macrophages and immune inhibitory molecules (T-cell immunoglobulin and mucin domain 3 (TIM-3), programmed cell death-1 (PD-1)), but not with M1 macrophages. We also observed a higher expression of CD163 and CD206, which were the markers for M2 macrophages in the TLQP-21 TFA (the agonist of C3AR1)groups than in the control groups. Conclusion Based on the ESTIMATE algorithm, we obtained and characterized prognosis-related genes in the TME of ESCC samples from the TCGA database. We have further revealed that C3AR1 may cause an immunosuppressive microenvironment by affecting the polarization of macrophages to M2 phenotype and lead to the progression of ESCC, which indicates that C3AR1 may be a potential target for immunotherapy.

In this paper, a novel fault diagnosis scheme for linear process systems using dissipativity theory is developed. Dissipativity (supply rate) of a process is an input/output property, which may not be valid when a fault occurs. For a given process, dissipativity is not a unique property, with different dissipative supply rates reflecting different aspects of its dynamics. In this approach, the dissipativity of a process is “shaped” such that it is fault-sensitive (i.e., no longer valid when faults occur) and fault-selective (i.e., no longer valid when one particular fault occurs). By adopting the storage functions and supply rates in the quadratic difference form (QdF), the dissipativity conditions are represented as quadratic functions of the input/output trajectories of the process, which captures much more detailed dynamical features compared to conventional dissipativity (e.g., QSR-type supply rates). These dissipativity properties are determined offline by solving an optimization problem with linear matrix inequality constraints. The online diagnosis algorithm involves checking of inequalities on input/output trajectories, which is much simpler compared to the diagnosis approaches based on observers or parameter estimation. The proposed approach is illustrated using a case study of fault diagnosis of a heat exchanger.

Immunotherapy is a currently popular treatment strategy for cancer patients. Although recent developments in cancer immunotherapy have had significant clinical impact, only a subset of patients exhibits clinical response. Therefore, understanding the molecular mechanisms of immunotherapy resistance is necessary. The mechanisms of immune escape appear to consist of two distinct tumor characteristics: a decrease in effective immunocyte infiltration and function and the accumulation of immunosuppressive cells in the tumor microenvironment. Several host-derived factors may also contribute to immune escape. Moreover, inter-patient heterogeneity predominantly results from differences in somatic mutations between cancers, which has led to the hypothesis that differential activation of specific tumor-intrinsic pathways may explain the phenomenon of immune exclusion in a subset of cancers. Increasing evidence has also shown that tumor-intrinsic signaling plays a key role in regulating the immunosuppressive tumor microenvironment and tumor immune escape. Therefore, understanding the mechanisms underlying immune avoidance mediated by tumor-intrinsic signaling may help identify new therapeutic targets for expanding the efficacy of cancer immunotherapies.

Fault detection is becoming increasingly important as the complexity of industrial process develops. In this paper, a data-driven fault detection method is proposed. The dissipativity theory is adopted to find the appropriate dissipativity properties for the process input output trajectory. The dissipativity properties can be viewed as an Abstract energy property, and the dissipativity properties of input output trajectories represent process dynamic features. As faults occur, the dissipative trajectories will change thus allow fault detection to be performed based on these dissipativity properties. A training algorithm is developed to search for the related properties using input output data. A prior knowledge of the process can be incorporated into the algorithm to facilitate the training. The proposed fault detection method is illustrated on a case study of a mono-chlorobenzene plant simulated using VMGSim.

Tumor-associated macrophages (TAMs), key immune cells in the tumor microenvironment, are shown to be closely correlated with the progression of non-small cell lung cancer (NSCLC). Cancer stem cells (CSCs) can contribute to NSCLC progression as well. We aimed to clarify whether TAMs promote the progression of NSCLC by mainly affecting the activities of CSCs. We found that TAM-like cells promoted CSC-like properties in NSCLC cells in vitro, which was mediated by TAM-derived IL-10. TAM-derived IL-10 promoted CSC-like properties of NSCLC cells through JAK1/STAT1/NF-κB/Notch1 signaling. Blockade of IL-10/JAK1 signaling inhibited TAM-mediated NSCLC tumor growth in vivo, and the TAM-mediated expression of CSC-related and mesenchymal-related genes in NSCLC. Lastly, expression levels of these signaling molecules were significantly correlated with survival of NSCLC patients. Therefore, IL-10/JAK1 signaling might be a potential therapeutic target for NSCLC treatment.

In this paper, a robust fault detection scheme based on the behavioral system theory and concept of dissipativity is proposed for linear time-invariant systems. Process model uncertainty is described using a dissipativity condition on the system from process input/output to the residue. Faults are detected in real time when such dissipativity condition (dissipation inequality) is violated. The proposed fault detection approach does not require models of faults. It can also be integrated with a robust control system. The connection and difference between the proposed approach and some existing fault detection methods are also discussed.