In recent years, immunotherapy represented by immune checkpoint inhibitors (ICIs) has led to unprecedented breakthroughs in cancer treatment. However, the fact that many tumors respond poorly or even not to ICIs, partly caused by the absence of tumor-infiltrating lymphocytes (TILs), significantly limits the application of ICIs. Converting these immune “cold” tumors into “hot” tumors that may respond to ICIs is an unsolved question in cancer immunotherapy. Since it is a general characteristic of cancers to resist apoptosis, induction of non-apoptotic regulated cell death (RCD) is emerging as a new cancer treatment strategy. Recently, several studies have revealed the interaction between non-apoptotic RCD and antitumor immunity. Specifically, autophagy, ferroptosis, pyroptosis, and necroptosis exhibit synergistic antitumor immune responses while possibly exerting inhibitory effects on antitumor immune responses. Thus, targeted therapies (inducers or inhibitors) against autophagy, ferroptosis, pyroptosis, and necroptosis in combination with immunotherapy may exert potent antitumor activity, even in tumors resistant to ICIs. This review summarizes the multilevel relationship between antitumor immunity and non-apoptotic RCD, including autophagy, ferroptosis, pyroptosis, and necroptosis, and the potential targeting application of non-apoptotic RCD to improve the efficacy of immunotherapy in malignancy.

The ant colony optimization (ACO) algorithm has the characteristics of positive feedback, essential parallelism, and global convergence, but it has the shortcomings of premature convergence and slow convergence speed. The co-evolutionary algorithm (CEA) emphasizes the existing interaction among different sub-populations, but it is overly formal, and does not form a very strict and unified definition. Therefore, a new adaptive co-evolutionary ant colony optimization (SCEACO) algorithm based on the complementary advantages and hybrid mechanism is proposed in this paper. Firstly, the pheromone update formula is improved and the pheromone range of the ACO algorithm is limited in order to achieve the adaptive update of the pheromone. The elitist strategy and co-evolutionary idea are used for reference, the symbiotic mechanism and hybrid mechanism are introduced to better utilize the advantages of the CEA and ACO. Then the multi-objective optimization problem is divided into several sub-problems, each sub-problem corresponds to one population. Each ant colony is divided into multiple sub-populations in a common search space, and each sub-population performs the search activity and pheromone updating strategy. The elitist strategy is used to retain the elitist individuals within the population and the min-max ant strategy is used to set pheromone concentration for each path. Next, the selection, crossover, and mutation operations of individuals are introduced to adaptively adjust the parameters and implement the information sharing of the population and the co-evolution. Finally, the gate assignment problem of a hub airport is selected to verify the optimization performance of the SCEACO algorithm. The experiment results show that the SCEACO algorithm can effectively solve the gate assignment problem of a hub airport and obtain the effective assignment result. The SCEACO algorithm improves the convergence speed, and enhances the local search ability and global search capability.

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Lung cancer is the most common cancer type and major cause of death from malignancy worldwide. Immune cells such as lymphocytes infiltrated in tumor are identified as strong prognostic biomarkers for lung adenocarcinoma (LURD) patients. In our research, based on immune cell signatures infiltrated in tumor immune microenvironment, we developed and verified a risk score model by selecting six valuable prognostic genes: CD1C, CR2, MS4A1, SFTPC, STAP1 and TFF1 for risk stratification and survival prediction in LURD patients. Furthermore, the associations of risk score with tumor-infiltrating immune cells, immunotherapy-related biomarkers and immune checkpoints were also evaluated. Based on above, we made conclusions that the risk score model as a robust prognosis biomarker can screen the population who can benefit potentiallyfrom immunotherapy, thus improving diagnostic accuracy and optimizing clinical decision in LURD management.

Background Cuproptosis is a novel type of programmed cell death which plays an important role in the development and progression of cancer. However, there is a limited amount of research on cuproptosis-associated long non-coding RNAs (lncRNAs) in head and neck squamous cell carcinomas (HNSCCs). This study aimed to investigate the predictive value of cuproptosis-related lncRNA signature for HNSCC prognosis. Method Transcriptomic and clinical data of HNSCC patients were obtained from the Cancer Genome Atlas (TCGA). We established a cuproptosis-related lncRNA signature and then constructed a hybrid nomogram based on risk scores and clinical factors. We also performed differential expression genes (DEGs) function, immune cells infiltration, immune checkpoint analysis based on cuproptosis-associated lncRNA signature. Results A signature of 27 cuproptosis-related lncRNAs was performed and the prognosis of patients at high risk is worse compared with patients at low risk based on above signature. A nomogram which integrated risk scores and clinical features also showed favorable predictive power. Furthermore, DEGs in high or low risk group were mainly enriched in immune-related pathways. Anti-tumor immune cells and immune checkpoints were mainly enriched in low risk group compared with high risk group. Conclusion Cuproptosis-related lncRNAs could be regarded as independent indicators for HNSCC prognosis which might be effective targets for HNSCC therapy.