Autophagy, a catabolic process, degrades damaged and defective cellular materials through lysosomes, thus working as a recycling mechanism of the cell. It is an evolutionarily conserved and highly regulated process that plays an important role in maintaining cellular homeostasis. Autophagy is constitutively active at the basal level; however, it gets enhanced to meet cellular needs in various stress conditions. The process involves various autophagy‐related genes that ultimately lead to the degradation of targeted cytosolic substrates. Many factors modulate both upstream and downstream autophagy pathways like nutritional status, energy level, growth factors, hypoxic conditions, and localization of p53. Any problem in executing autophagy can lead to various pathological conditions including neurodegeneration, aging, and cancer. In cancer, autophagy plays a contradictory role; it inhibits the formation of tumors, whereas, during advanced stages, autophagy promotes tumor progression. Besides, autophagy protects the tumor from various therapies by providing recycled nutrition and energy to the tumor cells. Autophagy is stimulated by tumor suppressor proteins, whereas it gets inhibited by oncogenes. Due to its dynamic and dual role in the pathogenesis of cancer, autophagy provides promising opportunities in developing novel and effective cancer therapies along with managing chemoresistant cancers. In this article, we summarize different strategies that can modulate autophagy in cancer to overcome the major obstacle, i.e., resistance developed in cancer to anticancer therapies.

With over a million deaths every year around the world, lung cancer is found to be the most recurrent cancer among all types. Nonsmall cell lung carcinoma (NSCLC) amounts to about 85% of the entire cases. The other 15% owes it to small cell lung carcinoma (SCLC). Despite decades of research, the prognosis for NSCLC patients is poorly understood with treatment options limited. First, this article emphasises on the part that tumour microenvironment (TME) and its constituents play in lung cancer progression. This review also highlights the inflammatory (pro‐ or anti‐) roles of different cytokines (ILs, TGF‐β, and TNF‐α) and chemokine (CC, CXC, C, and CX3C) families in the lung TME, provoking tumour growth and subsequent metastasis. The write‐up also pinpoints recent developments in the field of chemokine biology. Additionally, it covers the role of extracellular vesicles (EVs), as alternate carriers of cytokines and chemokines. This allows the cytokines/chemokines to modulate the EVs for their secretion, trafficking, and aid in cancer proliferation. In the end, this review also stresses on the role of these factors as prognostic biomarkers for lung immunotherapy, apart from focusing on inflammatory actions of these chemoattractants.

Head and neck squamous cell carcinoma (HNSC) is one of the most common malignant tumors worldwide with a high rate of morbidity and mortality, with 90% of predilections occurring for oral squamous cell carcinoma (OSCC). Cancers of the mouth account for 40% of head and neck cancers, including squamous cell carcinomas of the tongue, floor of the mouth, buccal mucosa, lips, hard and soft palate, and gingival. OSCC is the most devastating and commonly occurring oral malignancy, with a mortality rate of 500,000 deaths per year. This has imposed a strong necessity to discover driver genes responsible for its progression and malignancy. In the present study we filtered oral squamous cell carcinoma tissue samples from TCGA-HNSC cohort, which we followed by constructing a weighted PPI network based on the survival of patients and the expression profiles of samples collected from them. We found a total of 46 modules, with 18 modules having more than five edges. The KM and ME analyses revealed a single module (with 12 genes) as significant in the training and test datasets. The genes from this significant module were subjected to pathway enrichment analysis for identification of significant pathways and involved genes. Finally, the overlapping genes between gene sets ranked on the basis of weighted PPI module centralities (i.e., degree and eigenvector), significant pathway genes, and DEGs from a microarray OSCC dataset were considered as OSCC-specific hub genes. These hub genes were clinically validated using the IHC images available from the Human Protein Atlas (HPA) database.